Name: The Complete Self-Driving Car Course - Applied Deep Learning
Price: 29.99 GBP
Availability: InStock

<strong>Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.</strong>
        <p>Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.

This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks (CNN), and (3) Recurrent Neural Networks (RNN). You will learn about logistic regression and linear regression and know the purpose of neural networks. You will also understand forward and backward propagation as well as the cross-entropy function. Furthermore, you will explore image data, convolutional operation, and residual networks. In the final section of the course, you will understand the use of RNN, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM).

You will also have code blocks and notebooks to help you understand the topics covered in the course.

By the end of this course, you will have a hands-on understanding of Neural Networks in detail.

All resources and code files are placed here: https://github.com/PacktPublishing/Fundamentals-in-Neural-Networks</p>
        <h2>What You Will Learn</h2><p>Learn about linear and logistic regression in ANN<br>Learn about cross-entropy between two probability distributions<br>Understand convolution operation which scans inputs with respect to their dimensions<br>Understand VGG16, a convolutional neural network model<br>Understand why to use recurrent neural network<br>Understand Long short-term memory (LSTM)</p>
        <h2>Audience</h2><p>This course can be taken by a beginner level audience that intends to obtain an in-depth overview of Artificial Intelligence, Deep Learning, and three major types of neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. There is no prior coding or programming experience required. This course assumes you have your own laptop, and the code will be done using Colab.</p>
        <h2>Approach</h2><p>This course contains a detailed discussion on various topics of Deep Learning, mathematical description, and code walkthroughs of the three common families of neural networks.</p>
        <h2>Key Features</h2><p>Understand the intuition behind Artificial Neural Networks, Convolution Neural Networks, and Recurrent Neural Networks Understand backward and forward propagation in ANN Understand Bidirectional Recurrent Neural Networks (BRNN)</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Fundamentals-in-Neural-Networks</p>
        <h2>About the Author</h2><h3>Yiqiao Yin</h3><p>Yiqiao Yin was a PhD student in statistics at Columbia University. He has a BA in mathematics and an MS in finance from the University of Rochester. He also has a wide range of research interests in representation learning: feature learning, deep learning, computer vision, and NLP.

Yiqiao Yin is a senior data scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. He has held professional positions as an enterprise-level data scientist at EURO STOXX 50 company Bayer, a quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street.</p>
        <h2>Course Outline</h2><h4>1. Welcome</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome Message</p><p style='margin: 0'>This video explains the need for taking up the course and introduces you to the author.</p></td></tr><tr><td style='padding: 15px;'><p>2. Course Outline</p><p style='margin: 0'>This video explains the course outline and what the course has to offer.</p></td></tr></tbody></table><br><h4>2. Artificial Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Linear Regression</p><p style='margin: 0'>This video explains statistical machine learning, where you will start with the linear regression model.</p></td></tr><tr><td style='padding: 15px;'><p>2. Logistic Regression</p><p style='margin: 0'>This video explains logistic regression and specifically if the target here is discrete or binary.</p></td></tr><tr><td style='padding: 15px;'><p>3. Purpose of Neural Networks</p><p style='margin: 0'>This video explains the purpose of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>4. Forward Propagation</p><p style='margin: 0'>This video explains forward propagation and will dive deeper into the architecture of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>5. Backward Propagation</p><p style='margin: 0'>This video explains backward propagation, which is defined by the optimization problem called the gradient descent algorithm.</p></td></tr><tr><td style='padding: 15px;'><p>6. Activation Function</p><p style='margin: 0'>This video explains the role of the activation function, which is an interesting phenomenon in the design of neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>7. Cross-Entropy Loss Function</p><p style='margin: 0'>This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary.</p></td></tr><tr><td style='padding: 15px;'><p>8. Gradient Descent</p><p style='margin: 0'>This video explains the optimization problem using the gradient descent algorithm.</p></td></tr><tr><td style='padding: 15px;'><p>9. Lab 1 - Introduction to Python</p><p style='margin: 0'>This video demonstrates some of the basic commands in Python specifically the print statement, data structures, variables, and how to define a function.</p></td></tr><tr><td style='padding: 15px;'><p>10. Lab 2 - Introduction to TensorFlow - Remove the Throat-Clearing Sound in the Start of the Video</p><p style='margin: 0'>This video demonstrates some basic operations in TensorFlow such as objects and we will apply some mathematical operations to the Tensor objects.</p></td></tr><tr><td style='padding: 15px;'><p>11. Lab 3 - Introduction to Neural Network</p><p style='margin: 0'>This video demonstrates how to use Keras TensorFlow as API to essentially design and craft the neural network architecture.</p></td></tr><tr><td style='padding: 15px;'><p>12. Lab 4 - Functional API</p><p style='margin: 0'>This video demonstrates functional API versus sequential API.</p></td></tr><tr><td style='padding: 15px;'><p>13. Lab 5 - Building Deeper and Wider Model</p><p style='margin: 0'>This video demonstrates how to build a deeper and wider neural network model.</p></td></tr></tbody></table><br><h4>3. Convolutional Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Image Data</p><p style='margin: 0'>This video explains image data in CNN (Convolutional Neural Network).</p></td></tr><tr><td style='padding: 15px;'><p>2. Tensor and Matrix</p><p style='margin: 0'>This video explains what we mean by Tensor and Matrix.</p></td></tr><tr><td style='padding: 15px;'><p>3. Convolutional Operation</p><p style='margin: 0'>The Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature map or activation map.</p></td></tr><tr><td style='padding: 15px;'><p>4. Padding</p><p style='margin: 0'>This video explains padding in convolutional neural networks.</p></td></tr><tr><td style='padding: 15px;'><p>5. Stride</p><p style='margin: 0'>For a convolutional or pooling operation, the stride denotes the number of pixels by which the window moves after each operation.</p></td></tr><tr><td style='padding: 15px;'><p>6. Convolution in 2D and 3D</p><p style='margin: 0'>This video explains Convolution in 2D and 3D.</p></td></tr><tr><td style='padding: 15px;'><p>7. VGG16</p><p style='margin: 0'>This video explains VGG16 which is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition".</p></td></tr><tr><td style='padding: 15px;'><p>8. Residual Network</p><p style='margin: 0'>Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.</p></td></tr><tr><td style='padding: 15px;'><p>9. Lab 1 - Introduction to Convolutional 1-Dimensional</p><p style='margin: 0'>This video demonstrates convolutional operations in 1-dimension.</p></td></tr><tr><td style='padding: 15px;'><p>10. Lab 2 - Introduction to CNN</p><p style='margin: 0'>This video demonstrates the architecture and how to carry out the code using TensorFlow in collab and building a convolutional neural network.</p></td></tr><tr><td style='padding: 15px;'><p>11. Lab 3 - Deep CNN</p><p style='margin: 0'>This video demonstrates a deeper CNN, where you will build a much bigger number of trainable parameters.</p></td></tr><tr><td style='padding: 15px;'><p>12. Lab 4 - Transfer Learning</p><p style='margin: 0'>This video demonstrates transfer learning.</p></td></tr></tbody></table><br><h4>4. Recurrent Neural Networks</h4><p></p><table width='100%'><tbody><tr><td style='padding: 15px;'><p>1. Welcome to RNN</p><p style='margin: 0'>This video explains recurrent neural networks and why we want to use RNN.</p></td></tr><tr><td style='padding: 15px;'><p>2. Why Use RNN</p><p style='margin: 0'>A Recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data's sequential characteristics and use patterns to predict the next likely scenario.</p></td></tr><tr><td style='padding: 15px;'><p>3. Language Processing</p><p style='margin: 0'>NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information.</p></td></tr><tr><td style='padding: 15px;'><p>4. Forward Propagation in RNN</p><p style='margin: 0'>The forward propagation in an RNN makes a few assumptions: 1) We assume the hyperbolic tangent activation function for the hidden layer. 2) We assume that the output is discrete as if the RNN is used to predict words or characters.</p></td></tr><tr><td style='padding: 15px;'><p>5. Backward Propagation Through Time</p><p style='margin: 0'>Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers.</p></td></tr><tr><td style='padding: 15px;'><p>6. Gated Recurrent Unit (GRU)</p><p style='margin: 0'>Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets.</p></td></tr><tr><td style='padding: 15px;'><p>7. Long Short-Term Memory (LSTM)</p><p style='margin: 0'>Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. LSTM networks are well-suited to classifying, processing, and making predictions based on time series data since there can be lags of unknown duration between important events in a time series.</p></td></tr><tr><td style='padding: 15px;'><p>8. Bi-Directional RNN</p><p style='margin: 0'>Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. BRNNs are especially useful when the context of the input is needed. For example, in handwriting recognition, the performance can be enhanced by knowledge of the letters located before and after the current letter.</p></td></tr><tr><td style='padding: 15px;'><p>9. Lab 1 - RNN in Text Classification</p><p style='margin: 0'>This video demonstrates how to design a recurrent neural network or RNN.</p></td></tr><tr><td style='padding: 15px;'><p>10. Lab 2 - Sequence to Sequence Stock Candlestick Forecast</p><p style='margin: 0'>This video demonstrates sequence-to-sequence stock candlestick forecast.</p></td></tr></tbody></table>

Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.
Learning can be supervised, semi-supervised or unsupervised. Deep-learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks, and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance. This course covers the following three sections: (1) Neural Networks, (2) Convolutional Neural Networks (CNN), and (3) Recurrent Neural Networks (RNN). You will learn about logistic regression and linear regression and know the purpose of neural networks. You will also understand forward and backward propagation as well as the cross-entropy function. Furthermore, you will explore image data, convolutional operation, and residual networks. In the final section of the course, you will understand the use of RNN, Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM). You will also have code blocks and notebooks to help you understand the topics covered in the course. By the end of this course, you will have a hands-on understanding of Neural Networks in detail. All resources and code files are placed here: https://github.com/PacktPublishing/Fundamentals-in-Neural-Networks
What You Will Learn
Learn about linear and logistic regression in ANN
Learn about cross-entropy between two probability distributions
Understand convolution operation which scans inputs with respect to their dimensions
Understand VGG16, a convolutional neural network model
Understand why to use recurrent neural network
Understand Long short-term memory (LSTM)
Audience
This course can be taken by a beginner level audience that intends to obtain an in-depth overview of Artificial Intelligence, Deep Learning, and three major types of neural networks: Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. There is no prior coding or programming experience required. This course assumes you have your own laptop, and the code will be done using Colab.
Approach
This course contains a detailed discussion on various topics of Deep Learning, mathematical description, and code walkthroughs of the three common families of neural networks.
Key Features
Understand the intuition behind Artificial Neural Networks, Convolution Neural Networks, and Recurrent Neural Networks Understand backward and forward propagation in ANN Understand Bidirectional Recurrent Neural Networks (BRNN)
Github Repo
https://github.com/PacktPublishing/Fundamentals-in-Neural-Networks
About the Author
Yiqiao Yin
Yiqiao Yin was a PhD student in statistics at Columbia University. He has a BA in mathematics and an MS in finance from the University of Rochester. He also has a wide range of research interests in representation learning: feature learning, deep learning, computer vision, and NLP. Yiqiao Yin is a senior data scientist at an S&P 500 company LabCorp, developing AI-driven solutions for drug diagnostics and development. He has held professional positions as an enterprise-level data scientist at EURO STOXX 50 company Bayer, a quantitative researcher at AQR working on alternative quantitative strategies to portfolio management and factor-based trading, and equity trader at T3 Trading on Wall Street.
Course Outline
1. Welcome

1. Welcome Message
This video explains the need for taking up the course and introduces you to the author.
2. Course Outline
This video explains the course outline and what the course has to offer.
2. Artificial Neural Networks

1. Linear Regression
This video explains statistical machine learning, where you will start with the linear regression model.
2. Logistic Regression
This video explains logistic regression and specifically if the target here is discrete or binary.
3. Purpose of Neural Networks
This video explains the purpose of neural networks.
4. Forward Propagation
This video explains forward propagation and will dive deeper into the architecture of neural networks.
5. Backward Propagation
This video explains backward propagation, which is defined by the optimization problem called the gradient descent algorithm.
6. Activation Function
This video explains the role of the activation function, which is an interesting phenomenon in the design of neural networks.
7. Cross-Entropy Loss Function
This video explains the cross-entropy function, which is designed under the assumption that the variable you are trying to predict is binary.
8. Gradient Descent
This video explains the optimization problem using the gradient descent algorithm.
9. Lab 1 - Introduction to Python
This video demonstrates some of the basic commands in Python specifically the print statement, data structures, variables, and how to define a function.
10. Lab 2 - Introduction to TensorFlow - Remove the Throat-Clearing Sound in the Start of the Video
This video demonstrates some basic operations in TensorFlow such as objects and we will apply some mathematical operations to the Tensor objects.
11. Lab 3 - Introduction to Neural Network
This video demonstrates how to use Keras TensorFlow as API to essentially design and craft the neural network architecture.
12. Lab 4 - Functional API
This video demonstrates functional API versus sequential API.
13. Lab 5 - Building Deeper and Wider Model
This video demonstrates how to build a deeper and wider neural network model.
3. Convolutional Neural Networks

1. Image Data
This video explains image data in CNN (Convolutional Neural Network).
2. Tensor and Matrix
This video explains what we mean by Tensor and Matrix.
3. Convolutional Operation
The Convolution layer (CONV) uses filters that perform convolution operations as it is scanning the input with respect to its dimensions. Its hyperparameters include the filter size and stride. The resulting output is called a feature map or activation map.
4. Padding
This video explains padding in convolutional neural networks.
5. Stride
For a convolutional or pooling operation, the stride denotes the number of pixels by which the window moves after each operation.
6. Convolution in 2D and 3D
This video explains Convolution in 2D and 3D.
7. VGG16
This video explains VGG16 which is a convolutional neural network model proposed by K. Simonyan and A. Zisserman from the University of Oxford in the paper "Very Deep Convolutional Networks for Large-Scale Image Recognition".
8. Residual Network
Deeper neural networks are more difficult to train. We present a residual learning framework to ease the training of networks that are substantially deeper than those used previously.
9. Lab 1 - Introduction to Convolutional 1-Dimensional
This video demonstrates convolutional operations in 1-dimension.
10. Lab 2 - Introduction to CNN
This video demonstrates the architecture and how to carry out the code using TensorFlow in collab and building a convolutional neural network.
11. Lab 3 - Deep CNN
This video demonstrates a deeper CNN, where you will build a much bigger number of trainable parameters.
12. Lab 4 - Transfer Learning
This video demonstrates transfer learning.
4. Recurrent Neural Networks

1. Welcome to RNN
This video explains recurrent neural networks and why we want to use RNN.
2. Why Use RNN
A Recurrent neural network is a type of artificial neural network commonly used in speech recognition and natural language processing. Recurrent neural networks recognize data's sequential characteristics and use patterns to predict the next likely scenario.
3. Language Processing
NLP is a tool for structuring data in a way that AI systems can process that deals with language. NLP uses AI to 'read' through a document and extract key information.
4. Forward Propagation in RNN
The forward propagation in an RNN makes a few assumptions: 1) We assume the hyperbolic tangent activation function for the hidden layer. 2) We assume that the output is discrete as if the RNN is used to predict words or characters.
5. Backward Propagation Through Time
Backpropagation through time (BPTT) is a gradient-based technique for training certain types of recurrent neural networks. It can be used to train Elman networks. The algorithm was independently derived by numerous researchers.
6. Gated Recurrent Unit (GRU)
Gated recurrent units (GRUs) are a gating mechanism in recurrent neural networks. GRUs have been shown to exhibit better performance on certain smaller and less frequent datasets.
7. Long Short-Term Memory (LSTM)
Long short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning. LSTM networks are well-suited to classifying, processing, and making predictions based on time series data since there can be lags of unknown duration between important events in a time series.
8. Bi-Directional RNN
Bidirectional recurrent neural networks (BRNN) connect two hidden layers of opposite directions to the same output. BRNNs are especially useful when the context of the input is needed. For example, in handwriting recognition, the performance can be enhanced by knowledge of the letters located before and after the current letter.
9. Lab 1 - RNN in Text Classification
This video demonstrates how to design a recurrent neural network or RNN.
10. Lab 2 - Sequence to Sequence Stock Candlestick Forecast
This video demonstrates sequence-to-sequence stock candlestick forecast.

Packt

Python

TensorFlow

Deep Learning

Get started with Neural networks and understand the underlying concepts of Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. This hands-on course will help you understand deep learning in detail with no prior coding or programming experience required.

Fundamentals of Neural Networks

General Admission

<strong>If you are someone wishing to learn to code in Python, then this is just what you need. This course will help you build a solid foundation in coding with Python 3. It includes lots of opportunities for practice that will help put your learnings into action!</strong>
        <p>Python is an excellent language to start programming with; it is powerful and easy to get started with. You can see results and run the code right away and most computers require truly little setup to start coding.

This course is divided into four parts. You will first learn the basics and fundamentals of Python programming then improve with the help of practice exercises, work with the coding challenge, and finally implement all your learning to solve a project challenge.

In the first part, with the help of video lectures and working examples, you will learn the language and its features that will help you build a solid foundation.

In the second part, we will start practicing what we learned by doing simple practice exercises. These exercises will be super simple, and they will help you perfect and play with what you learned in the given section.

The third part is to solve some complex coding challenges. These challenges will present you with a random problem combining everything that you learned in the previous sections.

The fourth and final step is to apply everything you have learned and solve a business problem with the help of a micro-project.

By the end of this course, you will be ready to work on real-world Python projects.

All resources are available at https://github.com/PacktPublishing/Python-Foundation---Quick-Jump-Start-for-Programmers</p>
        <h2>What You Will Learn</h2><p>Learn how to work with Python types and data structures<br>Explore Python sequences and advanced data types<br>Understand Python control structures and loops<br>Learn Python functions and Lambda<br>Learn how to use exception handling, objects, and classes<br>Learn how to use packages and modules</p>
        <h2>Audience</h2><p>This course is designed for beginners and experienced programmers new to Python programming who want to learn or improve their Python programming skills. Basic programming understanding is required.</p>
        <h2>Approach</h2><p>This is a complete hands-on course where you will learn Python programming by first learning the basics and fundamentals, then improving your skills with practice exercises, working with coding challenges, and finally implementing all your learning to solve a real-world micro-project challenge.</p>
        <h2>Key Features</h2><p>Learn Python 3 programming language basics and fundamentals * Learn and improve programming skills with more than 100 coding exercises * Work on a micro-project challenge to solve a more significant problem</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Python-Foundation---Quick-Jump-Start-for-Programmers</p>
        <h2>About the Author</h2><h3>Prashant Kumar Pandey</h3><p>Prashant Kumar Pandey is passionate about helping people learn and grow in their careers by bridging the gap between their existing and required skills. In his journey to fulfill this mission, he is authoring books, publishing technical articles, and creating training videos to help IT professionals and students succeed in the industry. He is also the founder, lead author, and chief editor of the Learning Journal portal that has been providing various skill development courses, training sessions, and technical articles since 2018.</p><br><h3>Satyam Kumar</h3><p>Satyam Kumar is passionate about learning, exploring, and helping other people learn and grow in their careers. In his professional career, Satyam works as a software engineer in a multinational software company. He is also a contributing author and technology associate at the Learning Journal portal that offers various skill development courses.</p>
        <h2>Course Outline</h2><h4>1. Before you Start</h4><p></p><br><h4>2. Getting Started with Python</h4><p></p><br><h4>3. Python Language Basics</h4><p></p><br><h4>4. Working with Python Sequences</h4><p></p><br><h4>5. More Data Structures</h4><p></p><br><h4>6. Python Functions</h4><p></p><br><h4>7. Errors and Exception Handling</h4><p></p><br><h4>8. Python Objects and Classes</h4><p></p><br><h4>9. Python Modules and Packages</h4><p></p><br><h4>10. Final Project</h4><p></p><br><h4>11. Epilogue</h4><p></p>

If you are someone wishing to learn to code in Python, then this is just what you need. This course will help you build a solid foundation in coding with Python 3. It includes lots of opportunities for practice that will help put your learnings into action!
Python is an excellent language to start programming with; it is powerful and easy to get started with. You can see results and run the code right away and most computers require truly little setup to start coding. This course is divided into four parts. You will first learn the basics and fundamentals of Python programming then improve with the help of practice exercises, work with the coding challenge, and finally implement all your learning to solve a project challenge. In the first part, with the help of video lectures and working examples, you will learn the language and its features that will help you build a solid foundation. In the second part, we will start practicing what we learned by doing simple practice exercises. These exercises will be super simple, and they will help you perfect and play with what you learned in the given section. The third part is to solve some complex coding challenges. These challenges will present you with a random problem combining everything that you learned in the previous sections. The fourth and final step is to apply everything you have learned and solve a business problem with the help of a micro-project. By the end of this course, you will be ready to work on real-world Python projects. All resources are available at https://github.com/PacktPublishing/Python-Foundation---Quick-Jump-Start-for-Programmers
What You Will Learn
Learn how to work with Python types and data structures
Explore Python sequences and advanced data types
Understand Python control structures and loops
Learn Python functions and Lambda
Learn how to use exception handling, objects, and classes
Learn how to use packages and modules
Audience
This course is designed for beginners and experienced programmers new to Python programming who want to learn or improve their Python programming skills. Basic programming understanding is required.
Approach
This is a complete hands-on course where you will learn Python programming by first learning the basics and fundamentals, then improving your skills with practice exercises, working with coding challenges, and finally implementing all your learning to solve a real-world micro-project challenge.
Key Features
Learn Python 3 programming language basics and fundamentals * Learn and improve programming skills with more than 100 coding exercises * Work on a micro-project challenge to solve a more significant problem
Github Repo
https://github.com/PacktPublishing/Python-Foundation---Quick-Jump-Start-for-Programmers
About the Author
Prashant Kumar Pandey
Prashant Kumar Pandey is passionate about helping people learn and grow in their careers by bridging the gap between their existing and required skills. In his journey to fulfill this mission, he is authoring books, publishing technical articles, and creating training videos to help IT professionals and students succeed in the industry. He is also the founder, lead author, and chief editor of the Learning Journal portal that has been providing various skill development courses, training sessions, and technical articles since 2018.
Satyam Kumar
Satyam Kumar is passionate about learning, exploring, and helping other people learn and grow in their careers. In his professional career, Satyam works as a software engineer in a multinational software company. He is also a contributing author and technology associate at the Learning Journal portal that offers various skill development courses.
Course Outline
1. Before you Start

2. Getting Started with Python

3. Python Language Basics

4. Working with Python Sequences

5. More Data Structures

6. Python Functions

7. Errors and Exception Handling

8. Python Objects and Classes

9. Python Modules and Packages

10. Final Project

11. Epilogue


Coding

Programming

If you are someone wishing to learn to code in Python, then this is just what you need. This course will help you build a solid foundation in coding with Python 3. It includes lots of opportunities for practice that will help put your learnings into action!

