Name: Python Programming for Beginners - Learn in 100 Easy Steps
Price: 26.99 GBP
Availability: InStock

<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


Packt

Coding

Python

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

General Admission

<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

Oracle Business Intelligence

Linux

Supercharge your career as an Oracle DBA! This in-depth performance tuning course empowers you to pinpoint bottlenecks, master essential tools like AWR and ASH, elevate your database's performance, and unlock your potential as a top-tier Oracle DBA.

The Oracle Database Performance Tuning Course

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

PostgreSQL

Database Programming

Build 9 projects to master 2 essential and modern technologies: Python and PostgreSQL

The Complete Python and PostgreSQL Developer Course

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

Deep Learning

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

<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

Redhill

<h3><b>Objective-C programming training course description</b></h3>
      <p> A hands on introduction that will allow you to master
Objective-C and start using it to write powerful native
applications for even the newest Macs and iOS
devices! Using The step-by-step approach, will let you
get comfortable with Objective-C's unique capabilities
and Apple's Xcode 5 development environment. Make
the most of Objective-C objects and messaging. Work
effectively with design patterns, collections, blocks,
foundation classes, threading, Git and a whole lot more.
Every session builds on what you've already learned,
giving a rock-solid foundation for real-world success!</p>
        
				<h5><b>What will you learn</b></h5>
				<ul>
				 
	   <li>Use Xcode 5.</li>
					<li>Declare classes, instance variables, properties,
methods, and actions.</li>
                    <li>Use arrays, dictionaries, and sets.</li>
                    <li>Expand and extend classes with protocols,
delegates, categories, and extensions.</li>
                    <li>Use Apple's powerful classes and frameworks.</li>
                  </ul>
        <h5>
        <b>Objective-C programming training course details</b> </h5>
				<div>
					<div>
						<ul>
							<li><b> Who will benefit:</b> </li>
							
            <p>Developers wanting to learn Objective-C.
							</p>
              
							<li><b>Prerequisites:</b></li>
							
              <p>Software development fundamentals.
							</p>
              
							<li><b>Duration</b></li>
							
              <p>5 days
						</p>
              
							
              
              
						</ul>
					</div>
				</div>
      <h3><b>Objective-C programming training course contents</b></h3>
     <ul>
				<li><span>PART 1: GETTING STARTED WITH OBJECTIVE-C</span> </li><p>
</p><li>The Developer Program:</li><p>
Objective-C, enrolling as an Apple Developer, setting up the development environment, Xcode. Your first project.

</p><li>OO programming with Objective-C: </li><p>
OO projects, Frameworks, classes and instances, encapsulation, accessors, Inheritance.

</p><li>OO features in Objective-C: </li><p>
Messages, methods, working with id, nesting messages, method signatures and parameters. allocating and initializing objects. Using Xcode: Xcode, source code control, git and Xcode, Using a Remote Repository.

</p><li>Compiler Directives: </li><p>
Projects, Compiler Directives, Prefix headers, main.m, .h files.


</p><li><span>PART 2: OBJECTIVE-C BASICS</span> </li><p>
 </p><li>Messaging in a Testbed App: </li><p>
Setting Up the Testbed Apps, Adding a Text Field and Connecting It to Your Code, Sending a Message to the Text Field, Reviewing the Message Syntax.

</p><li>Declaring a Class in an Interface File: </li><p>
Context, Creating an Instance Variable with id, What Happens When Execution Stops, dynamic binding, Creating an Instance Variable for with the Class Name and with a Superclass Name, instance variable visibility.

</p><li>Properties in an Interface File: </li><p>
Interface Variables vs Properties, Declared Properties, Using Attributes. Implementing Properties. @synthesize, @dynamic.

</p><li>Methods in an Interface File: </li><p>
Methods in a Class, class and instance methods, Method declaration, returning complex data structures from Methods.

</p><li>Actions in an Interface File: </li><p>
Actions, Actions in OS X and iOS, disconnecting actions.

</p><li>Routing messages with selectors: </li><p>
Receiver and selector objects in messages, Objective-C Runtime, SEL and @selector (), performSelector, NSInvocation, testing whether an Instance can respond to a selector.

</p><li>Building on the Foundation: </li><p>
The Foundation Framework, Foundation Classes, Foundation Paradigms and Policies; Mutability, class clusters, notifications.

</p><li>Defining a Class in Implementation Files: </li><p>
Projects, dynamic typing, creating a new App, implementing a method, expanding Classses with init Methods.

</p><li>Organizing Data with Collections: </li><p>
Collecting Objects, Property Lists, Runtime, comparing the Collection Classes, Creating a Collection, Objective-C Literal Syntax, Enumerating collections, Testing Membership in a Collection, Accessing an Object in a Collection.

</p><li>Managing Memory and Runtime Objects: </li><p>
Managing objects in memory, managing reference counts manually and with ARC, variable qualifiers, variable autorelease.


			</p><li><span>PART 3: EXPANDING AND EXTENDING CLASSES</span> </li><p>
</p><li>Protocols and Delegates: </li><p>
Subclassing, Protocols, Delegates, Looking Deeper Inside Protocols.

</p><li>Categories and Extensions: </li><p>
Comparing categories and protocols, categories vs subclasses, working with categories, class extensions, informal protocols.

</p><li>Associative References and Fast Enumeration: </li><p>
Objective-C 2.0 Time-Saving Features, Extending Classes by Adding Instance Variables (Sort of), Using Fast Enumeration.

</p><li>Blocks: </li><p>
Revisiting Blocks, Callbacks, Blocks, Exploring Blocks in Cocoa, Cocoa Blocks and Memory.


	</p><li><span>PART 4: BEYOND THE BASICS </span></li><p>
</p><li>Handling Exceptions and Errors: </li><p>
Exception and Error classes: NSException, NSError, Identifying exceptions, throwing exceptions, catching exceptions.

</p><li>Queues and Threading: </li><p>
Getting Started with Concurrency, Introducing Queues, Dispatch Sources, Using Dispatch Queues.

</p><li>Working with the Debugger: </li><p>
Logging Information, Console Logs, NSLog, Smart Breakpoints, enhancing breakpoints with messages.

</p><li>Using Xcode Debug Gauges for Analysis: </li><p>
Debug Gauges, Monitoing CPU and memory utilization, monitoring energy, Using Instruments.


</p><li><span>PART 5: OPTIONAL TOPICS </span></li><p>
</p><li>C Syntax Summary: </li><p>
Data Types, Control Structures.

</p><li>Apps, Packages, and Bundles: </li><p>
Project Bundles, lproj Files, Asset Catalogs, plist Files, Precompiled Header Files (.pch).