Python Foundation - Quick Jump Start for Programmers

<p>This Data Science bundle training will help you become an expert in a relevant sector. Our world-class course creators will help you with every query throughout your learning journey and assist you in mastering<strong> Data Science with Python</strong>. So, add this course to your basket   harness the coveted wisdom and open the door to your next breakthrough!</p><p>Whether you're an aspirant looking to start your dream career, a professional looking to branch out, or somebody in between   this <strong>Data Science with Python</strong>, <strong>QLS Endorsed</strong> Course will get you sorted. The fundamental understanding in this course will add tremendous value and enable you to establish the meaningful career you've always craved.</p><p>ENROL NOW in our <strong>'Data Science with Python' </strong>today and Start Learning.</p><p><strong>QLS Endorsed Course:</strong></p><ul><li>Course 01: Python Data Science at QLS Level 1</li></ul><p><strong>â </strong><strong>FREE 10 CPD Courses </strong><strong>â</strong></p><ul><li>Course 01: Python Programming: Beginner To Expert</li><li>Course 02: Higher Order Functions in Python</li><li>Course 03: Python for Spatial Analysis in ArcGIS</li><li>Course 04: Easy to Advanced Data Structures</li><li>Course 05: Google Data Studio</li><li>Course 06: Diploma in Data Analytics with Tableau</li><li>Course 07: Business Intelligence and Data Mining</li><li>Course 08: Mastering SQL Programming</li><li>Course 09: Creativity and Problem-Solving Skills</li><li>Course 10: Time Management</li></ul><p><strong>Why Choose Imperial Academy?</strong></p><ul><li>Fully Online Training</li><li>Free QLS Endorsed &amp; CPD Accredited Certificate</li><li>No Time Limits. Study at Your Own Pace</li><li>Expert Instructors</li><li>Audio Visual Training</li><li>Up-to-date Curriculum and Valuable Guidelines</li></ul><p>Launch the dynamic career you've always wanted! Take this high-quality <strong>Data Science with Python </strong>Bundle training to articulate your innovative goals and develop a simple approach to attaining them.</p><p><strong>Course Curriculum:</strong></p><p><strong>Python Data Science at QLS Level 1</strong></p><ul><li>Module 01: Introduction To Python Data Science</li><li>Module 02: Environment Setup</li><li>Module 03: Numpy Package For Calculations</li><li>Module 04: Panda Package For Data Cleaning</li><li>Module 05: Matplotlib Data Visualization Part 1</li><li>Module 06: Matplotlib Data Visualization Part 2</li></ul><p><strong>------ 10 More FREE CPD Courses ------</strong></p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        150 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This <strong>Data Science with Python </strong>course will be a great source of knowledge to improve your existing aptitudes and take them to the next level.</p><p><strong>Disclaimer</strong></p><p>This <strong>Data Science with Python </strong>course has been endorsed by the <strong>Quality Licence Scheme</strong> for its high-quality, non-regulated provision and training programmes. This <strong>Data Science with Python</strong> course is not regulated by Ofqual and is not an accredited qualification. One Education will be able to advise you on any further recognition. For details, please visit the Learner FAQs on the Quality Licence Scheme website.</p><p>Imperial Academy is an approved reselling partner for <strong>Quality Licence Scheme</strong> courses under One Education.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>Anyone can enrol in this <strong>Data Science with Python</strong> bundle course.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <ul><li>Data Scientist: £30,000 - £90,000 per year</li><li>Data Analyst: £20,000 - £50,000 per year</li><li>Business Analyst: £25,000 - £70,000 per year</li><li>Data Engineer: £35,000 - £85,000 per year</li><li>Machine Learning Engineer: £40,000 - £100,000 per year</li><li>Business Intelligence Analyst: £30,000 - £70,000 per year</li><li>Chief Data Officer: £80,000 - £200,000 per year</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>CPD Certificate</h4>
        <p>
            Digital certificate -
            Included
        </p>
            <div><p><strong>CPD Accredited Certificate</strong></p><ul><li>PDF Certificate completely free of charge</li><li>Hard Copy for £9.99 each</li><li>International students must pay a £10 shipping cost</li></ul></div>
        <h4>Hard Copy QLS Certificate</h4>
        <p>
            Hard copy certificate -
            £109
        </p>
            <div><p><strong>Quality Licence Scheme (QLS) Endorsed Certificate</strong></p><ul><li>Level 1 - £109</li><li>International students must pay a £10 shipping cost</li></ul></div>

                        </div>

This Data Science bundle training will help you become an expert in a relevant sector. Our world-class course creators will help you with every query throughout your learning journey and assist you in mastering Data Science with Python. So, add this course to your basket harness the coveted wisdom and open the door to your next breakthrough!
Whether you're an aspirant looking to start your dream career, a professional looking to branch out, or somebody in between this Data Science with Python, QLS Endorsed Course will get you sorted. The fundamental understanding in this course will add tremendous value and enable you to establish the meaningful career you've always craved.
ENROL NOW in our 'Data Science with Python' today and Start Learning.
QLS Endorsed Course:
Course 01: Python Data Science at QLS Level 1
â FREE 10 CPD Courses â
Course 01: Python Programming: Beginner To Expert
Course 02: Higher Order Functions in Python
Course 03: Python for Spatial Analysis in ArcGIS
Course 04: Easy to Advanced Data Structures
Course 05: Google Data Studio
Course 06: Diploma in Data Analytics with Tableau
Course 07: Business Intelligence and Data Mining
Course 08: Mastering SQL Programming
Course 09: Creativity and Problem-Solving Skills
Course 10: Time Management
Why Choose Imperial Academy?
Fully Online Training
Free QLS Endorsed & CPD Accredited Certificate
No Time Limits. Study at Your Own Pace
Expert Instructors
Audio Visual Training
Up-to-date Curriculum and Valuable Guidelines
Launch the dynamic career you've always wanted! Take this high-quality Data Science with Python Bundle training to articulate your innovative goals and develop a simple approach to attaining them.
Course Curriculum:
Python Data Science at QLS Level 1
Module 01: Introduction To Python Data Science
Module 02: Environment Setup
Module 03: Numpy Package For Calculations
Module 04: Panda Package For Data Cleaning
Module 05: Matplotlib Data Visualization Part 1
Module 06: Matplotlib Data Visualization Part 2
------ 10 More FREE CPD Courses ------
 CPD 
150 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
This Data Science with Python course will be a great source of knowledge to improve your existing aptitudes and take them to the next level.
Disclaimer
This Data Science with Python course has been endorsed by the Quality Licence Scheme for its high-quality, non-regulated provision and training programmes. This Data Science with Python course is not regulated by Ofqual and is not an accredited qualification. One Education will be able to advise you on any further recognition. For details, please visit the Learner FAQs on the Quality Licence Scheme website.
Imperial Academy is an approved reselling partner for Quality Licence Scheme courses under One Education.
 Requirements 
Anyone can enrol in this Data Science with Python bundle course.
 Career path 
Data Scientist: £30,000 - £90,000 per year
Data Analyst: £20,000 - £50,000 per year
Business Analyst: £25,000 - £70,000 per year
Data Engineer: £35,000 - £85,000 per year
Machine Learning Engineer: £40,000 - £100,000 per year
Business Intelligence Analyst: £30,000 - £70,000 per year
Chief Data Officer: £80,000 - £200,000 per year
 Certificates 
CPD Certificate
 Digital certificate - Included 
CPD Accredited Certificate
PDF Certificate completely free of charge
Hard Copy for £9.99 each
International students must pay a £10 shipping cost
Hard Copy QLS Certificate
 Hard copy certificate - £109 
Quality Licence Scheme (QLS) Endorsed Certificate
Level 1 - £109
International students must pay a £10 shipping cost

Imperial Academy

QLS Level 1 Course | 10 FREE CPD Courses & PDF Certificates | CPD Accredited | Installment Payment | Tutor Support

Data Science with Python - QLS Endorsed

<strong>There are quite a few issues with manual memory management. Therefore, to avoid memory leaks and optimally use your memory, automatic memory management is essential. In this course, we'll learn about garbage collection as a form of automatic memory management.</strong>
        <p>Memory leaks and dangling pointers are the main issues of manual memory management. If you delete a parent node in a linked list, forgetting to delete all its children first-your memory is leaking. You delete an object chain in the correct order but suddenly, your program crashes since you forgot about the second owner of this resource, which now tries to dereference a null-pointer.

To avoid these issues, most modern high-level programming languages implement automatic memory management. You allocate objects manually; however, don't bother with their deallocation: a special program, garbage collector, knows how to automatically deallocate them correctly and reclaim for future reuse.

In this course, we'll study all the different techniques and algorithms related to automatic memory management, which are used today in practice.</p>
        <h2>What You Will Learn</h2><p>Learn about memory management allocations like Static, Stack, and Heap<br>Learn the algorithms and data structures behind automatic memory management in computer programs<br>Learn about virtual memory and memory layout<br>Learn about semantic and syntactic garbage<br>Learn about the various kinds of garbage collectors<br>Learn about GC barriers</p>
        <h2>Audience</h2><p>This course is mainly for compiler engineers. However, all computer engineers willing to implement a complex project to learn different memory management algorithms will find this course useful. (Generic knowledge is transferable to other systems.)</p>
        <h2>Approach</h2><p>Each lecture is self-sufficient and concise, and describes information related to the topic, not distracting one by unrelated materials or talks. Usage of animated presentations combined with live-editing notes makes understanding the topics easier and shows how (and when in time) the object structures are connected.</p>
        <h2>Key Features</h2><p>Learn about various GC algorithms, such as Mark-Compact collector and Reference counting collector * Identify the difference between tracing and direct collectors * Learn about Tri-color abstraction and marking</p>
        
        <h2>About the Author</h2><h3>Dmitry Soshnikov</h3><p>Dmitry Soshnikov is a software engineer and a lecturer on different computer science topics. He is passionate about education and focuses on high-quality educational content: concise and straight-to-the-point animated lectures with live-editing notes.</p>
        <h2>Course Outline</h2><h4>1. Memory management</h4><p></p><br><h4>2. Garbage Collectors</h4><p></p><br><h4>3. Advance topics</h4><p></p>

There are quite a few issues with manual memory management. Therefore, to avoid memory leaks and optimally use your memory, automatic memory management is essential. In this course, we'll learn about garbage collection as a form of automatic memory management.
Memory leaks and dangling pointers are the main issues of manual memory management. If you delete a parent node in a linked list, forgetting to delete all its children first-your memory is leaking. You delete an object chain in the correct order but suddenly, your program crashes since you forgot about the second owner of this resource, which now tries to dereference a null-pointer. To avoid these issues, most modern high-level programming languages implement automatic memory management. You allocate objects manually; however, don't bother with their deallocation: a special program, garbage collector, knows how to automatically deallocate them correctly and reclaim for future reuse. In this course, we'll study all the different techniques and algorithms related to automatic memory management, which are used today in practice.
What You Will Learn
Learn about memory management allocations like Static, Stack, and Heap
Learn the algorithms and data structures behind automatic memory management in computer programs
Learn about virtual memory and memory layout
Learn about semantic and syntactic garbage
Learn about the various kinds of garbage collectors
Learn about GC barriers
Audience
This course is mainly for compiler engineers. However, all computer engineers willing to implement a complex project to learn different memory management algorithms will find this course useful. (Generic knowledge is transferable to other systems.)
Approach
Each lecture is self-sufficient and concise, and describes information related to the topic, not distracting one by unrelated materials or talks. Usage of animated presentations combined with live-editing notes makes understanding the topics easier and shows how (and when in time) the object structures are connected.
Key Features
Learn about various GC algorithms, such as Mark-Compact collector and Reference counting collector * Identify the difference between tracing and direct collectors * Learn about Tri-color abstraction and marking
About the Author
Dmitry Soshnikov
Dmitry Soshnikov is a software engineer and a lecturer on different computer science topics. He is passionate about education and focuses on high-quality educational content: concise and straight-to-the-point animated lectures with live-editing notes.
Course Outline
1. Memory management

2. Garbage Collectors

3. Advance topics


There are quite a few issues with manual memory management. Therefore, to avoid memory leaks and optimally use your memory, automatic memory management is essential. In this course, we'll learn about garbage collection as a form of automatic memory management.

Garbage Collection Algorithms

<strong>This course will help you understand the fundamentals of data analysis with Python and Pandas library.</strong>
        <p>Pandas is an open-source library providing you with high-performance, easy-to-use data structures and data analysis tools for Python. Pandas provide a powerful and comprehensive toolset for working with data, including tools for reading and writing diverse files, data cleaning and wrangling, analysis and modeling, and visualization. Fields with the widespread use of Pandas include data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering.

This course covers fundamentals of data analysis with Python, predominantly using Pandas library. This course starts with covering the fundamentals of data analysis. You will be then working with Pandas, iPython, Jupyter Notebook. After that, you will explore important Jupyter Notebook commands. Post that, you will be working with CSV, Excel, TXT, JSON files, and API responses.

Finally, you will be working with DataFrames (indexing, slicing, adding, and deleting).

By the end of this course, you will have a good understanding of Pandas and will be ready to explore data analysis in-depth in the future.

All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/pandas-resources</p>
        <h2>What You Will Learn</h2><p>Learn fundamentals of data analysis<br>Understand important Jupyter Notebook commands<br>Work with DataFrames (indexing, slicing, adding and deleting)<br>Work with Pandas, iPython, Jupyter Notebook<br>Work with CSV, Excel, TXT, JSON Files, and API Responses<br>Add, delete, and update rows and columns</p>
        <h2>Audience</h2><p>This course is ideal for Python programmers and developers, and students who are interested in learning Pandas for doing data analysis.<br><br>This course is designed for beginners who want to start their journey in the field of data analytics. No prerequisites are required to opt for this course.</p>
        <h2>Approach</h2><p>This is a short, crisp, and clear course on using Python and especially Pandas library to do the data analysis. This course is a good mix of theoretical and practical concepts.</p>
        <h2>Key Features</h2><p>Short, crisp, and clear course on data analysis using Pandas library in Python * Beginners-level course to give you hands-on knowledge to start your journey in data analysis * A good blend of theoretical and practical concepts along with resource files to follow along smoothly </p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/pandas-resources</p>
        <h2>About the Author</h2><h3>Shubham Sarda</h3><p>Shubham Sarda is a software developer and digital marketer with a passion for teaching. He has worked with many funded start-ups, self-projects, and as a top-rated freelancer on multiple marketplaces. Currently, he stands among the top 700 freelancers with over 2,500+ projects on Fiverr, PeoplePerHour, Freelancer, and more.

As an instructor, he has taught programming and digital marketing to over 20,000 students, both with online courses and offline bootcamps. He has mastered explaining complex topics in the simplest form that is easy to understand and follow. His video courses are also used by companies to train their employees and by colleges to prepare and upgrade their students according to the latest industry requirements.</p>
        <h2>Course Outline</h2><h4>1. Course Introduction</h4><p></p><br><h4>2. What is Pandas?</h4><p></p><br><h4>3. Jupyter Notebooks</h4><p></p><br><h4>4. Working on Data</h4><p></p><br><h4>5. Thank You For Being Here!</h4><p></p>

This course will help you understand the fundamentals of data analysis with Python and Pandas library.
Pandas is an open-source library providing you with high-performance, easy-to-use data structures and data analysis tools for Python. Pandas provide a powerful and comprehensive toolset for working with data, including tools for reading and writing diverse files, data cleaning and wrangling, analysis and modeling, and visualization. Fields with the widespread use of Pandas include data science, finance, neuroscience, economics, advertising, web analytics, statistics, social science, and many areas of engineering. This course covers fundamentals of data analysis with Python, predominantly using Pandas library. This course starts with covering the fundamentals of data analysis. You will be then working with Pandas, iPython, Jupyter Notebook. After that, you will explore important Jupyter Notebook commands. Post that, you will be working with CSV, Excel, TXT, JSON files, and API responses. Finally, you will be working with DataFrames (indexing, slicing, adding, and deleting). By the end of this course, you will have a good understanding of Pandas and will be ready to explore data analysis in-depth in the future. All the resource files are uploaded on the GitHub repository at https://github.com/PacktPublishing/pandas-resources
What You Will Learn
Learn fundamentals of data analysis
Understand important Jupyter Notebook commands
Work with DataFrames (indexing, slicing, adding and deleting)
Work with Pandas, iPython, Jupyter Notebook
Work with CSV, Excel, TXT, JSON Files, and API Responses
Add, delete, and update rows and columns
Audience
This course is ideal for Python programmers and developers, and students who are interested in learning Pandas for doing data analysis.

This course is designed for beginners who want to start their journey in the field of data analytics. No prerequisites are required to opt for this course.
Approach
This is a short, crisp, and clear course on using Python and especially Pandas library to do the data analysis. This course is a good mix of theoretical and practical concepts.
Key Features
Short, crisp, and clear course on data analysis using Pandas library in Python * Beginners-level course to give you hands-on knowledge to start your journey in data analysis * A good blend of theoretical and practical concepts along with resource files to follow along smoothly 
Github Repo
https://github.com/PacktPublishing/pandas-resources
About the Author
Shubham Sarda
Shubham Sarda is a software developer and digital marketer with a passion for teaching. He has worked with many funded start-ups, self-projects, and as a top-rated freelancer on multiple marketplaces. Currently, he stands among the top 700 freelancers with over 2,500+ projects on Fiverr, PeoplePerHour, Freelancer, and more. As an instructor, he has taught programming and digital marketing to over 20,000 students, both with online courses and offline bootcamps. He has mastered explaining complex topics in the simplest form that is easy to understand and follow. His video courses are also used by companies to train their employees and by colleges to prepare and upgrade their students according to the latest industry requirements.
Course Outline
1. Course Introduction

2. What is Pandas?

3. Jupyter Notebooks

4. Working on Data

5. Thank You For Being Here!


Data Analysis

Pandas

This course will help you understand the fundamentals of data analysis with Python and Pandas library.

Data Analysis Crash Course for Beginners (Pandas + Python)

JavaScript

This course extensively illustrates how to become a skilled JavaScript developer. Working from the fundamentals, you will learn what JavaScript is, what it can do, and why to use it. A range of topics is covered clearly and structured while building practical projects along the way, including real-world examples and mini-challenges.

Modern JavaScript For Beginners

Statistics

Linear Algebra

Data Science

Probability

Start your data science journey with this carefully constructed comprehensive course and get hands-on experience with Python for data science. Gain in-depth knowledge about core Python and essential mathematical concepts in linear algebra, probability, and statistics. Complete data science training with 13+ hours of content.

Data Science 101: Methodology, Python, and Essential Math

Advanced Tutorial to Learn essential skills needed to transform your career from QA Engineer to SDET/Test Architect
If you have a passion to take up your Automation career to the next level? If yes then join me on this journey. The one and only Best "Full Stack QA tutorial" which touches upon technical challenges in every phase of Automation by providing smart solutions using latest technologies like Dockers, Jackson API, Jenkin Pipelines, Data Structures using Java Streams, Window Batch Scripting, Database readers, GIt and many more! After successful course completion, you should be able to apply for any Test Architect /SDET positions or lead the Challenging Automation projects from Scratch. Below are in detail Scenarios we are going to cover in this Tutorial
- Dockerization, integrating Selenium Grid with Docker,
- Building JSON/XML from database results,
- Parsing JSON into Java objects with Jackson API,
- Jenkins pipeline Scripting for CI/CD,
- Dynamically monitoring Server Logs with Java,
- Windows Batch job scripting,
- Data provider to Excel Integration,
- Java streams, Lambda expressions,
- GIT version control system and many more,
All the code and supporting files for this course are available at - https://github.com/PacktPublishing/SDET-Test-Architect-Essentials--Road-to-Full-stack-QA
What You Will Learn
Understand and implement Docker to provide virtualization environments for Automation Tests
Build JSON/XML on the fly from JDBC Query results with Jackson API and POJO implementation
Build and execute Window Batch Scripts for invoking Servers(Selenium/Protractor)
Understand Jenkin pipelines scripting for CI/CD
Complete knowledge on latest Java Streams and Lambda Expressions for interview prep
Parsing JSON files into Java objects to feed into Web Automation tests
How to monitor server logs dynamically with Java
Integrating TestNG Data provider into Excel for building robust Data-driven Automation
Understanding GIT commands in depth and version control
Audience
This course is for Automation Testers, QA Engineers and Software Engineers.
Approach
Each video includes step-by-step written instructions which will show you how to create each project the way the author does. The instructor takes you through the entire workflow while making sure to stop and explain and show you why you take certain actions to make things easier later.
Key Features
This course provides a lot of real-world experience by giving good examples with Selenium Grid, Automation Testing concepts, Test Infrastructure, Jenkins and other interesting things.
Github Repo
https://github.com/packtpublishing/sdet-test-architect-essentials--road-to-full-stack-qa
About the Author
Rahul Shetty
Rahul Shetty is the founder and trainer at Rahul Shetty Academy. He is a passionate software tester and has 13 years of rich experience in the IT industry. He has completed more than 300 offline training batches. Rahul has extensive knowledge in web automation testing, mobile automation testing, and API automation testing. He has also worked with various CMM-level organizations and managed the set-up of QA processes for projects.
Course Outline
1. Road Map to become SDET- Course Intro
In this section, the author walkthrough the course overview.
1. (Free Preview): What is this course about? Course FAQ's
Road Map to become SDET- Course Intro: What is this course about? Course FAQ's
2. (Free Preview): Course Content Walkthrough
Road Map to become SDET- Course Intro: Course Content Walkthrough
2. Converting JDBC Results into Java object with POJO classes
In this section, we set up the Database and tables, and create JDBC connection.
1. (Free Preview): Problem Statement- Deciding Approach to solve it
Converting JDBC Results into Java object with POJO classes: Problem Statement- Deciding Approach to solve it
2. Setting up Database and Tables as Prerequiste for driving data
Converting JDBC Results into Java object with POJO classes: Setting up Database and Tables as Prerequiste for driving data
3. JDBC Connection to read the values of DB through Java
Converting JDBC Results into Java object with POJO classes: JDBC Connection to read the values of DB through Java
4. Creating POJO classes for converting DB results into Java objects
Converting JDBC Results into Java object with POJO classes: Creating POJO classes for converting DB results into Java objects
3. Jackson API to convert Java objects into Json file formats
In this section, we convert Java Objects to JSON file, retrieve multiple results in Array List and merge JSON files.
1. Converting Java objects to Json file using Jackson API
Jackson API to convert Java objects into Json file formats: Converting Java objects to Json file using Jackson API
2. Retrieve Multiple results into Array list of Java objects for further Parsing
Jackson API to convert Java objects into Json file formats: Retrieve Multiple results into Array list of Java objects for further Parsing
3. How to merge multiple Json files into one Giant nested Json
Jackson API to convert Java objects into Json file formats: How to merge multiple Json files into one Giant nested Json
4. Introduction to GSON, JSON, Array for building Json dynamically
Jackson API to convert Java objects into Json file formats: Introduction to GSON, JSON, Array for building Json dynamically
5. Apache common String Utilities to Parse strings with escape characters
Jackson API to convert Java objects into Json file formats: Apache common String Utilities to Parse strings with escape characters
6. Parse Json file into Java object to send the data through getter Methods
Jackson API to convert Java objects into Json file formats: Parse Json file into Java object to send the data through getter Methods
4. Introduction to Docker and its Advantages
In this section, we learn about Docker and its uses in Automation Industry.
1. What is Docker? How it helps in Automation Industry?
Introduction to Docker and its Advantages: What is Docker? How it helps in Automation Industry?
2. What are containers and Images in Docker Software?
Introduction to Docker and its Advantages: What are containers and Images in Docker Software?
3. How exactly Virtual machines are different from Docker
Introduction to Docker and its Advantages: How exactly Virtual machines are different from Docker
4. How to Install Docker into Machines with Prerequisites needed
Introduction to Docker and its Advantages: How to Install Docker into Machines with Prerequisites needed
5. Understand working with DockerHub
Introduction to Docker and its Advantages: Understand working with DockerHub
5. Implementation of Docker to Automation tests
In this section, we look into implementation of remote WebDriver, invoking Docker and integrate it to the project.
1. Remote WebDriver capabilities details for Selenium Docker Integration
Implementation of Docker to Automation tests: Remote WebDriver capabilities details for Selenium Docker Integration
2. Invoking Docker with Chrome and Firefox Images for Selenium Tests
Implementation of Docker to Automation tests: Invoking Docker with Chrome and Firefox Images for Selenium Tests
3. Idea of Integrating Docker with Selenium Grid
Implementation of Docker to Automation tests: Idea of Integrating Docker with Selenium Grid
4. Docker compose file in creating Nodes and Hub
Implementation of Docker to Automation tests: Docker compose file in creating Nodes and Hub
5. Making the Docker Project TestNG Compatible
Implementation of Docker to Automation tests: Making the Docker Project TestNG Compatible
6. Parallel Distribution of tests with Multiple Dockers
Implementation of Docker to Automation tests: Parallel Distribution of tests with Multiple Dockers
6. Creating Window Batch files and Monitoring Server Logs
In this section, we create Windows Batch files, read server logs, build a BAT file and automate all tasks.
1. Create Window Batch files for invoking server
Creating Window Batch files and Monitoring Server Logs: Create Window Batch files for invoking server
2. Reading the Server log files through Java using Buffered Reader
Creating Window Batch files and Monitoring Server Logs: Reading the Server log files through Java using Buffered Reader
3. Monitor Server logs continuously from Your program for Synchronizing tests
Creating Window Batch files and Monitoring Server Logs: Monitor Server logs continuously from Your program for Synchronizing tests
4. Build bat file for Scale Chrome instances and deleting file Logs
Creating Window Batch files and Monitoring Server Logs: Build bat file for Scale Chrome instances and deleting file Logs
5. Automate all manual tasks of cleaning data for every test run
Creating Window Batch files and Monitoring Server Logs: Automate all manual tasks of cleaning data for every test run
7. Docker Integration to Jenkins
Here, we look into Docker integration to Jenkins.
1. Integrate Docker tests with Maven and Jenkins
Docker Integration to Jenkins: Integrate Docker tests with Maven and Jenkins
8. Introduction to Jenkin Pipelines
In this section, we look into Jenkin's pipeline, and groovy scripted script.
1. What are Jenkin Pipelines? How important it is in Agile?
Introduction to Jenkin Pipelines: What are Jenkin Pipelines? How important it is in Agile?
2. Jenkin Pipeline view plugin to chain the jobs -Demo
Introduction to Jenkin Pipelines: Jenkin Pipeline view plugin to chain the jobs -Demo
3. Groovy Scripted Syntax to build pipeline on fly
Introduction to Jenkin Pipelines: Groovy Scripted Syntax to build pipeline on fly
9. Build Pipelines Dynamically with Groovy Script for CI/CD
In this section, we create pipeline job, look into importance of JenkinsFile.
1. Create Pipeline job and Construct it using Declarative Syntax-Demo
Build Pipelines Dynamically with Groovy Script for CI/CD: Create Pipeline job and Construct it using Declarative Syntax-Demo
2. Importance of JenkinsFile and its Keywords for Pipeline building
Build Pipelines Dynamically with Groovy Script for CI/CD: Importance of JenkinsFile and its Keywords for Pipeline building
3. Demo- Push the JenkinFIle into GitHub and Start running Pipeline
Build Pipelines Dynamically with Groovy Script for CI/CD: Demo- Push the JenkinFIle into GitHub and Start running Pipeline
10. Integrating DataProvider with Excel for Robust Data Driven Implementation
Here, we look into importance of Data Provider, understand how it functions and look into live demo.
1. Importance of Data Provider and Excel Integration for better Data driven
Integrating DataProvider with Excel for Robust Data Driven Implementation: Importance of Data Provider and Excel Integration for better Data driven
2. Understand Dataprovider and how it sends data in Multidimensional array
Integrating DataProvider with Excel for Robust Data Driven Implementation: Understand Dataprovider and how it sends data in Multidimensional array
3. Get Excel dependencies and connect from java code to excel
Integrating DataProvider with Excel for Robust Data Driven Implementation: Get Excel dependencies and connect from java code to excel
4. Live demo on integrating Excel to Dataprovider to parameterize data
Integrating DataProvider with Excel for Robust Data Driven Implementation: Live demo on integrating Excel to Dataprovider to parameterize data
11. Java Streams- Map, Filter, Match, Collect- (Build Optimized Code)
In this section, we learn more about Java Streams, Lambda expressions, Map and few more.
1. What are Java Streams and Lambda Expressions?
Java Streams- Map, Filter, Match, Collect- (Build Optimized Code): What are Java Streams and Lambda Expressions?
2. Smart Usage of Stream filters to filter out the results
Java Streams- Map, Filter, Match, Collect- (Build Optimized Code): Smart Usage of Stream filters to filter out the results
3. How to use forEach on Streams to print the collections list
Java Streams- Map, Filter, Match, Collect- (Build Optimized Code): How to use forEach on Streams to print the collections list
4. Usage of Map and its importance in Streams API with example
Java Streams- Map, Filter, Match, Collect- (Build Optimized Code): Usage of Map and its importance in Streams API with example
5. Demonstration on Match, Sorted, Merging Streams functionality
Java Streams- Map, Filter, Match, Collect- (Build Optimized Code): Demonstration on Match, Sorted, Merging Streams functionality
6. Collecting Stream parsed data back into Collections array
Java Streams- Map, Filter, Match, Collect- (Build Optimized Code): Collecting Stream parsed data back into Collections array
12. GIT Commands and it uses -Version Control System
In this section, we are introduced to GIT, create config and repositories.
1. Introduction to GIT
GIT Commands and it uses -Version Control System: Introduction to GIT
2. Importance of GitHub and its uses
GIT Commands and it uses -Version Control System: Importance of GitHub and its uses
3. Creating Git config and repositories
GIT Commands and it uses -Version Control System: Creating Git config and repositories
4. How to push code to remote repository
GIT Commands and it uses -Version Control System: How to push code to remote repository
5. Understanding Staging and commit in git
GIT Commands and it uses -Version Control System: Understanding Staging and commit in git
6. Add remote repository and push the committed code
GIT Commands and it uses -Version Control System: Add remote repository and push the committed code
7. End to end working example on Git commands -1
GIT Commands and it uses -Version Control System: End to end working example on Git commands -1
8. End to end working example on Git commands -2
GIT Commands and it uses -Version Control System: End to end working example on Git commands -2
9. Importance of Branching in GIT
GIT Commands and it uses -Version Control System: Importance of Branching in GIT
10. How to resolve Merge conflicts with GIT
GIT Commands and it uses -Version Control System: How to resolve Merge conflicts with GIT
13. Grid & Jenkin Basics
In this bonus section, we are introduced to Grid, register Hub and nodes. Look into importance of Jenkins, install and configure Jenkins.
1. Introduction to Grid
Grid & Jenkin Basics: Introduction to Grid
2. Registering Hub with nodes for execution.
Grid & Jenkin Basics: Registering Hub with nodes for execution.
3. Registering nodes with Browser drivers
Grid & Jenkin Basics: Registering nodes with Browser drivers
4. Desired capabilities and RemoteWebdriver importance
Grid & Jenkin Basics: Desired capabilities and RemoteWebdriver importance
5. Importance of Jenkins in Test frameworks
Grid & Jenkin Basics: Importance of Jenkins in Test frameworks
6. Install and configure Jenkins
Grid & Jenkin Basics: Install and configure Jenkins
7. Configuring Jenkin Settings and Workspace
Grid & Jenkin Basics: Configuring Jenkin Settings and Workspace
8. Cross browser testing rules-Grid Program
Grid & Jenkin Basics: Cross browser testing rules-Grid Program

Java

DevOps

Docker

Selenium

Jenkins

Advanced Tutorial to Learn essential skills needed to transform your career from QA Engineer to SDET/Test Architect

SDET/Test Architect Essentials -Road to Full stack QA

<strong>Learn Python Programming using a Step By Step Approach with 200+ code examples.</strong>
        <p>Python is one of the most popular programming languages. Python offers both object-oriented and structural programming features. We love Programming. Our aim with this course is to create a love for Programming. 
In more than 150 Steps, we explore the most important Python Programming Language Features
??Basics of Python Programming - Expressions, Variables, and Printing Output
??Python Operators - Python Assignment Operator, Relational and Logical Operators, Short Circuit Operators
??Python Conditionals and If Statement
??Methods - Parameters, Arguments and Return Values
??An Overview Of Python Platform
??Object-Oriented Programming - Class, Object, State and Behavior
??Basics of OOPS - Encapsulation, Inheritance and Abstract Class.
??Basics about Python Data Types
??Basics about Python Built-in Modules
??Conditionals with Python - If Else Statement, Nested If Else
??Loops - For Loop, While Loop in Python, Break and Continue
??Immutablity of Python Basic Types
??Python Data Structures - List, Set, Dictionary and Tuples
??Introduction to Variable Arguments
??Basics of Designing a Class - Class, Object, State and Behavior. Deciding State and Constructors.
??Introduction to Exception Handling - Your Thought Process during Exception Handling. try, except, else and finally. Exception Hierarchy. Throwing an Exception. Creating and Throwing a Custom Exception.
All the code and supporting files for this course are available at: https://github.com/PacktPublishing/Python-Programming-for-Beginners---Learn-in-100-Easy-Steps
</p>
        <h2>What You Will Learn</h2><p>You will learn Python the modern way - Step By Step - with 200 hands-on Code Examples<br>You will understand the best practices in writing high-quality Pythonic Code<br>You will solve a Wide Range of Hands-on Programming exercises with Python<br>You will learn to Write awesome Object-Oriented Programs with Python<br>You will acquire all the Python Skills needed to transition into Analytics, Machine Learning and Data Science Roles<br>You will Acquire all the SKILLS to demonstrate an EXPERTISE with Python Programming in Your Job Interviews<br>You will learn about a wide variety of Python Data Structures - List, Set, Dictionary and Tuples<br>You will learn the basics of PyCharm IDE and Python Shell<br>You will learn how to think as a Python Programmer<br>You will learn the basics of programming - variables, choosing a data type, conditional execution, loops, writing great methods, breaking down problems into subproblems and implementing exception handling.<br>You will learn the basics of Object Oriented Programming - Inheritance, Abstract Class and Constructors<br>You will learn the important concepts of Object Oriented Programming - Abstraction and Inheritance</p>
        <h2>Audience</h2><p>This course is for anyone who wants to learn Programming with Python. You want to automate things with Python.</p>
        <h2>Approach</h2><p>We take a hands-on approach using a combination of Python Shell and PyCharm as an IDE to illustrate more than 150 Python Coding Exercises, Puzzles and Code Examples.</p>
        <h2>Key Features</h2><p>The course is amazing, the instructor has good knowledge in the field  * This course can be viewed with zero Python Programming Experience</p>
        <h2>Github Repo</h2><p>https://github.com/packtpublishing/python-programming-for-beginners---learn-in-100-easy-steps</p>
        <h2>About the Author</h2><h3>In28Minutes Official</h3><p>	Ranga Karanam is a seasoned technologist and architect with over two decades of expertise in programming, design, and architecture. He founded in28Minutes with the goal of helping students master cutting-edge cloud-native technologies such as AWS, Azure, Google Cloud, Docker, and Kubernetes. With 15 years of experience in Java programming and design, Ranga has collaborated with top banking clients worldwide.

His passion for creating hands-on courses with real-world projects motivated him to develop the Step-By-Step series of courses that follow a problem-solution-based approach, with practical and real-world application examples.