</p><li>Archiving and Packaging Apps for Development and Testing: </li><p>
Archiving.</p>
			
      </ul>

Objective-C programming training course description
 A hands on introduction that will allow you to master
Objective-C and start using it to write powerful native
applications for even the newest Macs and iOS
devices! Using The step-by-step approach, will let you
get comfortable with Objective-C's unique capabilities
and Apple's Xcode 5 development environment. Make
the most of Objective-C objects and messaging. Work
effectively with design patterns, collections, blocks,
foundation classes, threading, Git and a whole lot more.
Every session builds on what you've already learned,
giving a rock-solid foundation for real-world success!
What will you learn
Use Xcode 5.
Declare classes, instance variables, properties,
methods, and actions.
Use arrays, dictionaries, and sets.
Expand and extend classes with protocols,
delegates, categories, and extensions.
Use Apple's powerful classes and frameworks.
Objective-C programming training course details
Who will benefit:
Developers wanting to learn Objective-C. 
Prerequisites:
Software development fundamentals. 
Duration
5 days 
Objective-C programming training course contents
PART 1: GETTING STARTED WITH OBJECTIVE-C


The Developer Program:

Objective-C, enrolling as an Apple Developer, setting up the development environment, Xcode. Your first project. 
OO programming with Objective-C:

OO projects, Frameworks, classes and instances, encapsulation, accessors, Inheritance. 
OO features in Objective-C:

Messages, methods, working with id, nesting messages, method signatures and parameters. allocating and initializing objects. Using Xcode: Xcode, source code control, git and Xcode, Using a Remote Repository. 
Compiler Directives:

Projects, Compiler Directives, Prefix headers, main.m, .h files. 
PART 2: OBJECTIVE-C BASICS
 
Messaging in a Testbed App:

Setting Up the Testbed Apps, Adding a Text Field and Connecting It to Your Code, Sending a Message to the Text Field, Reviewing the Message Syntax. 
Declaring a Class in an Interface File:

Context, Creating an Instance Variable with id, What Happens When Execution Stops, dynamic binding, Creating an Instance Variable for with the Class Name and with a Superclass Name, instance variable visibility. 
Properties in an Interface File:

Interface Variables vs Properties, Declared Properties, Using Attributes. Implementing Properties. @synthesize, @dynamic. 
Methods in an Interface File:

Methods in a Class, class and instance methods, Method declaration, returning complex data structures from Methods. 
Actions in an Interface File:

Actions, Actions in OS X and iOS, disconnecting actions. 
Routing messages with selectors:

Receiver and selector objects in messages, Objective-C Runtime, SEL and @selector (), performSelector, NSInvocation, testing whether an Instance can respond to a selector. 
Building on the Foundation:

The Foundation Framework, Foundation Classes, Foundation Paradigms and Policies; Mutability, class clusters, notifications. 
Defining a Class in Implementation Files:

Projects, dynamic typing, creating a new App, implementing a method, expanding Classses with init Methods. 
Organizing Data with Collections:

Collecting Objects, Property Lists, Runtime, comparing the Collection Classes, Creating a Collection, Objective-C Literal Syntax, Enumerating collections, Testing Membership in a Collection, Accessing an Object in a Collection. 
Managing Memory and Runtime Objects:

Managing objects in memory, managing reference counts manually and with ARC, variable qualifiers, variable autorelease. 
PART 3: EXPANDING AND EXTENDING CLASSES


Protocols and Delegates:

Subclassing, Protocols, Delegates, Looking Deeper Inside Protocols. 
Categories and Extensions:

Comparing categories and protocols, categories vs subclasses, working with categories, class extensions, informal protocols. 
Associative References and Fast Enumeration:

Objective-C 2.0 Time-Saving Features, Extending Classes by Adding Instance Variables (Sort of), Using Fast Enumeration. 
Blocks:

Revisiting Blocks, Callbacks, Blocks, Exploring Blocks in Cocoa, Cocoa Blocks and Memory. 
PART 4: BEYOND THE BASICS


Handling Exceptions and Errors:

Exception and Error classes: NSException, NSError, Identifying exceptions, throwing exceptions, catching exceptions. 
Queues and Threading:

Getting Started with Concurrency, Introducing Queues, Dispatch Sources, Using Dispatch Queues. 
Working with the Debugger:

Logging Information, Console Logs, NSLog, Smart Breakpoints, enhancing breakpoints with messages. 
Using Xcode Debug Gauges for Analysis:

Debug Gauges, Monitoing CPU and memory utilization, monitoring energy, Using Instruments. 
PART 5: OPTIONAL TOPICS


C Syntax Summary:

Data Types, Control Structures. 
Apps, Packages, and Bundles:

Project Bundles, lproj Files, Asset Catalogs, plist Files, Precompiled Header Files (.pch). 
Archiving and Packaging Apps for Development and Testing:

Archiving.

Systems & Network Training

Network & Security

Network Engineer

Objective-C programming

<p>Turn your skills into a competitive advantage recognised globally and endorsed for quality by QLS, the UK's most reputable high-quality course auditor.</p><p>Master your data structures skills essential for efficient algorithms and good software design. Learn about data structures, storage, management, and organisation of data and information for adequate, simple data access and modification, including queues, heaps, stacks, hash tables, arrays, algorithms, binary trees, and more. Enrol now to become an exceptional programmer or a skilled professional in one of the in-demand essential fields of data science.</p><p>This premium QLS-endorsed Easy to Advanced Data Structures course has intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunities and a globally recognised certification. Thus, completing our Easy to Advanced Data Structures course will give you limitless possibilities anywhere and everywhere since the value of QLS-endorsed methods is relatively high!!</p><p>What's more, the quality of our first-class course is uncompromising for both you and us. You will be taught the richest materials from this exquisite Easy to Advanced Data Structures course, enabling you to open up a vast world of opportunities to grasp and gain success. For the price point, this course is an absolute steal! It's an investment you do not want to miss out on!</p><p>So, why delay any longer? Benefit your career progression by joining us today and distinguish yourself as unquestionably the most skilled in your workplace!</p><p><strong>Why Learn with us: </strong></p><p>As one of the leading e-learning service providers globally, we strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.</p><p><strong>Our Service for the </strong><strong>Easy to Advanced Data Structures</strong><strong> Includes:</strong></p><ul><li>Intensive Study Notes</li><li>Tutor Support</li><li>Customer Support</li><li>Assignment Help</li><li>QLS Endorsed Certificate</li><li>24/7 Learning Portal Access</li><li>Widely Compatible Study Materials</li></ul><p><strong>Course Curriculum of </strong><strong>Easy to Advanced Data Structures</strong></p><ul><li><strong>Unit 01:</strong> Introduction</li><li><strong>Unit 02:</strong> Arrays</li><li><strong>Unit 03:</strong> Linked List</li><li><strong>Unit 04:</strong> Stack</li><li><strong>Unit 05:</strong> Queues</li><li><strong>Unit 06:</strong> Priority Queues (PQs)</li><li><strong>Unit 07:</strong> Union Find</li><li><strong>Unit 08:</strong> Binary Search Trees</li><li><strong>Unit 09:</strong> Fenwick Tree</li><li><strong>Unit 10:</strong> Hash Tables</li><li><strong>Unit 11:</strong> Suffix Array</li><li><strong>Unit 12:</strong> AVL Trees</li><li><strong>Unit 13:</strong> Indexed Priority Queue</li><li><strong>Unit 14:</strong> Sparse Tables</li></ul><p><strong>Assessment &amp; Certification</strong></p><p>Learners must submit a comprehensive summarised assignment on all the units that our expert tutor will assess if all the learning outcomes are met as per the standard set by QLS. After the quality check, the learner will receive the Easy to Advanced Data Structures certificate.</p><p><strong>Endorsed Certificate of Achievement from the Quality Licence Scheme.</strong></p><p>An endorsed certificate will be issued to learners at the course's end to recognise course completion. Provided that the learner completes all course assessments, can claim a certificate.</p><p><strong>Endorsement:</strong></p><p>QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions, including your progression routes into further higher education.</p><h2>
        