Ranga and his team at in28Minutes specialize in Java and related frameworks such as Spring, Spring Boot, Spring MVC, Struts, and Hibernate. They are committed to equipping learners with the skills and knowledge necessary to succeed in today's rapidly evolving technology landscape.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Introduction to Python Programming With Multiplication Table</h4><p></p><br><h4>3. Introduction to Methods - Multiplication Table</h4><p></p><br><h4>4. Introduction to Python Platform</h4><p></p><br><h4>5. Introduction to PyCharm</h4><p></p><br><h4>6. Basic Numeric Data Types and Conditional Execution</h4><p></p><br><h4>7. Text in Python</h4><p></p><br><h4>8. Python Loops</h4><p></p><br><h4>9. Python Tips for Beginners</h4><p></p><br><h4>10. Introduction to Object Oriented Programming</h4><p></p><br><h4>11. Python Data Structures</h4><p></p><br><h4>12. Object Oriented Programming Again</h4><p></p><br><h4>13. Error Handling with Python</h4><p></p><br><h4>14. A Few More Python Tips</h4><p></p><br><h4>15. Thank You and Congratulations</h4><p></p>

Learn Python Programming using a Step By Step Approach with 200+ code examples.
Python is one of the most popular programming languages. Python offers both object-oriented and structural programming features. We love Programming. Our aim with this course is to create a love for Programming. In more than 150 Steps, we explore the most important Python Programming Language Features
??Basics of Python Programming - Expressions, Variables, and Printing Output
??Python Operators - Python Assignment Operator, Relational and Logical Operators, Short Circuit Operators
??Python Conditionals and If Statement
??Methods - Parameters, Arguments and Return Values
??An Overview Of Python Platform
??Object-Oriented Programming - Class, Object, State and Behavior
??Basics of OOPS - Encapsulation, Inheritance and Abstract Class.
??Basics about Python Data Types
??Basics about Python Built-in Modules
??Conditionals with Python - If Else Statement, Nested If Else
??Loops - For Loop, While Loop in Python, Break and Continue
??Immutablity of Python Basic Types
??Python Data Structures - List, Set, Dictionary and Tuples
??Introduction to Variable Arguments
??Basics of Designing a Class - Class, Object, State and Behavior. Deciding State and Constructors.
??Introduction to Exception Handling - Your Thought Process during Exception Handling. try, except, else and finally. Exception Hierarchy. Throwing an Exception. Creating and Throwing a Custom Exception.
All the code and supporting files for this course are available at: https://github.com/PacktPublishing/Python-Programming-for-Beginners---Learn-in-100-Easy-Steps

What You Will Learn
You will learn Python the modern way - Step By Step - with 200 hands-on Code Examples
You will understand the best practices in writing high-quality Pythonic Code
You will solve a Wide Range of Hands-on Programming exercises with Python
You will learn to Write awesome Object-Oriented Programs with Python
You will acquire all the Python Skills needed to transition into Analytics, Machine Learning and Data Science Roles
You will Acquire all the SKILLS to demonstrate an EXPERTISE with Python Programming in Your Job Interviews
You will learn about a wide variety of Python Data Structures - List, Set, Dictionary and Tuples
You will learn the basics of PyCharm IDE and Python Shell
You will learn how to think as a Python Programmer
You will learn the basics of programming - variables, choosing a data type, conditional execution, loops, writing great methods, breaking down problems into subproblems and implementing exception handling.
You will learn the basics of Object Oriented Programming - Inheritance, Abstract Class and Constructors
You will learn the important concepts of Object Oriented Programming - Abstraction and Inheritance
Audience
This course is for anyone who wants to learn Programming with Python. You want to automate things with Python.
Approach
We take a hands-on approach using a combination of Python Shell and PyCharm as an IDE to illustrate more than 150 Python Coding Exercises, Puzzles and Code Examples.
Key Features
The course is amazing, the instructor has good knowledge in the field * This course can be viewed with zero Python Programming Experience
Github Repo
https://github.com/packtpublishing/python-programming-for-beginners---learn-in-100-easy-steps
About the Author
In28Minutes Official
	Ranga Karanam is a seasoned technologist and architect with over two decades of expertise in programming, design, and architecture. He founded in28Minutes with the goal of helping students master cutting-edge cloud-native technologies such as AWS, Azure, Google Cloud, Docker, and Kubernetes. With 15 years of experience in Java programming and design, Ranga has collaborated with top banking clients worldwide. His passion for creating hands-on courses with real-world projects motivated him to develop the Step-By-Step series of courses that follow a problem-solution-based approach, with practical and real-world application examples. Ranga and his team at in28Minutes specialize in Java and related frameworks such as Spring, Spring Boot, Spring MVC, Struts, and Hibernate. They are committed to equipping learners with the skills and knowledge necessary to succeed in today's rapidly evolving technology landscape.
Course Outline
1. Introduction

2. Introduction to Python Programming With Multiplication Table

3. Introduction to Methods - Multiplication Table

4. Introduction to Python Platform

5. Introduction to PyCharm

6. Basic Numeric Data Types and Conditional Execution

7. Text in Python

8. Python Loops

9. Python Tips for Beginners

10. Introduction to Object Oriented Programming

11. Python Data Structures

12. Object Oriented Programming Again

13. Error Handling with Python

14. A Few More Python Tips

15. Thank You and Congratulations


Learn Python Programming using a Step By Step Approach with 200+ code examples.

Python Programming for Beginners - Learn in 100 Easy Steps

Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python
Self-driving cars have emerged to be one of the most transformative technologies. Fueled by deep learning algorithms, they are rapidly developing and creating new opportunities in the mobility sector. Deep learning jobs command some of the highest salaries in the development world. This is the first and one of the only courses that make practical use of deep learning and applies it to building a self-driving car. You'll learn and master deep learning in this fun and exciting course with top instructor Rayan Slim. Having trained thousands of students, Rayan is a highly rated and experienced instructor who follows a learning-by-doing approach. By the end of the course, you will have built a fully functional self-driving car powered entirely by deep learning. This powerful simulation will impress even the most senior developers and ensure you have hands-on skills in neural networks that you can bring to any project or company. This course will show you how to do the following:
- Use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car
- Train a perceptron-based neural network to classify between binary classes
- Train convolutional neural networks to identify various traffic signs
- Train deep neural networks to fit complex datasets
- Master Keras, a power neural network library written in Python
- Build and train a fully functional self-driving car All the code and supporting files for this course are available at https://github.com/PacktPublishing/The-Complete-Self-Driving-Car-Course---Applied-Deep-Learning
What You Will Learn
Apply Computer Vision and deep learning techniques to build automotive-related algorithms
Understand, build, and train convolutional neural networks with Keras.
Simulate a fully functional self-driving car with convolutional neural networks and Computer Vision
Train a deep learning model that can identify up to 43 different traffic signs
Use essential Computer Vision techniques to identify lane lines on a road
Build and train powerful neural networks with Keras
Understand neural networks at the most fundamental, perceptron-based level
Audience
This course is for anyone with an interest in deep learning and Computer Vision. Anyone (no matter the skill level) who wants to transition into the field of artificial intelligence, including entrepreneurs with an interest in working on some of the most cutting-edge technologies, will find this course useful.
Approach
This is the first, and the only, course that makes practical use of deep learning, and applies it to building a self-driving car, one of the most disruptive technologies in the world today. This course is designed to help students with no experience in programming or mathematics become accomplished, deep learning developers.
Key Features
The transition from a beginner to deep learning expert * Learn through demonstrations as your instructor completes each task with you * No experience required
Github Repo
https://github.com/packtpublishing/the-complete-self-driving-car-course---applied-deep-learning
Course Outline
1. Introduction
In this section, the author suggest as to why we should take the course.
1. Why This Course?
Introduction: Why This Course?
2. Installation
In this section, we look into installation on Mac and Windows.
1. Overview
Installation: Overview
2. Anaconda Distribution - Mac
Installation: Anaconda Distribution - Mac
3. Anaconda Distribution - Windows
Installation: Anaconda Distribution - Windows
4. Text Editor
Installation: Text Editor
5. Outro
Installation: Outro
3. Python Crash Course
In this section, the author covers Python and provides a crash course on this.
1. Python Crash Course Part 1 - Data Types
Python Crash Course: Python Crash Course Part 1 - Data Types
2. Jupyter Notebooks
Python Crash Course: Jupyter Notebooks
3. Arithmetic Operations
Python Crash Course: Arithmetic Operations
4. Variables
Python Crash Course: Variables
5. Numeric Data Types
Python Crash Course: Numeric Data Types
6. String Data Types
Python Crash Course: String Data Types
7. Booleans
Python Crash Course: Booleans
8. Methods
Python Crash Course: Methods
9. Lists
Python Crash Course: Lists
10. Slicing
Python Crash Course: Slicing
11. Membership Operators
Python Crash Course: Membership Operators
12. Mutability
Python Crash Course: Mutability
13. Mutability II
Python Crash Course: Mutability II
14. Common Functions & Methods
Python Crash Course: Common Functions & Methods
15. Tuples
Python Crash Course: Tuples
16. Sets
Python Crash Course: Sets
17. Dictionaries
Python Crash Course: Dictionaries
18. Compound Data Structures
Python Crash Course: Compound Data Structures
19. Part 1 - Outro
Python Crash Course: Part 1 - Outro
20. Part 2 - Control Flow
Python Crash Course: Part 2 - Control Flow
21. If, else
Python Crash Course: If, else
22. elif
Python Crash Course: elif
23. Complex Comparisons
Python Crash Course: Complex Comparisons
24. For Loops
Python Crash Course: For Loops
25. For Loops II
Python Crash Course: For Loops II
26. While Loops
Python Crash Course: While Loops
27. Break
Python Crash Course: Break
28. Part 2 - Outro
Python Crash Course: Part 2 - Outro
29. Part 3 - Functions
Python Crash Course: Part 3 - Functions
30. Functions
Python Crash Course: Functions
31. Scope
Python Crash Course: Scope
32. Doc Strings
Python Crash Course: Doc Strings
33. Lambda & Higher Order Functions
Python Crash Course: Lambda & Higher Order Functions
34. Part 3 - Outro
Python Crash Course: Part 3 - Outro
4. NumPy Crash Course
In this section, the author provides a crash course on NumPy.
1. Overview
NumPy Crash Course: Overview
2. Vector Addition - Arrays vs Lists
NumPy Crash Course: Vector Addition - Arrays vs Lists
3. Multidimensional Arrays
NumPy Crash Course: Multidimensional Arrays
4. One Dimensional Slicing
NumPy Crash Course: One Dimensional Slicing
5. Reshaping
NumPy Crash Course: Reshaping
6. Multidimensional Slicing
NumPy Crash Course: Multidimensional Slicing
7. Manipulating Array Shapes
NumPy Crash Course: Manipulating Array Shapes
8. Matrix Multiplication
NumPy Crash Course: Matrix Multiplication
9. Stacking
NumPy Crash Course: Stacking
10. Part 4 - Outro
NumPy Crash Course: Part 4 - Outro
5. Computer Vision: Finding Lane Lines
In this section, learn to use Computer Vision techniques via OpenCV to identify lane lines for a self-driving car.
1. Overview
Computer Vision: Finding Lane Lines: Overview
2. Loading Image
Computer Vision: Finding Lane Lines: Loading Image
3. Grayscale Conversion
Computer Vision: Finding Lane Lines: Grayscale Conversion
4. Smoothening Image
Computer Vision: Finding Lane Lines: Smoothening Image
5. Simple Edge Detection
Computer Vision: Finding Lane Lines: Simple Edge Detection
6. Region of Interest
Computer Vision: Finding Lane Lines: Region of Interest
7. Binary Numbers & Bitwise_and
Computer Vision: Finding Lane Lines: Binary Numbers & Bitwise_and
8. Line Detection - Hough Transform
Computer Vision: Finding Lane Lines: Line Detection - Hough Transform
9. Hough Transform II
Computer Vision: Finding Lane Lines: Hough Transform II
10. Optimizing
Computer Vision: Finding Lane Lines: Optimizing
11. Finding Lanes on Video
Computer Vision: Finding Lane Lines: Finding Lanes on Video
12. Part 5 - Conclusion
Computer Vision: Finding Lane Lines: Part 5 - Conclusion
6. The Perceptron
In this section, learn to train a Perceptron-based Neural Network to classify between binary classes.
1. Overview
The Perceptron: Overview
2. Machine Learning
The Perceptron: Machine Learning
3. Supervised Learning - Friendly Example
The Perceptron: Supervised Learning - Friendly Example
4. Classification
The Perceptron: Classification
5. Linear Model
The Perceptron: Linear Model
6. Perceptrons
The Perceptron: Perceptrons
7. Weights
The Perceptron: Weights
8. Project - Initial Stages
The Perceptron: Project - Initial Stages
9. Error Function
The Perceptron: Error Function
10. Sigmoid
The Perceptron: Sigmoid
11. Sigmoid Implementation (Code)
The Perceptron: Sigmoid Implementation (Code)
12. Cross Entropy
The Perceptron: Cross Entropy
13. Cross Entropy (Code)
The Perceptron: Cross Entropy (Code)
14. Gradient Descent
The Perceptron: Gradient Descent
15. Gradient Descent (Code)
The Perceptron: Gradient Descent (Code)
16. Recap
The Perceptron: Recap
17. Part 6 - Conclusion
The Perceptron: Part 6 - Conclusion
7. Keras
In this section, master Keras, a power Neural Network library written in Python.
1. Overview
Keras: Overview
2. Intro to Keras
Keras: Intro to Keras
3. Keras Models
Keras: Keras Models
4. Keras - Predictions
Keras: Keras - Predictions
5. Part 7 - Outro
Keras: Part 7 - Outro
8. Deep Neural Networks
In this section, train in Deep Neural Networks to fit complex datasets.
1. Overview
Deep Neural Networks: Overview
2. Non-Linear Boundaries
Deep Neural Networks: Non-Linear Boundaries
3. Architecture
Deep Neural Networks: Architecture
4. Feedforward Process
Deep Neural Networks: Feedforward Process
5. Error Function
Deep Neural Networks: Error Function
6. Backpropagation
Deep Neural Networks: Backpropagation
7. Code Implementation
Deep Neural Networks: Code Implementation
8. Conclusion
Deep Neural Networks: Conclusion
9. Multiclass Classification
In this section, we look into classification through Softmax, cross entropy.
1. Overview
Multiclass Classification: Overview
2. Softmax
Multiclass Classification: Softmax
3. Cross Entropy
Multiclass Classification: Cross Entropy
4. Implementation
Multiclass Classification: Implementation
5. Outro
Multiclass Classification: Outro
10. MNIST Image Recognition
In this section, we look into image recognition through Hyperparmters.
1. Overview
MNIST Image Recognition: Overview
2. MNIST Dataset
MNIST Image Recognition: MNIST Dataset
3. Train & Test
MNIST Image Recognition: Train & Test
4. Hyperparameters
MNIST Image Recognition: Hyperparameters
5. Implementation Part 1
MNIST Image Recognition: Implementation Part 1
6. Implementation Part 2
MNIST Image Recognition: Implementation Part 2
7. Implementation Part 3
MNIST Image Recognition: Implementation Part 3
8. Section 10 - Outro
MNIST Image Recognition: Section 10 - Outro
11. Convolutional Neural Networks
In this section, simulate a fully functional Self-Driving Car with Convolutional Neural Networks and Computer Vision.
1. Overview
Convolutional Neural Networks: Overview
2. Convolutions & MNIST
Convolutional Neural Networks: Convolutions & MNIST
3. Convolutional Layer
Convolutional Neural Networks: Convolutional Layer
4. Convolutions II
Convolutional Neural Networks: Convolutions II
5. Pooling
Convolutional Neural Networks: Pooling
6. Fully Connected Layer
Convolutional Neural Networks: Fully Connected Layer
7. Code Implementation I
Convolutional Neural Networks: Code Implementation I
8. Code Implementation II
Convolutional Neural Networks: Code Implementation II
9. Section 11 - Conclusion
Convolutional Neural Networks: Section 11 - Conclusion
12. Classifying Road Symbols
In this section, train a Deep Learning Model that can identify between 43 different Traffic Signs.
1. Overview
Classifying Road Symbols: Overview
2. Preprocessing Images
Classifying Road Symbols: Preprocessing Images
3. leNet Implementation
Classifying Road Symbols: leNet Implementation
4. Fine-tuning Model
Classifying Road Symbols: Fine-tuning Model
5. Testing
Classifying Road Symbols: Testing
6. Fit Generator
Classifying Road Symbols: Fit Generator
7. Section 12 - Outro
Classifying Road Symbols: Section 12 - Outro
13. Polynomial Regression
In this section, explore more on Polynomial regression.
1. Overview
Polynomial Regression: Overview
2. Implementation
Polynomial Regression: Implementation
3. Section 13 - Conclusion
Polynomial Regression: Section 13 - Conclusion
14. Behavioural Cloning
In this section, we collect data, download and balance data, preprocess images and define Nvidia Model.
1. Overview
Behavioural Cloning: Overview
2. Collecting Data
Behavioural Cloning: Collecting Data
3. Downloading Data
Behavioural Cloning: Downloading Data
4. Balancing Data
Behavioural Cloning: Balancing Data
5. Training & Validation Split
Behavioural Cloning: Training & Validation Split
6. Preprocessing Images
Behavioural Cloning: Preprocessing Images
7. Defining Nvidia Model
Behavioural Cloning: Defining Nvidia Model
8. Flask & Socket.io
Behavioural Cloning: Flask & Socket.io
9. Self Driving Car - Test 1
Behavioural Cloning: Self Driving Car - Test 1
10. Generator - Augmentation Techniques
Behavioural Cloning: Generator - Augmentation Techniques
11. Batch Generator
Behavioural Cloning: Batch Generator
12. Fit Generator
Behavioural Cloning: Fit Generator
13. Outro
Behavioural Cloning: Outro

Use deep learning, Computer Vision, and machine learning techniques to build an autonomous car with Python

The Complete Self-Driving Car Course - Applied Deep Learning

Register on the Data Structure today and build the experience, skills and knowledge you need to enhance your professional development and work towards your dream job. Study this course through online learning and take the first steps towards a long-term career.
The course consists of a number of easy to digest, in-depth modules, designed to provide you with a detailed, expert level of knowledge. Learn through a mixture of instructional video lessons and online study materials.
Receive online tutor support as you study the course, to ensure you are supported every step of the way. Get a digital certificate as a proof of your course completion.
The Data Structure course is incredibly great value and allows you to study at your own pace. Access the course modules from any internet-enabled device, including computers, tablet, and smartphones.
The course is designed to increase your employability and equip you with everything you need to be a success. Enrol on the now and start learning instantly!
What You Get With The Data Structure Course
Receive a e-certificate upon successful completion of the course
Get taught by experienced, professional instructors
Study at a time and pace that suits your learning style
24/7 help and advice via email or live chat
Get full tutor support on weekdays (Monday to Friday)
Course Design
The course is delivered through our online learning platform, accessible through any internet-connected device. There are no formal deadlines or teaching schedules, meaning you are free to study the course at your own pace.
You are taught through a combination of
Video lessons
Online study materials
Certification
After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for £9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for £15.99, which will reach your doorsteps by post.
Who Is This Course For:
The course is ideal for those who already work in this sector or are an aspiring professional. This course is designed to enhance your expertise and boost your CV. Learn key skills and gain a professional qualification to prove your newly-acquired knowledge.
Requirements:
The online training is open to all students and has no formal entry requirements. To study the Data Structure course, all your need is a passion for learning, a good understanding of English, numeracy, and IT skills. You must also be over the age of 16.
Course Content
Unit 01: Introduction			
	Module 01: Promo Video		00:02:00
	Module 02: Data Structure Introduction		00:05:00
	Module 03: Computational Complexity Analysis		00:13:00
Unit 02: Arrays			
	Module 01: Static and Dynamic Arrays		00:12:00
	Module 02: Dynamic Arrays Source Code		00:07:00
Unit 03: Liked List			
	Module 01: Singly and Doubly Linked Lists		00:15:00
	Module 02: Doubly Linked Lists Source Code		00:10:00
Unit 04: Stack			
	Module 01: Stack		00:12:00
	Module 02: Stack Implementation		00:04:00
	Module 03: Stack Source Code		00:04:00
Unit 05: Queues			
	Module 01: Queues (Part-1)		00:06:00
	Module 02: Queues (Part-2)		00:06:00
	Module 03: Queue Source Code		00:04:00
Unit 06: Priority Queues (PQs)			
	Module 01: Priority Queues (PQs) with an interlude on heaps		00:13:00
	Module 02: Turning Min PQ into Max PQ		00:06:00
	Module 03: Adding Elements to Binary Heap		00:10:00
	Module 04: Removing Elements from Binary Heap		00:14:00
	Module 05: Priority Queue Binary Heap Source Code		00:16:00
Unit 07: Union Find			
	Module 01: Disjoint Set		00:06:00
	Module 02: Kruskal's Algorithm		00:06:00
	Module 03: Union and Find Operations		00:11:00
	Module 04: Path Compression Union Find		00:07:00
	Module 05: Union Find Source Code		00:08:00
Unit 08: Binary Search Trees			
	Module 01: Binary Trees and Binary Search Trees (BST)		00:13:00
	Module 02: Inserting Element into a Binary Search Tree (BST)		00:06:00
	Module 03: Removing Element from a Binary Search Tree (BST)		00:14:00
	Module 04: Tree Traversals		00:12:00
	Module 05: Binary Search Source Code		00:13:00
Unit 09: Fenwick Tree			
	Module 01: Fenwick Tree Construction		00:06:00
	Module 02: Point Updates		00:06:00
	Module 03: Binary Indexed Tree		00:14:00
	Module 04: Fenwick Tree Source Code		00:06:00
Unit 10: Hash Tables			
	Module 01: Hash Table		00:17:00
	Module 02: Separate Chaining		00:08:00
	Module 03: Separate Chaining Source Code		00:12:00
	Module 04: Open Addressing		00:11:00
	Module 05: Linear Probing		00:14:00
	Module 06: Quadratic Probing		00:09:00
	Module 07: Double Hashing		00:15:00
	Module 08: Removing Element Open Addressing		00:08:00
	Module 09: Open Addressing Code		00:15:00
Unit 11: Suffix Array			
	Module 01: Introduction		00:03:00
	Module 02: The Longest Common Prefix (LCP) Array		00:03:00
	Module 03: Using SA/LCP Array to Find Unique Substrings		00:05:00
	Module 04: Longest Common Substring (LCS)		00:11:00
	Module 05: Longest Common Substring (LCS) Full Example		00:07:00
	Module 06: Longest Repeated Substring (LRS)		00:05:00
Unit 12: AVL Trees			
	Module 01: Balanced Binary Search Trees (BBSTs)		00:09:00
	Module 02: Inserting Elements into an AVL Tree		00:10:00
	Module 03: Removing an AVL Tree		00:09:00
	Module 04: AVL Tree Source Code		00:17:00
Unit 13: Indexed Priority Queue			
	Module 01: Indexed Priority Queue (Part-1)		00:25:00
	Module 02: Indexed Priority Queue Source Code		00:09:00
Unit 14: Sparse Tables			
	Module 01: Sparse Table		00:26:00
	Module 02: Sparse Table Source Code		00:07:00
Frequently Asked Questions
Are there any prerequisites for taking the course?
 There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. 
Can I access the course at any time, or is there a set schedule?
 You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. 
How long will I have access to the course?
 For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. 
Is there a certificate of completion provided after completing the course?
 Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. 
Can I switch courses or get a refund if I'm not satisfied with the course?
 We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. 
How do I track my progress in the course?
 Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. 
What if I have technical issues or difficulties with the course?
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Design & Technology Courses (DT)