        <div>CPD</div>
    </h2>

                        <div id="cpd">
                            <div>
                                <div>
                                    <div>
                                        10 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>Our course is open to everyone. This Easy to Advanced Data Structures is ideal for those who aspire to gain in-depth knowledge &amp; efficient skills to embark into the relevant sector as a promising potential.</p>
                        </div><h2>
        
        <div>Requirements</div>
    </h2>

                        <div id="requirements">
                            <p>No formal requirements are needed for this Easy to Advanced Data Structures course. So, learners do not require prior qualifications to enrol in this course.</p>
                        </div>

Turn your skills into a competitive advantage recognised globally and endorsed for quality by QLS, the UK's most reputable high-quality course auditor.
Master your data structures skills essential for efficient algorithms and good software design. Learn about data structures, storage, management, and organisation of data and information for adequate, simple data access and modification, including queues, heaps, stacks, hash tables, arrays, algorithms, binary trees, and more. Enrol now to become an exceptional programmer or a skilled professional in one of the in-demand essential fields of data science.
This premium QLS-endorsed Easy to Advanced Data Structures course has intensive study modules, dedicated tutor assistance, customer support, flexible learning opportunities and a globally recognised certification. Thus, completing our Easy to Advanced Data Structures course will give you limitless possibilities anywhere and everywhere since the value of QLS-endorsed methods is relatively high!!
What's more, the quality of our first-class course is uncompromising for both you and us. You will be taught the richest materials from this exquisite Easy to Advanced Data Structures course, enabling you to open up a vast world of opportunities to grasp and gain success. For the price point, this course is an absolute steal! It's an investment you do not want to miss out on!
So, why delay any longer? Benefit your career progression by joining us today and distinguish yourself as unquestionably the most skilled in your workplace!
Why Learn with us: 
As one of the leading e-learning service providers globally, we strive to provide all our learners with the best e-learning experience possible that can make a real difference in their career progression.
Our Service for the Easy to Advanced Data Structures Includes:
Intensive Study Notes
Tutor Support
Customer Support
Assignment Help
QLS Endorsed Certificate
24/7 Learning Portal Access
Widely Compatible Study Materials
Course Curriculum of Easy to Advanced Data Structures
Unit 01: Introduction
Unit 02: Arrays
Unit 03: Linked List
Unit 04: Stack
Unit 05: Queues
Unit 06: Priority Queues (PQs)
Unit 07: Union Find
Unit 08: Binary Search Trees
Unit 09: Fenwick Tree
Unit 10: Hash Tables
Unit 11: Suffix Array
Unit 12: AVL Trees
Unit 13: Indexed Priority Queue
Unit 14: Sparse Tables
Assessment & Certification
Learners must submit a comprehensive summarised assignment on all the units that our expert tutor will assess if all the learning outcomes are met as per the standard set by QLS. After the quality check, the learner will receive the Easy to Advanced Data Structures certificate.
Endorsed Certificate of Achievement from the Quality Licence Scheme.
An endorsed certificate will be issued to learners at the course's end to recognise course completion. Provided that the learner completes all course assessments, can claim a certificate.
Endorsement:
QLS endorsed this certified course as a highly qualified, non-regulated provision and training programme. There will be a trainer to answer all your questions, including your progression routes into further higher education.
 CPD 
10 CPD hours / points  Accredited by CPD Quality Standards
 Who is this course for? 
Our course is open to everyone. This Easy to Advanced Data Structures is ideal for those who aspire to gain in-depth knowledge & efficient skills to embark into the relevant sector as a promising potential.
 Requirements 
No formal requirements are needed for this Easy to Advanced Data Structures course. So, learners do not require prior qualifications to enrol in this course.

Kingston Open College

Learning & Development

NVQ Assessor

ELS (Effective Learning Service)

Data Structures & Algorithms (DSA)

European Computer Driving Licence (ECDL)

QLS Endorsed + CPD QS Accredited - Dual Certification | Instant Access | 24/7 Tutor Support | All-Inclusive Cost

Level 6 Diploma in Easy to Advanced Data Structures - QLS Endorsed

<h3><b>OOAD training course description</b></h3>
      <p>A workshop course providing thorough practical knowledge of object oriented analysis and design methods.</p>
        
				<h5><b>What will you learn</b></h5>
				<ul>
				 
		<li>Perform Systems Analysis with Object Oriented methods.</li>
					<li>Identify key classes and objects.</li>
                    <li>Expand and refine OO problem domain models.</li>
                    <li>Design Class hierarchies using inheritance and polymorphism.</li>
                    <li>Design programs with Object Oriented methods. </li>
                  </ul>
        <h5>
        <b>OOAD training course details</b> </h5>
				<div>
					<div>
						<ul>
							<li><b> Who will benefit:</b> </li>
							
              <p>System analysts, designers, programmers and project managers.</p>
              
							<li><b>Prerequisites:</b></li>
							
              <p>It is desirable that delegates have experience of programming in C++/Java or some other OOP language.
							</p>
              
							<li><b>Duration</b></li>
							
              <p>5 days
						</p>
              
							
              
              
						</ul>
					</div>
				</div>
      <h3><b>OOAD training course contents</b></h3>
      <ul>
				<li>What is OO? </li><p>
Classes, objects, messages, encapsulation, associations, inheritance, polymorphism, reusability.

</p><li>What is Systems Analysis and design? </li><p>
Data flow diagrams, structure diagrams. The OO approach.

</p><li>OOA </li><p>
The problem domain and object modelling. Identifying classes and objects. Generalisation and inheritance. Defining attributes and methods.

</p><li>OOD </li><p>
Refining the OOA results. Designing the User Interface. Designing the algorithms and data structures using objects. Designing the methods.

</p><li>OOP </li><p>
				Prototyping. Implementing OOD with OOPs and OOPLs.</p>
			
      </ul>

OOAD training course description
A workshop course providing thorough practical knowledge of object oriented analysis and design methods.
What will you learn
Perform Systems Analysis with Object Oriented methods.
Identify key classes and objects.
Expand and refine OO problem domain models.
Design Class hierarchies using inheritance and polymorphism.
Design programs with Object Oriented methods.
OOAD training course details
Who will benefit:
System analysts, designers, programmers and project managers.
Prerequisites:
It is desirable that delegates have experience of programming in C++/Java or some other OOP language. 
Duration
5 days 
OOAD training course contents
What is OO?

Classes, objects, messages, encapsulation, associations, inheritance, polymorphism, reusability. 
What is Systems Analysis and design?

Data flow diagrams, structure diagrams. The OO approach. 
OOA

The problem domain and object modelling. Identifying classes and objects. Generalisation and inheritance. Defining attributes and methods. 
OOD

Refining the OOA results. Designing the User Interface. Designing the algorithms and data structures using objects. Designing the methods. 
OOP
 Prototyping. Implementing OOD with OOPs and OOPLs.

Object Oriented Analysis & Design

<h3><b>Java training course description</b></h3>
      <p>A comprehensive introduction of the Java language and
environment. It is important to note that the course will
assume that the delegates are already familiar with the
C language as this enables more advanced features of
the Java language to be covered in the course. The
course will also give an overview of areas related to
programming in Java.</p>
        
				<h5><b>What will you learn</b></h5>
				<ul>
				 
		 <li>Describe the Java architecture.</li>
																					<li>Write Java applets and applications.</li>
                    <li>Debug Java programs.</li>
                    <li>Examine existing code and determine its function.</li>
                    <li>Use multimedia extensions, the awt, multithreading, exceptions within Java </li>
                  </ul>
        <h5>
        <b>Java training course details</b> </h5>
				<div>
					<div>
						<ul>
							<li><b> Who will benefit:</b> </li>
							
              <p>Those wishing to program in Java.</p>
              
							<li><b>Prerequisites:</b></li>
							
              <p>Complete C programming
							</p>
              
							<li><b>Duration</b></li>
							
              <p>5 days
						</p>
              
							
              
              
						</ul>
					</div>
				</div>
      <h3><b>Java training course contents</b></h3>
      <ul>
				<li>Review of UNIX fundamentals </li><p>

</p><li>What is Java? </li><p>
What Java is, history of Java, reasons for success. The Java Virtual Machine, Bytecodes, getting up and running with Java, Java resources. Simple Java applications.

</p><li>C features in Java </li><p>
Java data structures, Java flow control, differences from C, arrays, strings and packages.

</p><li>OO features in Java </li><p>
Java classes and objects, inheritance, overloading, packages. Differences from C++.

</p><li>Java applets </li><p>
Applications vs. applets, HTML, the applet tag, applet methods, life cycle, testing and debugging.

</p><li>Multimedia applets </li><p>
Images, sounds, fonts, colours and animation.

</p><li>Java products </li><p>
The JDK in detail, other development environments. Javabeans and JDBC overviews.

</p><li>Abstract Window Toolkit </li><p>
JFC and Swing versus AWT. Event handling (JDK 1.1), GUIs, panels, buttons, lists, scrollbars, text areas, framesâ¦

</p><li>Exception handling and multithreading </li><p>
Handling exceptions. Starting, pausing, stopping threads, producers, consumers, monitoring.

</p><li>More standard classes </li><p>
Java file I/O, Streams, The system class. The networking model, java.net classes.

</p><li>Security and Java </li><p>
Types of attack, the security manager, craplets, securing the network.

				</p><li>Integrating legacy code with Java</li>
			
      </ul>

Java training course description
A comprehensive introduction of the Java language and
environment. It is important to note that the course will
assume that the delegates are already familiar with the
C language as this enables more advanced features of
the Java language to be covered in the course. The
course will also give an overview of areas related to
programming in Java.
What will you learn
Describe the Java architecture.
Write Java applets and applications.
Debug Java programs.
Examine existing code and determine its function.
Use multimedia extensions, the awt, multithreading, exceptions within Java
Java training course details
Who will benefit:
Those wishing to program in Java.
Prerequisites:
Complete C programming 
Duration
5 days 
Java training course contents
Review of UNIX fundamentals
 
What is Java?

What Java is, history of Java, reasons for success. The Java Virtual Machine, Bytecodes, getting up and running with Java, Java resources. Simple Java applications. 
C features in Java

Java data structures, Java flow control, differences from C, arrays, strings and packages. 
OO features in Java

Java classes and objects, inheritance, overloading, packages. Differences from C++. 
Java applets

Applications vs. applets, HTML, the applet tag, applet methods, life cycle, testing and debugging. 
Multimedia applets

Images, sounds, fonts, colours and animation. 
Java products

The JDK in detail, other development environments. Javabeans and JDBC overviews. 
Abstract Window Toolkit

JFC and Swing versus AWT. Event handling (JDK 1.1), GUIs, panels, buttons, lists, scrollbars, text areas, framesâ¦ 
Exception handling and multithreading

Handling exceptions. Starting, pausing, stopping threads, producers, consumers, monitoring. 
More standard classes

Java file I/O, Streams, The system class. The networking model, java.net classes. 
Security and Java

Types of attack, the security manager, craplets, securing the network. 
Integrating legacy code with Java