Data Structure

Data Structures & Algorithms (DSA)

Trainers Course

<p>Delve into the intricate world of '<strong>Data Structure</strong>' with our comprehensive course, meticulously crafted for those who have a penchant for understanding the skeleton of software engineering. Data structures form the backbone of algorithmic efficiency, and mastering them is akin to holding the master key to software optimisation. Our course is a confluence of foundational knowledge and complex data structuring, ensuring that you emerge not only informed but also invigorated, ready to tackle any computational challenge thrown your way.</p>
<p><strong>Learning Outcomes</strong></p>
<ul>
<li>Gain foundational understanding of different data structures and their implementations.</li>
<li>Discover the intricate details of arrays, linked lists, stacks, and queues.</li>
<li>Develop the ability to effectively utilise advanced structures like AVL trees and Fenwick trees.</li>
<li>Master techniques for optimising algorithmic efficiency using suitable data structures.</li>
<li>Enhance problem-solving skills related to data storage and retrieval.</li>
</ul>
<h3><b>Why choose this Data Structure<span> course</span>?</b></h3>
<ul>
<li>Unlimited access to the course for a lifetime.</li>
<li>Opportunity to earn a certificate accredited by the <b>CPD Quality Standards</b><b> </b>after completing this course.</li>
<li>Structured lesson planning in line with industry standards.</li>
<li>Immerse yourself in innovative and captivating course materials and activities.</li>
<li>Assessments are designed to evaluate advanced cognitive abilities and skill proficiency.</li>
<li>Flexibility to complete the <b>Diploma in Data Structure at QLS Level 5</b> Course at your own pace, on your own schedule.</li>
<li>Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.</li>
</ul>
<h3><b>Who is this Data Structure<span> </span></b><b><span>course </span></b><b>for?</b></h3>
<ul>
<li>Individuals keen on deepening their computer science foundations.</li>
<li>Software developers aiming to optimise their code.</li>
<li>Students pursuing computer science and related disciplines.</li>
<li>Competitive coders desiring an edge in algorithm competitions.</li>
<li>Tech enthusiasts eager to understand the underpinnings of efficient programming.</li>
</ul>
<h3><b>Career path</b></h3>
<ul>
<li><strong>Software Developer</strong>: £25,000 - £45,000</li>
<li><strong>Algorithm Engineer</strong>: £40,000 - £60,000</li>
<li><strong>Data Scientist</strong>: £35,000 - £55,000</li>
<li><strong>Backend Developer</strong>: £28,000 - £50,000</li>
<li><strong>Systems Architect</strong>: £45,000 - £70,000</li>
<li><strong>Data Engineer</strong>: £30,000 - £55,000</li>
</ul>
<p><b>Prerequisites</b></p>
<p><span>This </span><b>Diploma in Data Structure at QLS Level 5</b><span> does not require you to have any prior qualifications or experience. You can just enrol and start learning.This </span><b>Diploma in Data Structure at QLS Level 5</b><span> was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.</span></p>
<h3><b>Certification</b></h3>
<p><span>After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.</span></p>
<p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme</strong></p>
<p>Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £115 to be delivered to your home by post. For international students, there is an additional postage charge of £10.</p>
<p><strong>Endorsement</strong></p>
<p>The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards.<br />
Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses.</p><div>
				<h3>
	<span>Course Curriculum</span>
</h3>

<div>
<table width="100%">				<tbody><tr>
					<td colspan="4">Unit 01: Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Promo Video</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Data Structure Introduction</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Computational Complexity Analysis</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 02: Arrays</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Static and Dynamic Arrays</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Dynamic Arrays Source Code</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 03: Linked List</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Singly and Doubly Linked Lists</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Doubly Linked Lists Source Code</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 04: Stack</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Stack</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Stack Implementation</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Stack Source Code</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 05: Queues</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Queues (Part-1)</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Queues (Part-2)</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Queue Source Code</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 06: Priority Queues (PQs)</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Priority Queues (PQs) with an interlude on heaps</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Turning Min PQ into Max PQ</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Adding Elements to Binary Heap</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Removing Elements from Binary Heap</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Priority Queue Binary Heap Source Code</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 07: Union Find</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Disjoint Set</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Kruskal's Algorithm</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Union and Find Operations</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Path Compression Union Find</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Union Find Source Code</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 08: Binary Search Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Binary Trees and Binary Search Trees (BST)</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Inserting Element into a Binary Search Tree (BST)</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Removing Element from a Binary Search Tree (BST)</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Tree Traversals</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05:  Binary Search Source Code</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 09: Fenwick Tree</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Fenwick Tree Construction</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Point Updates</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Binary Indexed Tree</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Fenwick Tree Source Code</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 10: Hash Tables</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Hash Table</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Separate Chaining</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Separate Chaining Source Code</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Open Addressing</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Linear Probing</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 06: Quadratic Probing</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 07: Double Hashing</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 08: Removing Element Open Addressing</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 09: Open Addressing Code</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 11: Suffix Array</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Introduction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: The Longest Common Prefix (LCP) Array</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03:  Using SA/LCP Array to Find Unique Substrings</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Longest Common Substring (LCS)</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Longest Common Substring (LCS) Full Example</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 06: Longest Repeated Substring (LRS)</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 12: AVL Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Balanced Binary Search Trees (BBSTs)</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Inserting Elements into an AVL Tree</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Removing an AVL Tree</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: AVL Tree Source Code</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 13: Indexed Priority Queue</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Indexed Priority Queue (Part-1)</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Indexed Priority Queue Source Code</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 14: Sparse Tables</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Sparse Table</td>
					<td> </td>
					<td><span><i></i> 00:26:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Sparse Table Source Code</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Assignment</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Assignment - Diploma in Data Structure at QLS Level 5</td>
					<td> </td>
					<td><span><i></i> 04:00:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Order your QLS Endorsed Certificate</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Order your QLS Endorsed Certificate</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table></div>

			</div>

Delve into the intricate world of 'Data Structure' with our comprehensive course, meticulously crafted for those who have a penchant for understanding the skeleton of software engineering. Data structures form the backbone of algorithmic efficiency, and mastering them is akin to holding the master key to software optimisation. Our course is a confluence of foundational knowledge and complex data structuring, ensuring that you emerge not only informed but also invigorated, ready to tackle any computational challenge thrown your way.
Learning Outcomes
Gain foundational understanding of different data structures and their implementations.
Discover the intricate details of arrays, linked lists, stacks, and queues.
Develop the ability to effectively utilise advanced structures like AVL trees and Fenwick trees.
Master techniques for optimising algorithmic efficiency using suitable data structures.
Enhance problem-solving skills related to data storage and retrieval.
Why choose this Data Structure course?
Unlimited access to the course for a lifetime.
Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course.
Structured lesson planning in line with industry standards.
Immerse yourself in innovative and captivating course materials and activities.
Assessments are designed to evaluate advanced cognitive abilities and skill proficiency.
Flexibility to complete the Diploma in Data Structure at QLS Level 5 Course at your own pace, on your own schedule.
Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience.
Who is this Data Structure course for?
Individuals keen on deepening their computer science foundations.
Software developers aiming to optimise their code.
Students pursuing computer science and related disciplines.
Competitive coders desiring an edge in algorithm competitions.
Tech enthusiasts eager to understand the underpinnings of efficient programming.
Career path
Software Developer: £25,000 - £45,000
Algorithm Engineer: £40,000 - £60,000
Data Scientist: £35,000 - £55,000
Backend Developer: £28,000 - £50,000
Systems Architect: £45,000 - £70,000
Data Engineer: £30,000 - £55,000
Prerequisites
This Diploma in Data Structure at QLS Level 5 does not require you to have any prior qualifications or experience. You can just enrol and start learning.This Diploma in Data Structure at QLS Level 5 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection.
Certification
After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8.
Endorsed Certificate of Achievement from the Quality Licence Scheme
Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £115 to be delivered to your home by post. For international students, there is an additional postage charge of £10.
Endorsement
The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards.

Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses.
 Course Curriculum

Unit 01: Introduction			
	Module 01: Promo Video		00:02:00
	Module 02: Data Structure Introduction		00:05:00
	Module 03: Computational Complexity Analysis		00:13:00
Unit 02: Arrays			
	Module 01: Static and Dynamic Arrays		00:12:00
	Module 02: Dynamic Arrays Source Code		00:07:00
Unit 03: Linked List			
	Module 01: Singly and Doubly Linked Lists		00:15:00
	Module 02: Doubly Linked Lists Source Code		00:10:00
Unit 04: Stack			
	Module 01: Stack		00:12:00
	Module 02: Stack Implementation		00:04:00
	Module 03: Stack Source Code		00:04:00
Unit 05: Queues			
	Module 01: Queues (Part-1)		00:06:00
	Module 02: Queues (Part-2)		00:06:00
	Module 03: Queue Source Code		00:04:00
Unit 06: Priority Queues (PQs)			
	Module 01: Priority Queues (PQs) with an interlude on heaps		00:13:00
	Module 02: Turning Min PQ into Max PQ		00:06:00
	Module 03: Adding Elements to Binary Heap		00:10:00
	Module 04: Removing Elements from Binary Heap		00:14:00
	Module 05: Priority Queue Binary Heap Source Code		00:16:00
Unit 07: Union Find			
	Module 01: Disjoint Set		00:06:00
	Module 02: Kruskal's Algorithm		00:06:00
	Module 03: Union and Find Operations		00:11:00
	Module 04: Path Compression Union Find		00:07:00
	Module 05: Union Find Source Code		00:08:00
Unit 08: Binary Search Trees			
	Module 01: Binary Trees and Binary Search Trees (BST)		00:13:00
	Module 02: Inserting Element into a Binary Search Tree (BST)		00:06:00
	Module 03: Removing Element from a Binary Search Tree (BST)		00:14:00
	Module 04: Tree Traversals		00:12:00
	Module 05: Binary Search Source Code		00:13:00
Unit 09: Fenwick Tree			
	Module 01: Fenwick Tree Construction		00:06:00
	Module 02: Point Updates		00:05:00
	Module 03: Binary Indexed Tree		00:14:00
	Module 04: Fenwick Tree Source Code		00:06:00
Unit 10: Hash Tables			
	Module 01: Hash Table		00:17:00
	Module 02: Separate Chaining		00:08:00
	Module 03: Separate Chaining Source Code		00:12:00
	Module 04: Open Addressing		00:11:00
	Module 05: Linear Probing		00:14:00
	Module 06: Quadratic Probing		00:09:00
	Module 07: Double Hashing		00:15:00
	Module 08: Removing Element Open Addressing		00:08:00
	Module 09: Open Addressing Code		00:15:00
Unit 11: Suffix Array			
	Module 01: Introduction		00:03:00
	Module 02: The Longest Common Prefix (LCP) Array		00:03:00
	Module 03: Using SA/LCP Array to Find Unique Substrings		00:05:00
	Module 04: Longest Common Substring (LCS)		00:11:00
	Module 05: Longest Common Substring (LCS) Full Example		00:07:00
	Module 06: Longest Repeated Substring (LRS)		00:05:00
Unit 12: AVL Trees			
	Module 01: Balanced Binary Search Trees (BBSTs)		00:09:00
	Module 02: Inserting Elements into an AVL Tree		00:10:00
	Module 03: Removing an AVL Tree		00:09:00
	Module 04: AVL Tree Source Code		00:17:00
Unit 13: Indexed Priority Queue			
	Module 01: Indexed Priority Queue (Part-1)		00:25:00
	Module 02: Indexed Priority Queue Source Code		00:09:00
Unit 14: Sparse Tables			
	Module 01: Sparse Table		00:26:00
	Module 02: Sparse Table Source Code		00:07:00
Assignment			
	Assignment - Diploma in Data Structure at QLS Level 5		04:00:00
Order your QLS Endorsed Certificate			
	Order your QLS Endorsed Certificate		00:00:00

Studyhub UK

Learning & Development

Diploma in Management Studies (DMS)

ICDL (International Certification of Digital Literacy)

Diploma in Data Structure at QLS Level 5

<p>Recent developments in data analytics and statistics underscore the critical importance of understanding both data structure and analytics methodologies in today's data-driven world. With the exponential growth of data, businesses are increasingly relying on skilled professionals who can harness the power of data to drive informed decision-making. Our comprehensive <strong>Data Structure, Data Analytics with Statistics &amp; Data Science  QLS Endorsed Diploma </strong>bundle, endorsed by the Quality Licence Scheme (QLS) and accredited by the CPD Quality Standards (QS), offers a holistic approach to mastering data structure, data analytics, and statistical techniques.</p><p>In an era where data reigns supreme, organisations seek individuals who can navigate complex datasets with confidence and precision. This Data Structure, Data Analytics bundle equips learners with the essential skills and knowledge needed to excel in the field of data science and analytics. By delving into topics such as data manipulation, statistical analysis, and database management, participants gain a deep understanding of how to extract valuable insights from raw data. Moreover, our guided courses in career development and communication empower learners to effectively communicate their findings and advance their professional journey.</p><p><strong>Bundle Include includes:</strong></p><p><strong>QLS Endorsed Courses:</strong></p><ul><li><strong>Course 01:</strong> Certificate in Data Analytics with Tableau at QLS Level 3</li><li><strong>Course 02:</strong> Diploma in Data Structure at QLS Level 5</li><li><strong>Course 03:</strong> Advanced Diploma in Statistics &amp; Probability for Data Science &amp; Machine Learning at QLS Level 7</li></ul><p><strong>CPD QS Accredited Courses:</strong></p><ul><li><strong>Course 04:</strong> Business and Data Analytics for Beginners</li><li><strong>Course 05:</strong> Learn Financial Analytics and Statistical Tools</li><li><strong>Course 06: </strong>SQL For Data Analytics &amp; Database Development</li><li><strong>Course 07:</strong> Big Data Analytics with PySpark Power BI and MongoDB</li><li><strong>Course 08</strong>: Google Data Studio: Data Analytics </li><li><strong>Course 09: </strong>Business Intelligence Analyst</li><li><strong>Course 10</strong>: Spatial Data Visualization and Machine Learning in Python</li><li><strong>Course 11:</strong> Data Analysis In Excel</li></ul><p>Take your career to the next level with our Data Structure, Data Analytics bundle that includes technical courses and five guided courses focused on personal development and career growth.</p><ul><li><strong>Course 12:</strong> Career Development Plan Fundamentals</li><li><strong>Course 13</strong>: CV Writing and Job Searching</li><li><strong>Course 14:</strong> Networking Skills for Personal Success</li><li><strong>Course 15: </strong>Ace Your Presentations: Public Speaking Masterclass</li><li><strong>Course 16</strong>: Decision Making and Critical Thinking </li></ul><p>Seize this opportunity to elevate your career with our comprehensive Data Structure, Data Analytics bundle, endorsed by the prestigious QLS and accredited by CPD.Data Structure, Data Analytics with Statistics &amp; Data Science QLS Endorsed Diploma.</p><p><strong>Learning Outcomes:</strong></p><p>Upon completion of this Data Structure, Data Analytics bundle, participants will be able to:</p><ul><li>Demonstrate proficiency in data analytics tools such as Tableau, SQL, and Google Data Studio through Data Structure, Data Analytics courses.</li><li>Apply statistical techniques to analyse and interpret data for informed decision-making.</li><li>Design and implement data structures to efficiently store and retrieve information.</li><li>Utilise machine learning algorithms for predictive analysis and pattern recognition.</li><li>Develop effective communication and presentation skills to convey insights to stakeholders.</li><li>Navigate career development pathways in the field of data science and analytics.</li></ul><p>This Data Structure, Data Analytics course bundle provides a deep dive into the foundational principles of data structure, data analytics, and statistical methodologies. Participants will explore the fundamental concepts of data manipulation, including sorting, searching, and storing data efficiently. Through hands-on exercises and theoretical discussions, learners will gain a solid understanding of various data structures such as arrays, linked lists, trees, and graphs, along with their applications in real-world scenarios.</p><p>Moreover, the Data Structure, Data Analytics bundle encompasses a comprehensive exploration of data analytics techniques, equipping participants with the skills to extract actionable insights from complex datasets. From descriptive and inferential statistics to predictive modelling and machine learning algorithms, learners will discover how to uncover patterns, trends, and correlations within data, enabling informed decision-making and strategic planning. Throughout the Data Structure, Data Analytics course, emphasis is placed on practical applications and case studies, allowing participants to apply their knowledge to solve real-world problems in diverse domains.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        160 CPD hours / points
                                    </div>
                                        <div>
                                            Accredited by CPD Quality Standards
                                        </div>
                                </div>
                            </div>
                        </div><h2>
        
        <div>Who is this course for?</div>
    </h2>

                        <div id="who_is_this_course_for">
                            <p>This Data Structure, Data Analytics course is perfect for:</p><ul><li>Aspiring data analysts seeking to enhance their analytical skills acrod Data Structure, Data Analytics courses. </li><li>Professionals transitioning into roles requiring proficiency in data analytics.</li><li>Students pursuing careers in data science, statistics, or related fields.</li><li>Business professionals looking to leverage data for strategic decision-making.</li><li>Individuals interested in advancing their career prospects in the field of data analytics.</li><li>Anyone seeking to gain a comprehensive understanding of data structure, analytics, and statistics.</li></ul>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>You are warmly invited to register for this bundle. Please be aware that there are no formal entry requirements or qualifications necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.</p>
                        </div><h2>
        
        <div>Career path</div>
    </h2>

                        <div id="career_path">
                            <p>Upon completion of the Data Structure, Data Analytics courses, you will be able to:</p><ul><li>Data Analyst</li><li>Business Intelligence Analyst</li><li>Data Scientist</li><li>Statistician</li><li>Database Administrator</li><li>Machine Learning Engineer</li><li>Data Engineer</li></ul>
                        </div><h2>
        
        <div>Certificates</div>
    </h2>

                        <div id="optional_extras">
                                    <h4>13 CPD Quality Standard Certificates</h4>
        <p>
            Digital certificate -
            Included
        </p>
        <h4>3 QLS Endorsed Certificates</h4>
        <p>
            Hard copy certificate -
            Included
        </p>