Introduction to Java programming

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>This course is intended for<p>This course is intended for anyone who is new to software development and wants, or needs, to gain an understanding of programming fundamentals and object-oriented programming concepts. They will typically be high school students, post-secondary school students, or career changers, with no prior programming experience. They might want to gain an understanding of the core programming fundamentals before moving on to more advanced courses such as Programming in C#.</p>Overview<p>Explain core programming fundamentals such as computer storage and processing. Explain computer number systems such as binary. Create and use variables and constants in programs. Explain how to create and use functions in a program. Create and use decisions structures in a computer program. Create and use repetition (loops) in a computer program. Explain pseudocode and its role in programming. Explain the basic computer data structures such as arrays, lists, stacks, and queues. Implement object-oriented programming concepts. Create and use classes in a computer program. Implement encapsulation, inheritance, and polymorphism. Describe the base class library (BCL) in the .NET Framework. Explain the application security concepts. Implement simple I/O in a computer program. Identify application errors and explain how to debug an application and handle errors. Identify the performance considerations for applications.</p><p>In this 5-day course, students will learn the basics of computer programming through the use of Microsoft Visual Studio 2022 and the Visual C# and Visual Basic programming languages. The course assumes no prior programming experience and introduces the concepts needed to progress to the intermediate courses on programming, Programming in C#.<br /><br /> The focus will be on core programming concepts such as computer storage, data types, decision structures, and repetition by using loops. The course also covers an introduction to object-oriented programming covering classes, encapsulation, inheritance, and polymorphism. Coverage is also included around exception handling, application security, performance, and memory management.</p></div><div id="course-pills-2"><h3>1 - Introduction to Core Programming Concepts</h3> <ul> <li>Computer Data Storage and Processing</li> <li>Application Types</li> <li>Application Lifecycle</li> <li>Code Compilation</li> </ul> <h3>2 - Core Programming Language Concepts</h3> <ul> <li>Syntax</li> <li>Data Types</li> <li>Variables and Constants</li> </ul> <h3>3 - Program Flow</h3> <ul> <li>Introduction to Structured Programming Concepts</li> <li>Introduction to Branching</li> <li>Using Functions</li> <li>Using Decision Structures</li> <li>Introducing Repetition</li> </ul> <h3>4 - Algorithms and Data Structures</h3> <ul> <li>Understand How to Write Pseudocode</li> <li>Algorithm Examples</li> <li>Introduction to Data Structures</li> </ul> <h3>5 - Error Handling and Debugging</h3> <ul> <li>Introduction to Program Errors</li> <li>Introduction to Structured Error Handling</li> <li>Introduction to Debugging</li> </ul> <h3>6 - Introduction to Object-Oriented Programming</h3> <ul> <li>Introduction to Complex Structures</li> <li>Introduction to Structs</li> <li>Introduction to Classes</li> <li>Introducing Encapsulation</li> </ul> <h3>7 - More Object-Oriented Programming</h3> <ul> <li>Introduction to Inheritance</li> <li>Introduction to Polymorphism</li> <li>Introduction to .NET and the Base Class Library</li> </ul> <h3>8 - Introduction to Application Security</h3> <ul> <li>Authentication and Authorization</li> <li>Code Permissions on Computers</li> <li>Introducing Code Signing</li> </ul> <h3>9 - Core I/O Programming</h3> <ul> <li>Using Console I/O</li> <li>Using File I/O</li> </ul> <h3>10 - Application Performance and Memory Management</h3> <ul> <li>Value Types vs Reference Types</li> <li>Converting Types</li> <li>The Garbage Collector</li> </ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans 55337 Introduction to Programming training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the 55337 Introduction to Programming course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
5 Days
30 CPD hours
This course is intended for
This course is intended for anyone who is new to software development and wants, or needs, to gain an understanding of programming fundamentals and object-oriented programming concepts. They will typically be high school students, post-secondary school students, or career changers, with no prior programming experience. They might want to gain an understanding of the core programming fundamentals before moving on to more advanced courses such as Programming in C#.
Overview
Explain core programming fundamentals such as computer storage and processing. Explain computer number systems such as binary. Create and use variables and constants in programs. Explain how to create and use functions in a program. Create and use decisions structures in a computer program. Create and use repetition (loops) in a computer program. Explain pseudocode and its role in programming. Explain the basic computer data structures such as arrays, lists, stacks, and queues. Implement object-oriented programming concepts. Create and use classes in a computer program. Implement encapsulation, inheritance, and polymorphism. Describe the base class library (BCL) in the .NET Framework. Explain the application security concepts. Implement simple I/O in a computer program. Identify application errors and explain how to debug an application and handle errors. Identify the performance considerations for applications.
In this 5-day course, students will learn the basics of computer programming through the use of Microsoft Visual Studio 2022 and the Visual C# and Visual Basic programming languages. The course assumes no prior programming experience and introduces the concepts needed to progress to the intermediate courses on programming, Programming in C#.

 The focus will be on core programming concepts such as computer storage, data types, decision structures, and repetition by using loops. The course also covers an introduction to object-oriented programming covering classes, encapsulation, inheritance, and polymorphism. Coverage is also included around exception handling, application security, performance, and memory management.
1 - Introduction to Core Programming Concepts
Computer Data Storage and Processing
Application Types
Application Lifecycle
Code Compilation
2 - Core Programming Language Concepts
Syntax
Data Types
Variables and Constants
3 - Program Flow
Introduction to Structured Programming Concepts
Introduction to Branching
Using Functions
Using Decision Structures
Introducing Repetition
4 - Algorithms and Data Structures
Understand How to Write Pseudocode
Algorithm Examples
Introduction to Data Structures
5 - Error Handling and Debugging
Introduction to Program Errors
Introduction to Structured Error Handling
Introduction to Debugging
6 - Introduction to Object-Oriented Programming
Introduction to Complex Structures
Introduction to Structs
Introduction to Classes
Introducing Encapsulation
7 - More Object-Oriented Programming
Introduction to Inheritance
Introduction to Polymorphism
Introduction to .NET and the Base Class Library
8 - Introduction to Application Security
Authentication and Authorization
Code Permissions on Computers
Introducing Code Signing
9 - Core I/O Programming
Using Console I/O
Using File I/O
10 - Application Performance and Memory Management
Value Types vs Reference Types
Converting Types
The Garbage Collector
Additional course details:
Nexus Humans 55337 Introduction to Programming training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the 55337 Introduction to Programming course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


Nexus Human

Online Course Creation

Computing Skills

Programming Fundamentals

Computer Programming

Core Skills Training Framework (CSTF)

Trainers Course

55337 Introduction to Programming

<h3><b>CWISA training course description</b></h3>
        <p>
    This CWISA course covers wireless technologies with
reference to IoT. It examines from an IoT perspective
how wireless works, and is an excellent introduction to
IoT for the wireless engineer. Topics range from wireless
technologies, RF, to mobile networks, IoT, and security.</p>
        
				<h5><b>What will you learn</b></h5>
				<ul>
				 
      <li>Describe wireless networking and IoT technologies.</li>
					<li>Explain basic RF communications.</li>
					<li>Plan wireless solutions.</li>
					<li>Describe how to implement wireless solutions.</li>
					<li>Use best practices in implementing wireless
solutions.</li>
                  </ul>
                        
        <h5>
        <b>CWISA training course details</b> </h5>
				<div>
					<div>
						<ul>
							<li><b> Who will benefit:</b> </li>
							
              <p>Anyone working with IoT technologies.</p>
              
							<li><b>Prerequisites:</b></li>
							
              <p>RF fundamentals.</p>
              
							<li><b>Duration</b></li>
							
              <p>4 days</p>
              
							
              
              
						</ul>
					</div>
				</div>
      <h3><b>CWISA training course contents</b></h3>
							