                        </div>

Recent developments in data analytics and statistics underscore the critical importance of understanding both data structure and analytics methodologies in today's data-driven world. With the exponential growth of data, businesses are increasingly relying on skilled professionals who can harness the power of data to drive informed decision-making. Our comprehensive Data Structure, Data Analytics with Statistics & Data Science QLS Endorsed Diploma bundle, endorsed by the Quality Licence Scheme (QLS) and accredited by the CPD Quality Standards (QS), offers a holistic approach to mastering data structure, data analytics, and statistical techniques.
In an era where data reigns supreme, organisations seek individuals who can navigate complex datasets with confidence and precision. This Data Structure, Data Analytics bundle equips learners with the essential skills and knowledge needed to excel in the field of data science and analytics. By delving into topics such as data manipulation, statistical analysis, and database management, participants gain a deep understanding of how to extract valuable insights from raw data. Moreover, our guided courses in career development and communication empower learners to effectively communicate their findings and advance their professional journey.
Bundle Include includes:
QLS Endorsed Courses:
Course 01: Certificate in Data Analytics with Tableau at QLS Level 3
Course 02: Diploma in Data Structure at QLS Level 5
Course 03: Advanced Diploma in Statistics & Probability for Data Science & Machine Learning at QLS Level 7
CPD QS Accredited Courses:
Course 04: Business and Data Analytics for Beginners
Course 05: Learn Financial Analytics and Statistical Tools
Course 06: SQL For Data Analytics & Database Development
Course 07: Big Data Analytics with PySpark Power BI and MongoDB
Course 08: Google Data Studio: Data Analytics
Course 09: Business Intelligence Analyst
Course 10: Spatial Data Visualization and Machine Learning in Python
Course 11: Data Analysis In Excel
Take your career to the next level with our Data Structure, Data Analytics bundle that includes technical courses and five guided courses focused on personal development and career growth.
Course 12: Career Development Plan Fundamentals
Course 13: CV Writing and Job Searching
Course 14: Networking Skills for Personal Success
Course 15: Ace Your Presentations: Public Speaking Masterclass
Course 16: Decision Making and Critical Thinking
Seize this opportunity to elevate your career with our comprehensive Data Structure, Data Analytics bundle, endorsed by the prestigious QLS and accredited by CPD.Data Structure, Data Analytics with Statistics & Data Science QLS Endorsed Diploma.
Learning Outcomes:
Upon completion of this Data Structure, Data Analytics bundle, participants will be able to:
Demonstrate proficiency in data analytics tools such as Tableau, SQL, and Google Data Studio through Data Structure, Data Analytics courses.
Apply statistical techniques to analyse and interpret data for informed decision-making.
Design and implement data structures to efficiently store and retrieve information.
Utilise machine learning algorithms for predictive analysis and pattern recognition.
Develop effective communication and presentation skills to convey insights to stakeholders.
Navigate career development pathways in the field of data science and analytics.
This Data Structure, Data Analytics course bundle provides a deep dive into the foundational principles of data structure, data analytics, and statistical methodologies. Participants will explore the fundamental concepts of data manipulation, including sorting, searching, and storing data efficiently. Through hands-on exercises and theoretical discussions, learners will gain a solid understanding of various data structures such as arrays, linked lists, trees, and graphs, along with their applications in real-world scenarios.
Moreover, the Data Structure, Data Analytics bundle encompasses a comprehensive exploration of data analytics techniques, equipping participants with the skills to extract actionable insights from complex datasets. From descriptive and inferential statistics to predictive modelling and machine learning algorithms, learners will discover how to uncover patterns, trends, and correlations within data, enabling informed decision-making and strategic planning. Throughout the Data Structure, Data Analytics course, emphasis is placed on practical applications and case studies, allowing participants to apply their knowledge to solve real-world problems in diverse domains.
 CPD 
160 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
This Data Structure, Data Analytics course is perfect for:
Aspiring data analysts seeking to enhance their analytical skills acrod Data Structure, Data Analytics courses.
Professionals transitioning into roles requiring proficiency in data analytics.
Students pursuing careers in data science, statistics, or related fields.
Business professionals looking to leverage data for strategic decision-making.
Individuals interested in advancing their career prospects in the field of data analytics.
Anyone seeking to gain a comprehensive understanding of data structure, analytics, and statistics.
 Requirements 
You are warmly invited to register for this bundle. Please be aware that there are no formal entry requirements or qualifications necessary. This curriculum has been crafted to be open to everyone, regardless of previous experience or educational attainment.
 Career path 
Upon completion of the Data Structure, Data Analytics courses, you will be able to:
Data Analyst
Business Intelligence Analyst
Data Scientist
Statistician
Database Administrator
Machine Learning Engineer
Data Engineer
 Certificates 
13 CPD Quality Standard Certificates
 Digital certificate - Included 
3 QLS Endorsed Certificates
 Hard copy certificate - Included 

Compliance Central

Business Analytics & Intelligence

Data Engineering

Big Data Analytics

Data Skills

Data Structure, Data Analytics with Statistics & Data Science  QLS Endorsed Diploma

Algorithms

This course takes you through all the important topics of data structure and algorithms from scratch. You will learn how to solve real-world problems with linked lists, stacks, queues, sorting algorithms, and a lot more using Python.

Data Structures and Algorithms: The Complete Masterclass

<div>
	<h3><b>Course Overview</b></h3>
<p><span>Do you know, effective use of data structure can increase the efficiency of your software design process? To create efficient algorithms and continue a smooth software design process Data Structure is one of the most fundamental ingredients. Learn the basics of data structure and how you can use them from this </span><b>Easy to Advanced Data Structures Masterclass </b><span>course and create incredible software designs using that knowledge.</span></p>
<p><span>This </span><b>Easy to Advanced Data Structures Masterclass</b><span> course will help you to strengthen your basics, clear misunderstandings and get hold of the functions of data structure and how you can use it. The animated video lessons will help you understand data Structure easily. You will learn about Static and dynamic arrays, linked lists, stacks, queues, search trees, hash tables, sparse tables and many other functions that will help you understand how you can use data structure and create efficient software designs.</span></p>
<h3><b>Learning Outcomes</b></h3>
<ul>
<li><span>Understand the basics of data structure </span></li>
<li><span>Familiarize with the algorithms associated with data structure </span></li>
<li><span>Be able to include linked lists, dynamic arrays, queues and stacks in your data structure project </span></li>
<li><span>Learn what Static and dynamic arrays are </span></li>
<li><span>Be able to Union or disjoint sets in your data table </span></li>
<li><span>Get a clear understanding of hash tables and how they work</span></li>
</ul>
<h3><b>Who is this course for?</b></h3>
<p><span>This course is ideal for anyone who wants to learn about data structure or strengthen their basics. It is especially helpful for those who work in the IT industry and deal with database management.</span></p>
<h3><b>Entry Requirement</b></h3>
<ul>
<li><span>This course is available to all learners, of all academic backgrounds.</span></li>
<li><span>Learners should be aged 16 or over to undertake the qualification.</span></li>
<li><span>Good understanding of English language, numeracy and ICT are required to attend this course.</span></li>
</ul>
<h3><b>Certification</b></h3>
<ul>
<li><span>After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24.</span></li>
<li><span>PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days.</span></li>
</ul>
<h3><b>Why choose us?</b></h3>
<ul>
<li><span>Affordable, engaging &amp; high-quality e-learning study materials;</span></li>
<li><span>Tutorial videos/materials from the industry-leading experts;</span></li>
<li><span>Study in a user-friendly, advanced online learning platform;</span></li>
<li><span>Efficient exam systems for the assessment and instant result;</span></li>
<li><span>The UK &amp; internationally recognized accredited qualification;</span></li>
<li><span>Access to course content on mobile, tablet or desktop from anywhere anytime;</span></li>
<li><span>The benefit of career advancement opportunities;</span></li>
<li><span>24/7 student support via email.</span></li>
</ul>
<h3><b>Career Path</b></h3>
<p><span><strong>Easy to Advanced Data Structures Masterclass</strong> is a useful qualification to possess and would be beneficial for any related profession or industry such as:</span></p>
<ul>
<li>Software Engineers</li>
<li><span>Programmers </span></li>
<li><span>Web Designers </span></li>
<li><span>Web Developers </span></li>
<li><span>App Developers</span></li>
</ul>
		</div><table width="100%">				<tbody><tr>
					<td colspan="4">Unit 01: Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Promo Video</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Data Structure Introduction</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Computational Complexity Analysis</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 02: Arrays</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Static and Dynamic Arrays</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Dynamic Arrays Source Code</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 03: Linked List</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Singly and Doubly Linked Lists</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Doubly Linked Lists Source Code</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 04: Stack</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Stack</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Stack Implementation</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Stack Source Code</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 05: Queues</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Queues (Part-1)</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Queues (Part-2)</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Queue Source Code</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 06: Priority Queues (PQs)</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Priority Queues (PQs) with an interlude on heaps</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Turning Min PQ into Max PQ</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Adding Elements to Binary Heap</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Removing Elements from Binary Heap</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Priority Queue Binary Heap Source Code</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 07: Union Find</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Disjoint Set</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Kruskal's Algorithm</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Union and Find Operations</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Path Compression Union Find</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Union Find Source Code</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 08: Binary Search Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Binary Trees and Binary Search Trees (BST)</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Inserting Element into a Binary Search Tree (BST)</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Removing Element from a Binary Search Tree (BST)</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Tree Traversals</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05:  Binary Search Source Code</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 09: Fenwick Tree</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Fenwick Tree Construction</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Point Updates</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Binary Indexed Tree</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Fenwick Tree Source Code</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 10: Hash Tables</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Hash Table</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Separate Chaining</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Separate Chaining Source Code</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Open Addressing</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Linear Probing</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 06: Quadratic Probing</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 07: Double Hashing</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 08: Removing Element Open Addressing</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 09: Open Addressing Code</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 11: Suffix Array</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Introduction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: The Longest Common Prefix (LCP) Array</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03:  Using SA/LCP Array to Find Unique Substrings</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Longest Common Substring (LCS)</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Longest Common Substring (LCS) Full Example</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 06: Longest Repeated Substring (LRS)</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 12: AVL Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Balanced Binary Search Trees (BBSTs)</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Inserting Elements into an AVL Tree</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Removing an AVL Tree</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: AVL Tree Source Code</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 13: Indexed Priority Queue</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Indexed Priority Queue (Part-1)</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Indexed Priority Queue Source Code</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 14: Sparse Tables</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Sparse Table</td>
					<td> </td>
					<td><span><i></i> 00:26:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Sparse Table Source Code</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Certificate and Transcript</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Order Your Certificates and Transcripts</td>
					<td> </td>
					<td><span><i></i> 00:00:00</span></td>
				</tr>
				</tbody></table>

Course Overview
Do you know, effective use of data structure can increase the efficiency of your software design process? To create efficient algorithms and continue a smooth software design process Data Structure is one of the most fundamental ingredients. Learn the basics of data structure and how you can use them from this Easy to Advanced Data Structures Masterclass course and create incredible software designs using that knowledge.
This Easy to Advanced Data Structures Masterclass course will help you to strengthen your basics, clear misunderstandings and get hold of the functions of data structure and how you can use it. The animated video lessons will help you understand data Structure easily. You will learn about Static and dynamic arrays, linked lists, stacks, queues, search trees, hash tables, sparse tables and many other functions that will help you understand how you can use data structure and create efficient software designs.
Learning Outcomes
Understand the basics of data structure
Familiarize with the algorithms associated with data structure
Be able to include linked lists, dynamic arrays, queues and stacks in your data structure project
Learn what Static and dynamic arrays are
Be able to Union or disjoint sets in your data table
Get a clear understanding of hash tables and how they work
Who is this course for?
This course is ideal for anyone who wants to learn about data structure or strengthen their basics. It is especially helpful for those who work in the IT industry and deal with database management.
Entry Requirement
This course is available to all learners, of all academic backgrounds.
Learners should be aged 16 or over to undertake the qualification.
Good understanding of English language, numeracy and ICT are required to attend this course.
Certification
After you have successfully completed the course, you will be able to obtain an Accredited Certificate of Achievement. You can however also obtain a Course Completion Certificate following the course completion without sitting for the test. Certificates can be obtained either in hardcopy at the cost of £39 or in PDF format at the cost of £24.
PDF certificate's turnaround time is 24 hours, and for the hardcopy certificate, it is 3-9 working days.
Why choose us?
Affordable, engaging & high-quality e-learning study materials;
Tutorial videos/materials from the industry-leading experts;
Study in a user-friendly, advanced online learning platform;
Efficient exam systems for the assessment and instant result;
The UK & internationally recognized accredited qualification;
Access to course content on mobile, tablet or desktop from anywhere anytime;
The benefit of career advancement opportunities;
24/7 student support via email.
Career Path
Easy to Advanced Data Structures Masterclass is a useful qualification to possess and would be beneficial for any related profession or industry such as:
Software Engineers
Programmers
Web Designers
Web Developers
App Developers
Unit 01: Introduction			
	Module 01: Promo Video		00:02:00
	Module 02: Data Structure Introduction		00:05:00
	Module 03: Computational Complexity Analysis		00:13:00
Unit 02: Arrays			
	Module 01: Static and Dynamic Arrays		00:12:00
	Module 02: Dynamic Arrays Source Code		00:07:00
Unit 03: Linked List			
	Module 01: Singly and Doubly Linked Lists		00:15:00
	Module 02: Doubly Linked Lists Source Code		00:10:00
Unit 04: Stack			
	Module 01: Stack		00:12:00
	Module 02: Stack Implementation		00:04:00
	Module 03: Stack Source Code		00:04:00
Unit 05: Queues			
	Module 01: Queues (Part-1)		00:06:00
	Module 02: Queues (Part-2)		00:06:00
	Module 03: Queue Source Code		00:04:00
Unit 06: Priority Queues (PQs)			
	Module 01: Priority Queues (PQs) with an interlude on heaps		00:13:00
	Module 02: Turning Min PQ into Max PQ		00:06:00
	Module 03: Adding Elements to Binary Heap		00:10:00
	Module 04: Removing Elements from Binary Heap		00:14:00
	Module 05: Priority Queue Binary Heap Source Code		00:16:00
Unit 07: Union Find			
	Module 01: Disjoint Set		00:06:00
	Module 02: Kruskal's Algorithm		00:06:00
	Module 03: Union and Find Operations		00:11:00
	Module 04: Path Compression Union Find		00:07:00
	Module 05: Union Find Source Code		00:08:00
Unit 08: Binary Search Trees			
	Module 01: Binary Trees and Binary Search Trees (BST)		00:13:00
	Module 02: Inserting Element into a Binary Search Tree (BST)		00:06:00
	Module 03: Removing Element from a Binary Search Tree (BST)		00:14:00
	Module 04: Tree Traversals		00:12:00
	Module 05: Binary Search Source Code		00:13:00
Unit 09: Fenwick Tree			
	Module 01: Fenwick Tree Construction		00:06:00
	Module 02: Point Updates		00:05:00
	Module 03: Binary Indexed Tree		00:14:00
	Module 04: Fenwick Tree Source Code		00:06:00
Unit 10: Hash Tables			
	Module 01: Hash Table		00:17:00
	Module 02: Separate Chaining		00:08:00
	Module 03: Separate Chaining Source Code		00:12:00
	Module 04: Open Addressing		00:11:00
	Module 05: Linear Probing		00:14:00
	Module 06: Quadratic Probing		00:09:00
	Module 07: Double Hashing		00:15:00
	Module 08: Removing Element Open Addressing		00:08:00
	Module 09: Open Addressing Code		00:15:00
Unit 11: Suffix Array			
	Module 01: Introduction		00:03:00
	Module 02: The Longest Common Prefix (LCP) Array		00:03:00
	Module 03: Using SA/LCP Array to Find Unique Substrings		00:05:00
	Module 04: Longest Common Substring (LCS)		00:11:00
	Module 05: Longest Common Substring (LCS) Full Example		00:07:00
	Module 06: Longest Repeated Substring (LRS)		00:05:00
Unit 12: AVL Trees			
	Module 01: Balanced Binary Search Trees (BBSTs)		00:09:00
	Module 02: Inserting Elements into an AVL Tree		00:10:00
	Module 03: Removing an AVL Tree		00:09:00
	Module 04: AVL Tree Source Code		00:17:00
Unit 13: Indexed Priority Queue			
	Module 01: Indexed Priority Queue (Part-1)		00:25:00
	Module 02: Indexed Priority Queue Source Code		00:09:00
Unit 14: Sparse Tables			
	Module 01: Sparse Table		00:26:00
	Module 02: Sparse Table Source Code		00:07:00
Certificate and Transcript			
	Order Your Certificates and Transcripts		00:00:00

John Academy

Computing Skills

Programming Fundamentals

Easy to Advanced Data Structures Masterclass

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							</div><table width="100%">				<tbody><tr>
					<td colspan="4"> Unit 01: Introduction</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Promo Video</td>
					<td> </td>
					<td><span><i></i> 00:02:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Data Structure Introduction</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Computational Complexity Analysis</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 02: Arrays</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Static and Dynamic Arrays</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Dynamic Arrays Source Code</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td colspan="4"> Unit 03: Liked List</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Singly and Doubly Linked Lists</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Doubly Linked Lists Source Code</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 04: Stack</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Stack</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Stack Implementation</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Stack Source Code</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 05: Queues</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Queues (Part-1)</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Queues (Part-2)</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Queue Source Code</td>
					<td> </td>
					<td><span><i></i> 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 06: Priority Queues (PQs)</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Priority Queues (PQs) with an interlude on heaps</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Turning Min PQ into Max PQ</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Adding Elements to Binary Heap</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Removing Elements from Binary Heap</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Priority Queue Binary Heap Source Code</td>
					<td> </td>
					<td><span><i></i> 00:16:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 07: Union Find</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Disjoint Set</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Kruskal's Algorithm</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Union and Find Operations</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Path Compression Union Find</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Union Find Source Code</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 08: Binary Search Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Binary Trees and Binary Search Trees (BST)</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Inserting Element into a Binary Search Tree (BST)</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Removing Element from a Binary Search Tree (BST)</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Tree Traversals</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05:  Binary Search Source Code</td>
					<td> </td>
					<td><span><i></i> 00:13:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 09: Fenwick Tree</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Fenwick Tree Construction</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Point Updates</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Binary Indexed Tree</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Fenwick Tree Source Code</td>
					<td> </td>
					<td><span><i></i> 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 10: Hash Tables</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Hash Table</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Separate Chaining</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Separate Chaining Source Code</td>
					<td> </td>
					<td><span><i></i> 00:12:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Open Addressing</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Linear Probing</td>
					<td> </td>
					<td><span><i></i> 00:14:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 06: Quadratic Probing</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 07: Double Hashing</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 08: Removing Element Open Addressing</td>
					<td> </td>
					<td><span><i></i> 00:08:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 09: Open Addressing Code</td>
					<td> </td>
					<td><span><i></i> 00:15:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 11: Suffix Array</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Introduction</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: The Longest Common Prefix (LCP) Array</td>
					<td> </td>
					<td><span><i></i> 00:03:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03:  Using SA/LCP Array to Find Unique Substrings</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: Longest Common Substring (LCS)</td>
					<td> </td>
					<td><span><i></i> 00:11:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 05: Longest Common Substring (LCS) Full Example</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 06: Longest Repeated Substring (LRS)</td>
					<td> </td>
					<td><span><i></i> 00:05:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 12: AVL Trees</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Balanced Binary Search Trees (BBSTs)</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Inserting Elements into an AVL Tree</td>
					<td> </td>
					<td><span><i></i> 00:10:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 03: Removing an AVL Tree</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 04: AVL Tree Source Code</td>
					<td> </td>
					<td><span><i></i> 00:17:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 13: Indexed Priority Queue</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Indexed Priority Queue (Part-1)</td>
					<td> </td>
					<td><span><i></i> 00:25:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Indexed Priority Queue Source Code</td>
					<td> </td>
					<td><span><i></i> 00:09:00</span></td>
				</tr>
								<tr>
					<td colspan="4">Unit 14: Sparse Tables</td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 01: Sparse Table</td>
					<td> </td>
					<td><span><i></i> 00:26:00</span></td>
				</tr>
								<tr>
					<td><i></i></td>
					<td>Module 02: Sparse Table Source Code</td>
					<td> </td>
					<td><span><i></i> 00:07:00</span></td>
				</tr>
				</tbody></table>