	<ul>				
<li>Introduction to wireless technologies</li><p>
History of wireless, radio waves and frequencies,
wireless technologies and related components,
common components of wireless solutions, LAN
networking requirements, Network security,
Implementing wireless solutions, staging,
documentation, security updates, Industry
organizations, IEEE, compatibility and certification
groups.
</p><li>Wireless network use cases</li><p>
Wireless BANs, Wireless PANs, Wireless LANs,
Wireless MANs, Wireless WANs, Wireless sensor
networks, New network driver-Internet of Things,
IoT for industry (IIoT), IoT for connected vehicles,
Residential environments, Retail, Education (K12), Higher education, Agriculture, Smart cities /
Public access, Health care, Office buildings,
Hospitality, Industry, Stadiums, arenas, and large
public venues.
</p><li>Planning wireless solutions</li><p>
Identifying use cases and applications, common
wireless requirements and constraints, performing
a wireless system design, selecting and evaluating
design parameters.
</p><li>RF communications</li><p>
RF wave characteristics, RF propagation
behaviours, RF signal metrics, fundamentals of
wireless modulation. other wireless carriers,
common frequency bands.
</p><li>Radio frequency hardware</li><p>
Hardware levels, basic RF hardware components
(circuit board level), RF link types (use category).
RF device types.
</p><li>Mobile communications networks</li><p>
Mobile networks, LTE, 5G, Use cases.
</p><li>Short-range, low-rate, and low-power networks</li><p>
RF and speed, RF and range, RF and power,
802.11, 802.15.4, Bluetooth, LoRa (Long range) /
LoRaWAN, ZigBee, 6LoWPAN, NB-IoT and
LTE-M.
</p><li>Wireless sensor networks</li><p>
What is a Wireless Sensor Network (WSN)? WSN
applications, Sensors and actuators, WSN
architectures, Planning a WSN.
</p><li>Internet of Things (IoT)</li><p>
Internet of Things (IoT) defined, IoT history and its
definition revisited, one more comment on the
definition of IoT, IoT verticals, Oil &amp; Gas, IoT
structure/ architecture basics, IoT connected
objects.
</p><li>Securing wireless networks</li><p>
Confidentiality, integrity and availability, Privacy,
non-repudiation, authenticity &amp; safety, Importance
of authentication in wireless networks, Key
cryptographic technologies &amp; concepts,
Authentication methods, Authorisation, OAuth 2.0
authorisation framework, monitoring.
</p><li>Troubleshooting wireless solutions</li><p>
Proper solutions design, designing and
implementing wireless solutions, basic installation
procedures, general configuration considerations,
troubleshooting and remediation, troubleshoot
common problems in wireless solutions.
</p><li>Programming, scripting and automation</li><p>
What is an API? categories of APIs, common API
communication methods, choosing a language,
why are we integrating systems? Application &amp;
integration architectures. Data structures &amp; types,
XML, YAML, API types.
</p>
				</ul>

CWISA training course description
 This CWISA course covers wireless technologies with
reference to IoT. It examines from an IoT perspective
how wireless works, and is an excellent introduction to
IoT for the wireless engineer. Topics range from wireless
technologies, RF, to mobile networks, IoT, and security.
What will you learn
Describe wireless networking and IoT technologies.
Explain basic RF communications.
Plan wireless solutions.
Describe how to implement wireless solutions.
Use best practices in implementing wireless
solutions.
CWISA training course details
Who will benefit:
Anyone working with IoT technologies.
Prerequisites:
RF fundamentals.
Duration
4 days
CWISA training course contents
Introduction to wireless technologies

History of wireless, radio waves and frequencies,
wireless technologies and related components,
common components of wireless solutions, LAN
networking requirements, Network security,
Implementing wireless solutions, staging,
documentation, security updates, Industry
organizations, IEEE, compatibility and certification
groups.

Wireless network use cases

Wireless BANs, Wireless PANs, Wireless LANs,
Wireless MANs, Wireless WANs, Wireless sensor
networks, New network driver-Internet of Things,
IoT for industry (IIoT), IoT for connected vehicles,
Residential environments, Retail, Education (K12), Higher education, Agriculture, Smart cities /
Public access, Health care, Office buildings,
Hospitality, Industry, Stadiums, arenas, and large
public venues.

Planning wireless solutions

Identifying use cases and applications, common
wireless requirements and constraints, performing
a wireless system design, selecting and evaluating
design parameters.

RF communications

RF wave characteristics, RF propagation
behaviours, RF signal metrics, fundamentals of
wireless modulation. other wireless carriers,
common frequency bands.

Radio frequency hardware

Hardware levels, basic RF hardware components
(circuit board level), RF link types (use category).
RF device types.

Mobile communications networks

Mobile networks, LTE, 5G, Use cases.

Short-range, low-rate, and low-power networks

RF and speed, RF and range, RF and power,
802.11, 802.15.4, Bluetooth, LoRa (Long range) /
LoRaWAN, ZigBee, 6LoWPAN, NB-IoT and
LTE-M.

Wireless sensor networks

What is a Wireless Sensor Network (WSN)? WSN
applications, Sensors and actuators, WSN
architectures, Planning a WSN.

Internet of Things (IoT)

Internet of Things (IoT) defined, IoT history and its
definition revisited, one more comment on the
definition of IoT, IoT verticals, Oil & Gas, IoT
structure/ architecture basics, IoT connected
objects.

Securing wireless networks

Confidentiality, integrity and availability, Privacy,
non-repudiation, authenticity & safety, Importance
of authentication in wireless networks, Key
cryptographic technologies & concepts,
Authentication methods, Authorisation, OAuth 2.0
authorisation framework, monitoring.

Troubleshooting wireless solutions

Proper solutions design, designing and
implementing wireless solutions, basic installation
procedures, general configuration considerations,
troubleshooting and remediation, troubleshoot
common problems in wireless solutions.

Programming, scripting and automation

What is an API? categories of APIs, common API
communication methods, choosing a language,
why are we integrating systems? Application &
integration architectures. Data structures & types,
XML, YAML, API types.


CWISA Wireless IoT Solutions Administrator

<h3><b>Software development training course description</b></h3>
      <p>This three-day MTA Training course helps you prepare
for Microsoft Technology Associate Exam 98-361, and
build an understanding of these topics: Core
programming, Object-Oriented programming, general
software development, web applications, desktop
applications, and databases. This course leverages the
same content as found in the Microsoft Official
Academic Course (MOAC) for this exam.</p>
        
				<h5><b>What will you learn</b></h5>
				<ul>
				 
	    <li>Describe core programming.</li>
																							<li>Explain Object Oriented programming.</li>
                    <li>Describe general software development.</li>
                    <li>Describe Web applications.</li>
                    <li>Describe desktop applications.</li>
                    <li>Explain how databases work.</li>
                  </ul>
        <h5>
        <b>Software development training course details</b> </h5>
				<div>
					<div>
						<ul>
							<li><b> Who will benefit:</b> </li>
							
            <p>Anyone looking to learn the fundamentals of software.
							</p>
              
							<li><b>Prerequisites:</b></li>
							
              <p> None.
							</p>
              
							<li><b>Duration</b></li>
							
              <p>3 days
						</p>
              
							
              
              
						</ul>
					</div>
				</div>
      <h3><b>Software development training course contents</b></h3>
      <ul>
				<li><span>Core programming</span> </li><p>
 </p><li>Computer storage and data types </li><p>
How a computer stores programs and the instructions in computer memory, memory stacks and heaps, memory size requirements for the various data storage types, numeric data and textual data.

</p><li>Computer decision structures </li><p>
Various decision structures used in all computer programming languages; If decision structures; multiple decision structures, such as Ifâ¦Else and switch/Select Case; reading flowcharts; decision tables; evaluating expressions.

</p><li>Handling repetition </li><p>
For loops, While loops, Do...While loops and recursion.

</p><li>Understand error handling </li><p>
Structured exception handling.


	</p><li><span>Object-oriented programming</span> </li><p>
</p><li>Classes </li><p>
Properties, methods, events and constructors; how to create a class; how to use classes in code.

</p><li>Inheritance </li><p>
Inheriting the functionality of a base class into a derived class.

</p><li>Polymorphism </li><p>
Extending the functionality in a class after inheriting from a base class, overriding methods in the derived class.

</p><li>Encapsulation </li><p>
Creating classes that hide their implementation details while still allowing access to the required functionality through the interface, access modifiers.


	</p><li><span>General software development </span></li><p>
</p><li>Application life cycle management </li><p>
Phases of application life cycle management, software testing.

</p><li>Interpret application specifications </li><p>
Application specifications, translating them into prototypes, code, select appropriate application type and components.

</p><li>Algorithms and data structures </li><p>
Arrays, stacks, queues, linked lists and sorting algorithms; performance implications of various data structures; choosing the right data structure.


	</p><li><span>Web applications </span> </li><p>
</p><li>Web page development </li><p>
HTML, CSS, JavaScript.

</p><li>ASP.NET web application development </li><p>
Page life cycle, event model, state management, client-side versus server-side programming.

</p><li>Web hosting </li><p>
Creating virtual directories and websites, deploying web applications, understanding the role of Internet Information Services.

</p><li>Web services </li><p>
Web services that will be consumed by client applications, accessing web services from a client application, SOAP, WSDL.


	</p><li><span>Desktop applications</span> </li><p>
</p><li>Windows apps </li><p>
UI design guideline categories, characteristics and capabilities of Store Apps, identify gestures.

</p><li>Console-based applications </li><p>
Characteristics and capabilities of console- based applications.

</p><li>Windows Services </li><p>
Characteristics and capabilities of Windows Services.


</p><li><span>		Databases</span> </li><p>
</p><li>Relational database management systems </li><p>
Characteristics and capabilities of database products, database design, ERDs, normalisation concepts.

</p><li>Database query methods </li><p>
SQL, creating and accessing stored procedures, updating and selecting data.

</p><li>Database connection methods </li><p>
Connecting to various types of data stores, such as flat file; XML file; in-memory object; resource optimisation.</p>
			
      </ul>

Software development training course description
This three-day MTA Training course helps you prepare
for Microsoft Technology Associate Exam 98-361, and
build an understanding of these topics: Core
programming, Object-Oriented programming, general
software development, web applications, desktop
applications, and databases. This course leverages the
same content as found in the Microsoft Official
Academic Course (MOAC) for this exam.
What will you learn
Describe core programming.
Explain Object Oriented programming.
Describe general software development.
Describe Web applications.
Describe desktop applications.
Explain how databases work.
Software development training course details
Who will benefit:
Anyone looking to learn the fundamentals of software. 
Prerequisites:
 None. 
Duration
3 days 
Software development training course contents
Core programming
 
Computer storage and data types

How a computer stores programs and the instructions in computer memory, memory stacks and heaps, memory size requirements for the various data storage types, numeric data and textual data. 
Computer decision structures

Various decision structures used in all computer programming languages; If decision structures; multiple decision structures, such as Ifâ¦Else and switch/Select Case; reading flowcharts; decision tables; evaluating expressions. 
Handling repetition

For loops, While loops, Do...While loops and recursion. 
Understand error handling

Structured exception handling. 
Object-oriented programming


Classes

Properties, methods, events and constructors; how to create a class; how to use classes in code. 
Inheritance

Inheriting the functionality of a base class into a derived class. 
Polymorphism

Extending the functionality in a class after inheriting from a base class, overriding methods in the derived class. 
Encapsulation

Creating classes that hide their implementation details while still allowing access to the required functionality through the interface, access modifiers. 
General software development


Application life cycle management

Phases of application life cycle management, software testing. 
Interpret application specifications

Application specifications, translating them into prototypes, code, select appropriate application type and components. 
Algorithms and data structures

Arrays, stacks, queues, linked lists and sorting algorithms; performance implications of various data structures; choosing the right data structure. 
Web applications


Web page development

HTML, CSS, JavaScript. 
ASP.NET web application development

Page life cycle, event model, state management, client-side versus server-side programming. 
Web hosting

Creating virtual directories and websites, deploying web applications, understanding the role of Internet Information Services. 
Web services

Web services that will be consumed by client applications, accessing web services from a client application, SOAP, WSDL. 
Desktop applications


Windows apps

UI design guideline categories, characteristics and capabilities of Store Apps, identify gestures. 
Console-based applications

Characteristics and capabilities of console- based applications. 
Windows Services

Characteristics and capabilities of Windows Services. 
Databases


Relational database management systems

Characteristics and capabilities of database products, database design, ERDs, normalisation concepts. 
Database query methods

SQL, creating and accessing stored procedures, updating and selecting data. 
Database connection methods

Connecting to various types of data stores, such as flat file; XML file; in-memory object; resource optimisation.