Data Structure Level 5 is yet another 'Teacher's Choice' course from Teachers Training for a complete understanding of the fundamental topics. You are also entitled to exclusive tutor support and a professional CPD-accredited certificate in addition to the special discounted price for a limited time. Just like all our courses, this Data Structure Level 5 and its curriculum have also been designed by expert teachers so that teachers of tomorrow can learn from the best and equip themselves with all the necessary skills.
Consisting of several modules, the course teaches you everything you need to succeed in this profession.
The course can be studied part-time. You can become accredited within 9 hours studying at your own pace. Your qualification will be recognised and can be checked for validity on our dedicated website.
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Unit 01: Introduction			
	Module 01: Promo Video		00:02:00
	Module 02: Data Structure Introduction		00:05:00
	Module 03: Computational Complexity Analysis		00:13:00
Unit 02: Arrays			
	Module 01: Static and Dynamic Arrays		00:12:00
	Module 02: Dynamic Arrays Source Code		00:07:00
Unit 03: Liked List			
	Module 01: Singly and Doubly Linked Lists		00:15:00
	Module 02: Doubly Linked Lists Source Code		00:10:00
Unit 04: Stack			
	Module 01: Stack		00:12:00
	Module 02: Stack Implementation		00:04:00
	Module 03: Stack Source Code		00:04:00
Unit 05: Queues			
	Module 01: Queues (Part-1)		00:06:00
	Module 02: Queues (Part-2)		00:06:00
	Module 03: Queue Source Code		00:04:00
Unit 06: Priority Queues (PQs)			
	Module 01: Priority Queues (PQs) with an interlude on heaps		00:13:00
	Module 02: Turning Min PQ into Max PQ		00:06:00
	Module 03: Adding Elements to Binary Heap		00:10:00
	Module 04: Removing Elements from Binary Heap		00:14:00
	Module 05: Priority Queue Binary Heap Source Code		00:16:00
Unit 07: Union Find			
	Module 01: Disjoint Set		00:06:00
	Module 02: Kruskal's Algorithm		00:06:00
	Module 03: Union and Find Operations		00:11:00
	Module 04: Path Compression Union Find		00:07:00
	Module 05: Union Find Source Code		00:08:00
Unit 08: Binary Search Trees			
	Module 01: Binary Trees and Binary Search Trees (BST)		00:13:00
	Module 02: Inserting Element into a Binary Search Tree (BST)		00:06:00
	Module 03: Removing Element from a Binary Search Tree (BST)		00:14:00
	Module 04: Tree Traversals		00:12:00
	Module 05: Binary Search Source Code		00:13:00
Unit 09: Fenwick Tree			
	Module 01: Fenwick Tree Construction		00:06:00
	Module 02: Point Updates		00:05:00
	Module 03: Binary Indexed Tree		00:14:00
	Module 04: Fenwick Tree Source Code		00:06:00
Unit 10: Hash Tables			
	Module 01: Hash Table		00:17:00
	Module 02: Separate Chaining		00:08:00
	Module 03: Separate Chaining Source Code		00:12:00
	Module 04: Open Addressing		00:11:00
	Module 05: Linear Probing		00:14:00
	Module 06: Quadratic Probing		00:09:00
	Module 07: Double Hashing		00:15:00
	Module 08: Removing Element Open Addressing		00:08:00
	Module 09: Open Addressing Code		00:15:00
Unit 11: Suffix Array			
	Module 01: Introduction		00:03:00
	Module 02: The Longest Common Prefix (LCP) Array		00:03:00
	Module 03: Using SA/LCP Array to Find Unique Substrings		00:05:00
	Module 04: Longest Common Substring (LCS)		00:11:00
	Module 05: Longest Common Substring (LCS) Full Example		00:07:00
	Module 06: Longest Repeated Substring (LRS)		00:05:00
Unit 12: AVL Trees			
	Module 01: Balanced Binary Search Trees (BBSTs)		00:09:00
	Module 02: Inserting Elements into an AVL Tree		00:10:00
	Module 03: Removing an AVL Tree		00:09:00
	Module 04: AVL Tree Source Code		00:17:00
Unit 13: Indexed Priority Queue			
	Module 01: Indexed Priority Queue (Part-1)		00:25:00
	Module 02: Indexed Priority Queue Source Code		00:09:00
Unit 14: Sparse Tables			
	Module 01: Sparse Table		00:26:00
	Module 02: Sparse Table Source Code		00:07:00

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Data Structure Level 5

<strong>This course covers essential topics required for working with data structures and algorithms using JavaScript. From basics of data structures and algorithms to key concepts, such as arrays, lists, Big O time-space asymptomatic analysis, trees, and maps, this course will teach you everything with the help of engaging examples and activities.</strong>
        <p>Data structures allow you to organize code in the most efficient way to help you perform operations effectively. An algorithm is a step-by-step procedure for solving problems, be it solving a computer problem, writing source code, or innovating new programs. With this course, you'll learn the concepts of data structures and algorithms using JavaScript.
Starting with an introduction to data structures and algorithms, the course takes you through the concept of arrays, where you'll solve a minesweeper challenge to understand the importance of an array in data structures. Next, you'll become familiar with lists and discover how Big O helps in describing the complexity of an algorithm. As you advance, you'll gain insights into stacks, queues, recursion, and trees by solving challenges such as condense, Josephus, palindrome, and Morse code. Toward the end, you'll get an overview of hash maps and graphs.
By the end of this course, you'll have developed a solid understanding of data structures and algorithms essential for building reliable software.

The code files are added on the GitHub repository at https://github.com/PacktPublishing/Data-Structures-in-JavaScript-Master-the-Fundamentals</p>
        <h2>What You Will Learn</h2><p>Distinguish between arrays and lists<br>Implement list push, grow, remove, and insert<br>Understand the Big O time-space asymptotic analysis<br>Find out when to use stacks and trees<br>Discover the applications of trees in data structure and algorithm<br>Handle collisions with hash maps</p>
        <h2>Audience</h2><p>If you are looking to build a solid foundation in data structures and algorithms to design and develop software, then this is the course for you.</p>
        <h2>Approach</h2><p>With the help of engaging challenges, this course helps you learn the fundamentals of data structures and algorithms using JavaScript.</p>
        <h2>Key Features</h2><p>Get to grips with the fundamentals of data structures and algorithms * Learn how to solve common computer programs using algorithms * Solve challenges such as Minesweeper, Sieve of Eratosthenes, and Morse code</p>
        <h2>Github Repo</h2><p>https://github.com/PacktPublishing/Data-Structures-in-JavaScript-Master-the-Fundamentals</p>
        <h2>About the Author</h2><h3>Eduonix Learning Solutions</h3><p>Eduonix learning Solutions is a premier training and skill development organization which was started with a vision to bring world class training content, pedagogy and best learning practices to everyone's doorsteps . Eduonix aims to identify and provide the best learning and training environment. It identifies industry veterans and content creators around the globe and bring it to the global audience using number of intuitive platforms for easy and affordable access to quality content. Eduonix offers easy to understand online courses and workshops for everyday people. If you have ever wanted to learn a new skill, but don't want to attend four years of college to do it, we have a solution for you.</p>
        <h2>Course Outline</h2><h4>1. Introduction</h4><p></p><br><h4>2. Arrays</h4><p></p><br><h4>3. Lists</h4><p></p><br><h4>4. Big O Time Space Asymptotic Analysis</h4><p></p><br><h4>5. Linked Lists</h4><p></p><br><h4>6. Stacks</h4><p></p><br><h4>7. Queues</h4><p></p><br><h4>8. Recursion</h4><p></p><br><h4>9. Trees</h4><p></p><br><h4>10. Hashmaps</h4><p></p><br><h4>11. Graphs</h4><p></p>

This course covers essential topics required for working with data structures and algorithms using JavaScript. From basics of data structures and algorithms to key concepts, such as arrays, lists, Big O time-space asymptomatic analysis, trees, and maps, this course will teach you everything with the help of engaging examples and activities.
Data structures allow you to organize code in the most efficient way to help you perform operations effectively. An algorithm is a step-by-step procedure for solving problems, be it solving a computer problem, writing source code, or innovating new programs. With this course, you'll learn the concepts of data structures and algorithms using JavaScript.
Starting with an introduction to data structures and algorithms, the course takes you through the concept of arrays, where you'll solve a minesweeper challenge to understand the importance of an array in data structures. Next, you'll become familiar with lists and discover how Big O helps in describing the complexity of an algorithm. As you advance, you'll gain insights into stacks, queues, recursion, and trees by solving challenges such as condense, Josephus, palindrome, and Morse code. Toward the end, you'll get an overview of hash maps and graphs.
By the end of this course, you'll have developed a solid understanding of data structures and algorithms essential for building reliable software. The code files are added on the GitHub repository at https://github.com/PacktPublishing/Data-Structures-in-JavaScript-Master-the-Fundamentals
What You Will Learn
Distinguish between arrays and lists
Implement list push, grow, remove, and insert
Understand the Big O time-space asymptotic analysis
Find out when to use stacks and trees
Discover the applications of trees in data structure and algorithm
Handle collisions with hash maps
Audience
If you are looking to build a solid foundation in data structures and algorithms to design and develop software, then this is the course for you.
Approach
With the help of engaging challenges, this course helps you learn the fundamentals of data structures and algorithms using JavaScript.
Key Features
Get to grips with the fundamentals of data structures and algorithms * Learn how to solve common computer programs using algorithms * Solve challenges such as Minesweeper, Sieve of Eratosthenes, and Morse code
Github Repo
https://github.com/PacktPublishing/Data-Structures-in-JavaScript-Master-the-Fundamentals
About the Author
Eduonix Learning Solutions
Eduonix learning Solutions is a premier training and skill development organization which was started with a vision to bring world class training content, pedagogy and best learning practices to everyone's doorsteps . Eduonix aims to identify and provide the best learning and training environment. It identifies industry veterans and content creators around the globe and bring it to the global audience using number of intuitive platforms for easy and affordable access to quality content. Eduonix offers easy to understand online courses and workshops for everyday people. If you have ever wanted to learn a new skill, but don't want to attend four years of college to do it, we have a solution for you.
Course Outline
1. Introduction

2. Arrays

3. Lists

4. Big O Time Space Asymptotic Analysis

5. Linked Lists

6. Stacks

7. Queues

8. Recursion

9. Trees

10. Hashmaps

11. Graphs


This course covers essential topics required for working with data structures and algorithms using JavaScript. From basics of data structures and algorithms to key concepts, such as arrays, lists, Big O time-space asymptomatic analysis, trees, and maps, this course will teach you everything with the help of engaging examples and activities.

Data Structures in JavaScript - Master the Fundamentals

Learn to program with Python 3, visualize algorithms and data structures, and implement them in Python projects
This course is one of the most comprehensive and beginner-friendly courses on learning to code with Python-one of the top programming languages in the World-and using it to build algorithms and data structures with projects from scratch. We will walk you step-by-step through the fascinating world of Python programming using visualizations of programs as they execute, algorithms as they run, and data structures as they are constructed. Nothing is left to the imagination; you'll see it all and then build it all. Since it caters to a broad spectrum of students, the course is split into two parts: part 1 focusing on the Python programming language and part 2 focusing on Algorithms, data structures, performance analysis, and larger-scale projects. Part 1: Python and programming fundamentals
•	Text - Strings
•	Numbers - ints and floats
•	Execution flow control - branching with if/elif/else
•	Compound data types - lists, dictionaries, tuples, and sets
•	Iterables and iteration with generators, for and while loops, and more!
•	Functions, execution context and frames, and building custom functions
•	List comprehension
•	Lambda expressions
•	Generators and creating your own generators with yield
•	Objects and building classes, methods, and special methods
•	Reading from and writing to files using context managers
•	Visualization with each topic and more! Part 2: Algorithms, Data Structures, and Performance Analysis
•	Sorting algorithms (basic) - bubble sort, selection sort, and insertion sort
•	Sorting algorithms (advanced) - merge sort and quick sort
•	Big O notation, complexity analysis, divide and conquer, and math visualizations
•	Recursion in-depth with examples
•	Searching algorithms - bisection search and hashing
•	Data structures with linked lists, stacks, queues, trees, and binary search trees
•	Operations with data structures - insert, search, update, and delete
•	Multiple projects with increasing levels of complexity to tie concepts together
•	Visualizations of all algorithms, data structure, operations, and more! All the codes and supporting files for this course are available at - https://github.com/PacktPublishing/Python-3-Project-based-Python-Algorithms-Data-Structures
What You Will Learn
Learn Python 3 from scratch, in-depth
Understand the fundamentals of programming languages
Learn to visualize algorithms, data structures, program executions, and information flows
Learn to use Python to build projects
Audience
This course is designed for beginners who have never programmed before; programmers switching to Python; intermediate and advanced programmers looking to understand algorithms and data structures; Job interview candidates looking to understand the subject matter behind technical interview questions, and anyone keen to learn how to visualize programs and information flows.
Approach
This course will walk you step-by-step through the fascinating world of Python programming using visualizations of programs as they execute, algorithms as they run, and data structures as they are constructed.
Key Features
Learn to code with Python while building projects and implementing algorithms and data structures * Understand the fundamentals of programming languages
Github Repo
https://github.com/packtpublishing/python-3-project-based-python-algorithms-data-structures
About the Author
Mashrur Hossain
Mashrur is a full-time programming instructor specializing in programming fundamentals, web application development, machine learning, and cybersecurity. He has been a technology professional for over a decade and has degrees in Computer Science and Economics. His niche is building comprehensive career-focused technology courses for students entering new/complex and challenging fields in today's technology space. This is a nice segway for him since his real passion is building and tinkering with programming languages. He loves everything to do with development and learning about new tools and technologies. His favorite languages are Python and Ruby on Rails, and his favorite tech fields are web app development, machine learning, and data analytics (which is where Ruby on Rails and Python fall into place nicely). He encourages his students to focus on these technologies as well. In the past, he has worked with Enterprise Software Systems with roles played in analysis, development, management, and training. He led projects using both agile and waterfall methodologies and thus is well versed in the inner workings of the software development and delivery world. During his time in corporate America, he realized how much he enjoyed training new hires and new team members and helping them succeed. He dedicated a good amount of time over 7 years on-boarding new analysts and developers and then worked with them to build and maintain systems which put him in a unique position to know and understand what new entrants to a field need in order to succeed. He strongly believes in focusing on fundamentals and practice; and not in shortcuts or gimmicks.
Course Outline
1. Introduction

1. Introduction
This video introduces the course.
2. Course structure and content overview
An in-depth look at the structure of the course and an overview of the content of every section
2. Development environment setup

1. Section intro and overview
A look at development environments, some options and what we'll use in the course
2. Download and install Python
Explore various options available and download Python 3
3. Setup Atom as text editor (setup used in this course)
Download and customize the Atom text editor. This is the editor used in this course throughout
4. Exploring Jupyter Notebooks interface (optional)
A detailed look at using Jupyter Notebooks for developing Python code
3. Python in-depth

1. Section intro and overview
Introduction to material covered in the section and the who it's intended for
2. Command line/Terminal basics
A quick look at using the command line/terminal window to navigate file directories
3. Strings, variables, top down execution flow
Working with strings and variables to handle text
4. Strings: concatenation, indexing, slicing, python console
Detailed look at string concatenation, indexing and slicing
5. String methods, functions and import statements
Wrap up our look at strings with some functions and methods available to string objects. Also investigate import statements and how they work
6. Print formatting and special characters
A look at how to format print statements and use special characters within strings
7. Numbers, math, type casting and input
Working with numbers and computation along with type casting and getting input from user
8. Introduction to branching (if, elif, else) and conditionals
An introductory look at branching and how it works including real code examples from projects built in the course in later sections
9. Building if, elif, else blocks incrementally
An in-depth look at building if elif and else blocks using conditional tests and boolean values
10. Lists, dicts, sets and tuples - Intro to compound data types in Python
An introductory look at collections in Python
11. Lists - an in-depth look 1
Working with lists and exploring functions and methods
12. Lists - an in-depth look 2
Working with sublists, slicing, indexing and basic iteration
13. Dictionaries, sets and tuples
Working with dictionaries, sets and tuples
14. Iterators, for loops, generators, list comprehension
An in-depth look at using for loops in combination with iterables and generators along with list comprehension
15. While loops, enumerate, zip
Conclude our look at iterators, generators and popular functions associated with them with while loops and enumerate and zip functions
16. Functions - an introductory look
A look at functions, their structure, properties and examples
17. Functions - implementation step by step
Take a program and build a function step by step
18. Functions - execution context, frames, mutable vs. immutable arguments in-depth
An in-depth look at the execution context (with global and function frames) of python programs, along with the differences between mutable and immutable data types passed in as parameters
19. Classes and objects - an introductory look
An introductory look at objects with example classes built in the course
20. Building a custom Student class and intro to special methods
Learning the basics of building a class from scratch and special __init__ method
21. Add some methods to the class
Add the add and remove_course methods to the class
22. Special methods and what they are
Explore some special methods like __init__ and __repr__
23. Reading from and writing to files
Working with files and performing read and write operations using context managers and permissions
24. Add read functionality and utilize special and static methods
Add previously built read functionality to the student class using static and regular methods, along with another special method __eq__ to test for equality based on custom definition
25. Inheritance, subclasses and complete example class
Complete Student class and look at inheritance and subclasses
26. Lambda expressions and map function
Explore lambda expressions and use them in conjunction with other functions like map
27. Generators - under the hood
You have already seen generator objects in use, let's take a deeper look at what they are and how they work.
28. Build your own generators using yield
Create custom generators using yield
4. Algorithms - Sort, performance, complexity and big O notation

1. Introduction to section 4 and overview of the material covered in it
Intro to part 2 of this course with section 4 and sorting algorithms, performance analysis and recursion
2. Bubble sort demonstration and complexity analysis
Visual presentation and complexity analysis of the bubble sort algorithm
3. Bubble sort implementation
Implement the bubble sort algorithm step by step
4. Selection sort demonstration and complexity analysis
Visual presentation and complexity analysis of the selection sort algorithm
5. Selection sort implementation
Implement the selection sort algorithm step by step
6. Insertion sort demonstration and assignment handoff
Complete visualization of the insertion sort algorithm and assignment handoff for implementation
7. Insertion sort programmatic execution step by step
Programmatic visual of execution steps performed by the insertion sort algorithm as it sorts a list of 5 elements
8. Performance measures - deep dive with a programmatic view
Look at best, worst and average cases for complexity in more detail
9. O(nlog(n)) performance and algorithm prerequisites
Discuss algorithms that achieve O(nlog(n)) performance and prerequisites for next one
10. Analyze log(n), visualize the math behind it and how it relates to algorithms
An in-depth look at log(n) and what it represents
11. Merge sort visualization and complexity analysis
Look at the merge sort algorithm and its big O analysis
12. Implement merge function - part 1
Start building the merge function - comparison
13. Implement merge function - part 2
Continue building merge function - iteration through lists
14. Implement merge function - part 3
Complete merge function - add remaining items
15. A look at the recursive divide function
A look at the divide/split function which introduces recursion
16. In-depth look at execution context of recursive divide function
Analyze execution context and frames created by recursive divide function in-depth
17. Recursion mini-project 1 - Countdown timer
Build a countdown timer using recursion
18. Recursion mini-project 2 - Factorial
Build a recursive factorial function step by step
19. Recursion mini-project 3 - Fibonacci series
Understand, break down and implement a recursive fibonacci function
20. Complete merge sort algorithm and analyze updated execution context
Complete implementation of the merge sort algorithm and visualize execution context
21. Quicksort demo
A visual look at the Quicksort algorithm
22. Quicksort implementation
Implement Quicksort algorithm step by step
23. Section final project objective and motivation
Final project kickoff and look at objective and motivation for building the project
24. Project specs and runtime execution intro
Look at specs for the project along with actual completed program execution
25. Project phase 1: Build random int list generator
Build a function that generates lists of integers of various sizes and ranges
26. Project phase 2: Get input from user for size and range
Get the list size and range of ints for each list element from the user during program execution
27. Project phase 3: Add functions, calculate and analyze runtime
Add functions to analyze and implement time analysis functionality
28. Project phase 4: Extract redundancies, create function and cleanup code
Add additional functionality to the program by extracting redundant code into a function and beautifying output
29. Project phase 5: Add multiple run functionality and perform additional testing
Add ability to perform several runs of the functions under analysis and perform testing using additional functions and test cases
5. Algorithms - Search and abstract data structures