Software development fundamentals

<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

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

Data Structure

Algorithms

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

<div id="course-pills-1">Duration<p>5 Days</p><p>30 CPD hours</p>Overview<p>Mining data Manipulating data Visualizing and reporting data Applying basic statistical methods Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle</p><p>CompTIA Data+ is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making. CompTIA Data+ gives you the confidence to bring data analysis to life. <br /><br /> As the importance for data analytics grows, more job roles are required to set context and better communicate vital business intelligence. Collecting, analyzing, and reporting on data can drive priorities and lead business decision-making.</p></div><div id="course-pills-2"><h3>1 - Identifying Basic Concepts of Data Schemas</h3> <ul> <li>Identify Relational and Non-Relational Databases</li> <li>Understand the Way We Use Tables, Primary Keys, and Normalization</li> </ul> <h3>2 - Understanding  Different Data Systems</h3> <ul> <li>Describe Types of Data Processing and Storage Systems</li> <li>Explain How Data Changes</li> </ul> <h3>3 - Understanding  Types and  Characteristics of Data</h3> <ul> <li>Understand Types of Data</li> <li>Break Down the Field Data Types</li> </ul> <h3>4 - Comparing and  Contrasting Different  Data Structures,  Formats, and Markup  Languages</h3> <ul> <li>Differentiate between Structured Data and Unstructured Data</li> <li>Recognize Different File Formats</li> <li>Understand the Different Code Languages Used for Data</li> </ul> <h3>5 - Explaining  Data Integration and  Collection Methods</h3> <ul> <li>Understand the Processes of Extracting, Transforming, and Loading Data</li> <li>Explain API/Web Scraping and Other Collection Methods</li> <li>Collect and Use Public and Publicly-Available Data</li> <li>Use and Collect Survey Data</li> </ul> <h3>6 - Identifying  Common Reasons for  Cleansing and Profiling  Data</h3> <ul> <li>Learn to Profile Data</li> <li>Address Redundant, Duplicated, and Unnecessary Data</li> <li>Work with Missing Value</li> <li>Address Invalid Data</li> <li>Convert Data to Meet Specifications</li> </ul> <h3>7 - Executing  Different Data  Manipulation  Techniques</h3> <ul> <li>Manipulate Field Data and Create Variables</li> <li>Transpose and Append Data</li> <li>Query Data</li> </ul> <h3>8 - Explaining  Common Techniques for  Data Manipulation and  Optimization</h3> <ul> <li>Use Functions to Manipulate Data</li> <li>Use Common Techniques for Query Optimization</li> </ul> <h3>9 - Applying  Descriptive Statistical  Methods</h3> <ul> <li>Use Measures of Central Tendency</li> <li>Use Measures of Dispersion</li> <li>Use Frequency and Percentages</li> </ul> <h3>10 - Describing  Key Analysis Techniques</h3> <ul> <li>Get Started with Analysis</li> <li>Recognize Types of Analysis</li> </ul> <h3>11 - Understanding the Use  of Different Statistical  Methods</h3> <ul> <li>Understand the Importance of Statistical Tests</li> <li>Break Down the Hypothesis Test</li> <li>Understand Tests and Methods to Determine Relationships Between Variables</li> </ul> <h3>12 - Using the  Appropriate Type of  Visualization</h3> <ul> <li>Use Basic Visuals</li> <li>Build Advanced Visuals</li> <li>Build Maps with Geographical Data</li> <li>Use Visuals to Tell a Story</li> </ul> <h3>13 - Expressing  Business Requirements  in a Report Format</h3> <ul> <li>Consider Audience Needs When Developing a Report</li> <li>Describe Data Source Considerations For Reporting</li> <li>Describe Considerations for Delivering Reports and Dashboards</li> <li>Develop Reports or Dashboards</li> <li>Understand Ways to Sort and Filter Data</li> </ul> <h3>14 - Designing  Components for Reports  and Dashboards</h3> <ul> <li>Design Elements for Reports and Dashboards</li> <li>Utilize Standard Elements</li> <li>Creating a Narrative and Other Written Elements</li> <li>Understand Deployment Considerations</li> </ul> <h3>15 - Understand Deployment Considerations</h3> <ul> <li>Understand How Updates and Timing Affect Reporting</li> <li>Differentiate Between Types of Reports</li> </ul> <h3>16 - Summarizing the Importance of Data Governance</h3> <ul> <li>Define Data Governance</li> <li>Understand Access Requirements and Policies</li> <li>Understand Security Requirements</li> <li>Understand Entity Relationship Requirements</li> </ul> <h3>17 - Applying  Quality Control to Data</h3> <ul> <li>Describe Characteristics, Rules, and Metrics of Data Quality</li> <li>Identify Reasons to Quality Check Data and Methods of Data Validation</li> </ul> <h3>18 - Explaining Master Data Management Concepts</h3> <ul> <li>Explain the Basics of Master Data Management</li> <li>Describe Master Data Management Processes</li> </ul></div><div id="course-pills-3"><h4>Additional course details:</h4><br /><p>Nexus Humans CompTIA Data Plus (DA0-001) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. </p><p></p> This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts. <p></p>Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.  <p></p>While we feel this is the best course for the CompTIA Data Plus (DA0-001) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.  <p></p>Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.<p></p></div>

Duration
5 Days
30 CPD hours
Overview
Mining data Manipulating data Visualizing and reporting data Applying basic statistical methods Analyzing complex datasets while adhering to governance and quality standards throughout the entire data life cycle
CompTIA Data+ is an early-career data analytics certification for professionals tasked with developing and promoting data-driven business decision-making. CompTIA Data+ gives you the confidence to bring data analysis to life. 

 As the importance for data analytics grows, more job roles are required to set context and better communicate vital business intelligence. Collecting, analyzing, and reporting on data can drive priorities and lead business decision-making.
1 - Identifying Basic Concepts of Data Schemas
Identify Relational and Non-Relational Databases
Understand the Way We Use Tables, Primary Keys, and Normalization
2 - Understanding Different Data Systems
Describe Types of Data Processing and Storage Systems
Explain How Data Changes
3 - Understanding Types and Characteristics of Data
Understand Types of Data
Break Down the Field Data Types
4 - Comparing and Contrasting Different Data Structures, Formats, and Markup Languages
Differentiate between Structured Data and Unstructured Data
Recognize Different File Formats
Understand the Different Code Languages Used for Data
5 - Explaining Data Integration and Collection Methods
Understand the Processes of Extracting, Transforming, and Loading Data
Explain API/Web Scraping and Other Collection Methods
Collect and Use Public and Publicly-Available Data
Use and Collect Survey Data
6 - Identifying Common Reasons for Cleansing and Profiling Data
Learn to Profile Data
Address Redundant, Duplicated, and Unnecessary Data
Work with Missing Value
Address Invalid Data
Convert Data to Meet Specifications
7 - Executing Different Data Manipulation Techniques
Manipulate Field Data and Create Variables
Transpose and Append Data
Query Data
8 - Explaining Common Techniques for Data Manipulation and Optimization
Use Functions to Manipulate Data
Use Common Techniques for Query Optimization
9 - Applying Descriptive Statistical Methods
Use Measures of Central Tendency
Use Measures of Dispersion
Use Frequency and Percentages
10 - Describing Key Analysis Techniques
Get Started with Analysis
Recognize Types of Analysis
11 - Understanding the Use of Different Statistical Methods
Understand the Importance of Statistical Tests
Break Down the Hypothesis Test
Understand Tests and Methods to Determine Relationships Between Variables
12 - Using the Appropriate Type of Visualization
Use Basic Visuals
Build Advanced Visuals
Build Maps with Geographical Data
Use Visuals to Tell a Story
13 - Expressing Business Requirements in a Report Format
Consider Audience Needs When Developing a Report
Describe Data Source Considerations For Reporting
Describe Considerations for Delivering Reports and Dashboards
Develop Reports or Dashboards
Understand Ways to Sort and Filter Data
14 - Designing Components for Reports and Dashboards
Design Elements for Reports and Dashboards
Utilize Standard Elements
Creating a Narrative and Other Written Elements
Understand Deployment Considerations
15 - Understand Deployment Considerations
Understand How Updates and Timing Affect Reporting
Differentiate Between Types of Reports
16 - Summarizing the Importance of Data Governance
Define Data Governance
Understand Access Requirements and Policies
Understand Security Requirements
Understand Entity Relationship Requirements
17 - Applying Quality Control to Data
Describe Characteristics, Rules, and Metrics of Data Quality
Identify Reasons to Quality Check Data and Methods of Data Validation
18 - Explaining Master Data Management Concepts
Explain the Basics of Master Data Management
Describe Master Data Management Processes
Additional course details:
Nexus Humans CompTIA Data Plus (DA0-001) training program is a workshop that presents an invigorating mix of sessions, lessons, and masterclasses meticulously crafted to propel your learning expedition forward. 

This immersive bootcamp-style experience boasts interactive lectures, hands-on labs, and collaborative hackathons, all strategically designed to fortify fundamental concepts.

Guided by seasoned coaches, each session offers priceless insights and practical skills crucial for honing your expertise. Whether you're stepping into the realm of professional skills or a seasoned professional, this comprehensive course ensures you're equipped with the knowledge and prowess necessary for success.

While we feel this is the best course for the CompTIA Data Plus (DA0-001) course and one of our Top 10 we encourage you to read the course outline to make sure it is the right content for you.

Additionally, private sessions, closed classes or dedicated events are available both live online and at our training centres in Dublin and London, as well as at your offices anywhere in the UK, Ireland or across EMEA.


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