1. Introduction to section 5
Intro to section 5 and a brief overview of the material covered in this section
2. Intro to search - Linear, Bisection/Binary search
A look at basic searching techniques and the bisection search algorithm
3. Bisection/Binary search - Iterative implementation
Build the binary search function using iteratively
4. Bisection search - recursive implementation
Replace the while loop built in the iterative solution in the prior video with recursive function calls to achieve the same goal
5. Project handoff: Bringing it together
Design and build a project that utilizes the algorithms and tools that have been developed so far
6. Project conclusion walkthrough
Go through implementation steps from concluded project
7. Hashmaps and O(1) search complexity
Visualization of how a hashmap works to achieve O(1) complexity/performance
8. Hash project 1: Define and set up class blueprint with __init__ and __str__
Define the structure requirement for a hash table and setup the class definition
9. Hash project 2: Set up insert and hashing functionality for data structure
Start building the set_val method and introduce python's hash function
10. Hash project 3: Add update functionality
Modify set_val method to include update functionality
11. Hash project 4: Build search method
Implement the get_val method and add search functionality for the class
12. Project: Use hash structure in a practical exercise - Quote finder
Start a project using the hash table structure/class that was built called quote finder
13. Project: Complete quote finder using hash table
Project execution and completion for quote finder using the implemented hash table class
14. Intro to linear data structures - Linked Lists
Introduction to idea and operations of linked lists.
15. Build a custom linked list
Build methods in the custom linked list class and homework assignment
16. Recursively reverse a linked list
Reverse a linked list using recursion
17. Visualize Stacks and Queues, and their operations
Visualize and analyze operations associated with Stacks and Queues
18. Introduction to Trees and Binary Search Trees
An in-depth look at trees, rules surrounding them and a tree known as Binary Search Tree
19. In-order traversal of a Binary Search Tree
In-depth look at in-order traversal and how it results in a sorted representation of the nodes of a BST
20. Build a Binary Search Tree from scratch - Insert
Inserting nodes into a BST in-depth with test cases
21. BST from scratch - In-order traversal
Build the in-order traversal method so nodes can be displayed in order based on the values of their keys
22. BST from scratch - Search
Add a search method to the BST class
23. BST from scratch - Delete demo
Visualize and understand the process of the delete functionality from a BST
24. BST - Deleting leaf nodes
Delete scenario one - deleting nodes with no children (leaf nodes)
25. BST - Deleting nodes with 1 child node
Delete scenario 2 - deleting nodes with 1 child node
26. BST - Deleting nodes with 2 children
Delete scenario 3 - deleting nodes with both left and right child nodes
27. Project: Job Scheduler using Binary Search Trees - Introduction
Introduction to the job scheduler project - to be implemented by students, including motivation and requirements
28. Project: Job Scheduler execution flow
Step by step display of the functionality of the completed job scheduler project
29. Project: Job Scheduler implementation tips and notes
Go through my implementation of the project and discuss code, tips and pointers
30. Thank you for taking the course and next steps
Here, the author thanks everyone for taking the course and suggestions for possible next steps

Learn to program with Python 3, visualize algorithms and data structures, and implement them in Python projects

Python 3: Project-based Python, Algorithms, Data Structures

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			<h2>Overview
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					<p><span lang="EN-GB">This comprehensive course on <strong>Data Structures Complete Course</strong> will deepen your understanding on this topic.</span></p><p><span lang="EN-GB">After successful completion of this course you can acquire the required skills in this sector. This <strong>Data Structures Complete Course</strong><strong> </strong>comes with accredited certification, which will enhance your CV and make you worthy in the job market.</span></p><p><span lang="EN-GB">So enrol in this course today to fast track your career ladder.</span></p>					</div>
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			<h2>How will I get my certificate?</h2>		</div>
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					<p>You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.<br /></p>					</div>
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			<h2>Who is This course for?</h2>		</div>
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					<p><span lang="EN-GB">There is no experience or previous qualifications required for enrolment on this <strong>Data Structures Complete Course</strong>. It is available to all students, of all academic backgrounds.</span></p>					</div>
						</div>
				</div>
				<div>
				<div>
			<h2>Requirements</h2>		</div>
				</div>
				<div>
				<div>
								<div>
					<p><span lang="EN-GB">Our<strong> Data Structures Complete Course</strong> is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.</span></p><p><span lang="EN-GB">There is no time limit for completing this course, it can be studied in your own time at your own pace.</span></p>					</div>
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				<div>
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			<h2>Career Path</h2>		</div>
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								<div>
					<p><span lang="EN-GB">Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business &amp; Management, Admin, Accountancy &amp; Finance, Secretarial &amp; PA, Teaching &amp; Mentoring etc.</span></p>
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					</div> <h3>Course Curriculum</h3><div><p>14 sections • 55 lectures • 09:02:00 total length</p></div><div><div><div><div><div><div><div><p>•Module 01: Promo Video: 00:02:00</p></div><div><div><p>•Module 02: Data Structure Introduction: 00:05:00</p></div><div><div><p>•Module 03: Computational Complexity Analysis: 00:13:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Static and Dynamic Arrays: 00:12:00</p></div><div><div><p>•Module 02: Dynamic Arrays Source Code: 00:07:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Singly and Doubly Linked Lists: 00:15:00</p></div><div><div><p>•Module 02: Doubly Linked Lists Source Code: 00:10:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Stack: 00:12:00</p></div><div><div><p>•Module 02: Stack Implementation: 00:04:00</p></div><div><div><p>•Module 03: Stack Source Code: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Queues (Part-1): 00:06:00</p></div><div><div><p>•Module 02: Queues (Part-2): 00:06:00</p></div><div><div><p>•Module 03: Queue Source Code: 00:04:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Priority Queues (PQs) with an interlude on heaps: 00:13:00</p></div><div><div><p>•Module 02: Turning Min PQ into Max PQ: 00:06:00</p></div><div><div><p>•Module 03: Adding Elements to Binary Heap: 00:10:00</p></div><div><div><p>•Module 04: Removing Elements from Binary Heap: 00:14:00</p></div><div><div><p>•Module 05: Priority Queue Binary Heap Source Code: 00:16:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Disjoint Set: 00:06:00</p></div><div><div><p>•Module 02: Kruskal's Algorithm: 00:06:00</p></div><div><div><p>•Module 03: Union and Find Operations: 00:11:00</p></div><div><div><p>•Module 04: Path Compression Union Find: 00:07:00</p></div><div><div><p>•Module 05: Union Find Source Code: 00:08:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Binary Trees and Binary Search Trees (BST): 00:13:00</p></div><div><div><p>•Module 02: Inserting Element into a Binary Search Tree (BST): 00:06:00</p></div><div><div><p>•Module 03: Removing Element from a Binary Search Tree (BST): 00:14:00</p></div><div><div><p>•Module 04: Tree Traversals: 00:12:00</p></div><div><div><p>•Module 05:  Binary Search Source Code: 00:13:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Fenwick Tree Construction: 00:06:00</p></div><div><div><p>•Module 02: Point Updates: 00:06:00</p></div><div><div><p>•Module 03: Binary Indexed Tree: 00:14:00</p></div><div><div><p>•Module 04: Fenwick Tree Source Code: 00:06:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Hash Table: 00:17:00</p></div><div><div><p>•Module 02: Separate Chaining: 00:08:00</p></div><div><div><p>•Module 03: Separate Chaining Source Code: 00:12:00</p></div><div><div><p>•Module 04: Open Addressing: 00:11:00</p></div><div><div><p>•Module 05: Linear Probing: 00:14:00</p></div><div><div><p>•Module 06: Quadratic Probing: 00:09:00</p></div><div><div><p>•Module 07: Double Hashing: 00:15:00</p></div><div><div><p>•Module 08: Removing Element Open Addressing: 00:08:00</p></div><div><div><p>•Module 09: Open Addressing Code: 00:15:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Introduction: 00:03:00</p></div><div><div><p>•Module 02: The Longest Common Prefix (LCP) Array: 00:03:00</p></div><div><div><p>•Module 03:  Using SA/LCP Array to Find Unique Substrings: 00:05:00</p></div><div><div><p>•Module 04: Longest Common Substring (LCS): 00:11:00</p></div><div><div><p>•Module 05: Longest Common Substring (LCS) Full Example: 00:07:00</p></div><div><div><p>•Module 06: Longest Repeated Substring (LRS): 00:05:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Balanced Binary Search Trees (BBSTs): 00:09:00</p></div><div><div><p>•Module 02: Inserting Elements into an AVL Tree: 00:10:00</p></div><div><div><p>•Module 03: Removing an AVL Tree: 00:09:00</p></div><div><div><p>•Module 04: AVL Tree Source Code: 00:17:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Indexed Priority Queue (Part-1): 00:25:00</p></div><div><div><p>•Module 02: Indexed Priority Queue Source Code: 00:09:00</p></div></div></div></div></div><div><div><div><div><div><div><p>•Module 01: Sparse Table: 00:26:00</p></div><div><div><p>•Module 02: Sparse Table Source Code: 00:07:00</p></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div></div>

Overview

This comprehensive course on Data Structures Complete Course will deepen your understanding on this topic.
After successful completion of this course you can acquire the required skills in this sector. This Data Structures Complete Course comes with accredited certification, which will enhance your CV and make you worthy in the job market.
So enrol in this course today to fast track your career ladder.
How will I get my certificate?
You may have to take a quiz or a written test online during or after the course. After successfully completing the course, you will be eligible for the certificate.

Who is This course for?
There is no experience or previous qualifications required for enrolment on this Data Structures Complete Course. It is available to all students, of all academic backgrounds.
Requirements
Our Data Structures Complete Course is fully compatible with PC's, Mac's, Laptop, Tablet and Smartphone devices. This course has been designed to be fully compatible with tablets and smartphones so you can access your course on Wi-Fi, 3G or 4G.
There is no time limit for completing this course, it can be studied in your own time at your own pace.
Career Path
Having these various qualifications will increase the value in your CV and open you up to multiple sectors such as Business & Management, Admin, Accountancy & Finance, Secretarial & PA, Teaching & Mentoring etc.
Course Curriculum
14 sections • 55 lectures • 09:02:00 total length
•Module 01: Promo Video: 00:02:00
•Module 02: Data Structure Introduction: 00:05:00
•Module 03: Computational Complexity Analysis: 00:13:00
•Module 01: Static and Dynamic Arrays: 00:12:00
•Module 02: Dynamic Arrays Source Code: 00:07:00
•Module 01: Singly and Doubly Linked Lists: 00:15:00
•Module 02: Doubly Linked Lists Source Code: 00:10:00
•Module 01: Stack: 00:12:00
•Module 02: Stack Implementation: 00:04:00
•Module 03: Stack Source Code: 00:04:00
•Module 01: Queues (Part-1): 00:06:00
•Module 02: Queues (Part-2): 00:06:00
•Module 03: Queue Source Code: 00:04:00
•Module 01: Priority Queues (PQs) with an interlude on heaps: 00:13:00
•Module 02: Turning Min PQ into Max PQ: 00:06:00
•Module 03: Adding Elements to Binary Heap: 00:10:00
•Module 04: Removing Elements from Binary Heap: 00:14:00
•Module 05: Priority Queue Binary Heap Source Code: 00:16:00
•Module 01: Disjoint Set: 00:06:00
•Module 02: Kruskal's Algorithm: 00:06:00
•Module 03: Union and Find Operations: 00:11:00
•Module 04: Path Compression Union Find: 00:07:00
•Module 05: Union Find Source Code: 00:08:00
•Module 01: Binary Trees and Binary Search Trees (BST): 00:13:00
•Module 02: Inserting Element into a Binary Search Tree (BST): 00:06:00
•Module 03: Removing Element from a Binary Search Tree (BST): 00:14:00
•Module 04: Tree Traversals: 00:12:00
•Module 05: Binary Search Source Code: 00:13:00
•Module 01: Fenwick Tree Construction: 00:06:00
•Module 02: Point Updates: 00:06:00
•Module 03: Binary Indexed Tree: 00:14:00
•Module 04: Fenwick Tree Source Code: 00:06:00
•Module 01: Hash Table: 00:17:00
•Module 02: Separate Chaining: 00:08:00
•Module 03: Separate Chaining Source Code: 00:12:00
•Module 04: Open Addressing: 00:11:00
•Module 05: Linear Probing: 00:14:00
•Module 06: Quadratic Probing: 00:09:00
•Module 07: Double Hashing: 00:15:00
•Module 08: Removing Element Open Addressing: 00:08:00
•Module 09: Open Addressing Code: 00:15:00
•Module 01: Introduction: 00:03:00
•Module 02: The Longest Common Prefix (LCP) Array: 00:03:00
•Module 03: Using SA/LCP Array to Find Unique Substrings: 00:05:00
•Module 04: Longest Common Substring (LCS): 00:11:00
•Module 05: Longest Common Substring (LCS) Full Example: 00:07:00
•Module 06: Longest Repeated Substring (LRS): 00:05:00
•Module 01: Balanced Binary Search Trees (BBSTs): 00:09:00
•Module 02: Inserting Elements into an AVL Tree: 00:10:00
•Module 03: Removing an AVL Tree: 00:09:00
•Module 04: AVL Tree Source Code: 00:17:00
•Module 01: Indexed Priority Queue (Part-1): 00:25:00
•Module 02: Indexed Priority Queue Source Code: 00:09:00
•Module 01: Sparse Table: 00:26:00
•Module 02: Sparse Table Source Code: 00:07:00

Apex Learning

Data Structures Complete Course

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					<div><p>Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Javascript for Data Structures has been specially designed to help learners gain a good command of Javascript for Data Structures, providing them with a solid foundation of knowledge to become a qualified professional.</p><p>Through this Javascript for Data Structures, you will gain both practical and theoretical understanding of Javascript for Data Structures that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.</p><p>Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.</p></div>
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										<span>Know how to use your theoretical knowledge to adapt in any working environment</span>
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										<span>Get help from our expert tutors anytime you need</span>
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										<span>Access to course contents that are designed and prepared by industry professionals</span>
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										<span>Study at your convenient time and from wherever you want</span>
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			<h2> <b>Why should I take this course?</b></h2>		</div>
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										<span>Affordable premium-quality E-learning content, you can learn at your own pace.</span>
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										<span>You will receive a completion certificate upon completing the course.</span>
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										<span>Internationally recognized Accredited Qualification will boost up your resume.</span>
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										<span>You will learn the researched and proven approach adopted by successful people to transform their careers.</span>
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										<span>You will be able to incorporate various techniques successfully and understand your customers better.</span>
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										<span>No formal qualifications required, anyone from any academic background can take this course.</span>
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										<span>Access to a computer or digital device with internet connectivity.</span>
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                                <h2>Course Curriculum</h2>
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					<td colspan="4"><strong> Introduction</strong></td>
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					<td>Welcome to the Course</td><td><span>🕐 00:03:00</span></td>
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					<td colspan="4"><strong>Essential Concepts</strong></td>
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					<td>Essential Concepts</td><td><span>🕐 00:02:00</span></td>
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					<td>Constructor Function</td><td><span>🕐 00:09:00</span></td>
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					<td>Protoype</td><td><span>🕐 00:04:00</span></td>
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					<td>Class44</td><td><span>🕐 00:04:00</span></td>
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					<td>List Data Structure</td><td><span>🕐 00:01:00</span></td>
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					<td>Creating Class For List Data Structure</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Implenting Add Feature</td><td><span>🕐 00:06:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Working On Find And Remove Feature</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating InsertAfter Feature</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Implementing Contains And Clear Feature</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Challenge - Work On Traverse List Features</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Solution - Implementing List Traverse Features</td><td><span>🕐 00:08:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Challenge - Work On DisplayElementNameOnPostion Features</td><td><span>🕐 00:01:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Solution - Implementing DisplayElementNameOnPostion Features</td><td><span>🕐 00:06:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Stack Data Structure</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Stack Data Structure</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Using Object To Create Stack Class</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Implementing Push and Pop Feature</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Working with Peek and Clear Feature</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Challenge - Extend Stack Class Feature</td><td><span>🕐 00:01:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Solution - Extending Stack Class Feature</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Challenge - Convert Stack Class From Object To Array Class</td><td><span>🕐 00:01:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Solution - Converting Stack Class From Object To Array Class</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Queue Data Structure</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Queue Data Structure</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Using Object To Create Queue Class</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Implementing Enqueue Feature</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Implementing Tricky Dequeue Feature</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Working On PeeK Size and Clear Features</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Extending Features With Two Extra Methods</td><td><span>🕐 00:06:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Challenge Convert Queue Class From Object To Array Class</td><td><span>🕐 00:01:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Solution Converting Queue Class From Object To Array Class</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Set Data Structure</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Set Data Structure</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating Set Class</td><td><span>🕐 00:03:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Implementing Add And Remove Feature</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Working On Contains, Show And Size Method</td><td><span>🕐 00:05:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Creating Unique Set</td><td><span>🕐 00:09:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Adding Mutual Members Feature</td><td><span>🕐 00:04:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Challenge Implement Difference Set Feature</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Challenge Implement Difference Set Feature</td><td><span>🕐 00:02:00</span></td>
				</tr>
								<tr>
					<td colspan="4"><strong>Final Thought</strong></td>
				</tr>
								<tr>
					<td>▶</td>
					<td>Final Thought</td><td><span>🕐 00:01:00</span></td>
				</tr>
				</tbody></table></div>

			</div>
		                        </div>

Gain the skills and credentials to kickstart a successful career and learn from the experts with this step-by-step training course. This Javascript for Data Structures has been specially designed to help learners gain a good command of Javascript for Data Structures, providing them with a solid foundation of knowledge to become a qualified professional.
Through this Javascript for Data Structures, you will gain both practical and theoretical understanding of Javascript for Data Structures that will increase your employability in this field, help you stand out from the competition and boost your earning potential in no time.
Not only that, but this training includes up-to-date knowledge and techniques that will ensure you have the most in-demand skills to rise to the top of the industry. This qualification is fully accredited, broken down into several manageable modules, ideal for aspiring professionals.
 Learning outcome
Familiar yourself with the recent development and updates of the relevant industry
Know how to use your theoretical knowledge to adapt in any working environment
Get help from our expert tutors anytime you need
Access to course contents that are designed and prepared by industry professionals
Study at your convenient time and from wherever you want
 Why should I take this course?
Affordable premium-quality E-learning content, you can learn at your own pace.
You will receive a completion certificate upon completing the course.
Internationally recognized Accredited Qualification will boost up your resume.
You will learn the researched and proven approach adopted by successful people to transform their careers.
You will be able to incorporate various techniques successfully and understand your customers better.
 Requirements
No formal qualifications required, anyone from any academic background can take this course.
Access to a computer or digital device with internet connectivity.
Course Curriculum
Introduction			
▶	Welcome to the Course	🕐 00:03:00
Essential Concepts			
▶	Essential Concepts	🕐 00:02:00
▶	Constructor Function	🕐 00:09:00
▶	Protoype	🕐 00:04:00
▶	Class44	🕐 00:04:00
List Data Structure			
▶	List Data Structure	🕐 00:01:00
▶	Creating Class For List Data Structure	🕐 00:03:00
▶	Implenting Add Feature	🕐 00:06:00
▶	Working On Find And Remove Feature	🕐 00:08:00
▶	Creating InsertAfter Feature	🕐 00:05:00
▶	Implementing Contains And Clear Feature	🕐 00:04:00
▶	Challenge - Work On Traverse List Features	🕐 00:03:00
▶	Solution - Implementing List Traverse Features	🕐 00:08:00
▶	Challenge - Work On DisplayElementNameOnPostion Features	🕐 00:01:00
▶	Solution - Implementing DisplayElementNameOnPostion Features	🕐 00:06:00
Stack Data Structure			
▶	Stack Data Structure	🕐 00:03:00
▶	Using Object To Create Stack Class	🕐 00:04:00
▶	Implementing Push and Pop Feature	🕐 00:09:00
▶	Working with Peek and Clear Feature	🕐 00:04:00
▶	Challenge - Extend Stack Class Feature	🕐 00:01:00
▶	Solution - Extending Stack Class Feature	🕐 00:03:00
▶	Challenge - Convert Stack Class From Object To Array Class	🕐 00:01:00
▶	Solution - Converting Stack Class From Object To Array Class	🕐 00:05:00
Queue Data Structure			
▶	Queue Data Structure	🕐 00:03:00
▶	Using Object To Create Queue Class	🕐 00:04:00
▶	Implementing Enqueue Feature	🕐 00:03:00
▶	Implementing Tricky Dequeue Feature	🕐 00:09:00
▶	Working On PeeK Size and Clear Features	🕐 00:03:00
▶	Extending Features With Two Extra Methods	🕐 00:06:00
▶	Challenge Convert Queue Class From Object To Array Class	🕐 00:01:00
▶	Solution Converting Queue Class From Object To Array Class	🕐 00:04:00
Set Data Structure			
▶	Set Data Structure	🕐 00:02:00
▶	Creating Set Class	🕐 00:03:00
▶	Implementing Add And Remove Feature	🕐 00:09:00
▶	Working On Contains, Show And Size Method	🕐 00:05:00
▶	Creating Unique Set	🕐 00:09:00
▶	Adding Mutual Members Feature	🕐 00:04:00
▶	Challenge Implement Difference Set Feature	🕐 00:02:00
▶	Challenge Implement Difference Set Feature	🕐 00:02:00
Final Thought			
▶	Final Thought	🕐 00:01:00

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Salesforce Certified JavaScript Developer

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Javascript for Data Structures: Dive into the world of data structures using JavaScript. Learn to implement arrays, linked lists, stacks, queues, trees, and more. Perfect for aspiring developers.

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