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Learning Outcomes After completing this course, learners will be able to: Learn Python for data analysis using NumPy and Pandas Acquire a clear understanding of data visualisation using Matplotlib, Seaborn and Pandas Deepen your knowledge of Python for interactive and geographical potting using Plotly and Cufflinks Understand Python for data science and machine learning Get acquainted with Recommender Systems with Python Enhance your understanding of Python for Natural Language Processing (NLP) Description Whether you are from an engineering background or not you still can efficiently work in the field of data science and the machine learning sector, if you have proficient knowledge of Python. Since Python is the easiest and most used programming language, you can start learning this language now to advance your career with the Data Science And Machine Learning Using Python : A Bootcamp course. This course will give you a thorough understanding of the Python programming language. Moreover, it will show how can you use Python for data analysis and machine learning. Alongside that, from this course, you will get to learn data visualisation, and interactive and geographical plotting by using Python. The course will also provide detailed information on Python for data analysis, Natural Language Processing (NLP) and much more. Upon successful completion of this course, get a CPD- certificate of achievement which will enhance your resume and career. Certificate of Achievement After the successful completion of the final assessment, you will receive a CPD-accredited certificate of achievement. The PDF certificate is for 9.99, and it will be sent to you immediately after through e-mail. You can get the hard copy for 15.99, which will reach your doorsteps by post.  Method of Assessment After completing this course, you will be provided with some assessment questions. To pass that assessment, you need to score at least 60%. Our experts will check your assessment and give you feedback accordingly. Career Path After completing this course, you can explore various career options such as Web Developer Software Engineer Data Scientist Machine Learning Engineer Data Analyst In the UK professionals usually get a salary of £25,000 - £30,000 per annum for these positions. Course Content Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Machine Learning using Python : A Bootcamp 00:00:00 Order your Certificates & Transcripts Order your Certificates & Transcripts 00:00:00 Frequently Asked Questions Are there any prerequisites for taking the course? There are no specific prerequisites for this course, nor are there any formal entry requirements. All you need is an internet connection, a good understanding of English and a passion for learning for this course. Can I access the course at any time, or is there a set schedule? You have the flexibility to access the course at any time that suits your schedule. Our courses are self-paced, allowing you to study at your own pace and convenience. How long will I have access to the course? For this course, you will have access to the course materials for 1 year only. This means you can review the content as often as you like within the year, even after you've completed the course. However, if you buy Lifetime Access for the course, you will be able to access the course for a lifetime. Is there a certificate of completion provided after completing the course? Yes, upon successfully completing the course, you will receive a certificate of completion. This certificate can be a valuable addition to your professional portfolio and can be shared on your various social networks. Can I switch courses or get a refund if I'm not satisfied with the course? We want you to have a positive learning experience. If you're not satisfied with the course, you can request a course transfer or refund within 14 days of the initial purchase. How do I track my progress in the course? Our platform provides tracking tools and progress indicators for each course. You can monitor your progress, completed lessons, and assessments through your learner dashboard for the course. What if I have technical issues or difficulties with the course? If you encounter technical issues or content-related difficulties with the course, our support team is available to assist you. You can reach out to them for prompt resolution.

Are you ready to be at the helm, steering the ship into a realm where data is the new gold? In the infinite world of data, where information spirals at breakneck speed, lies a universe rich in potential and discovery: the domain of Data Science and Visualisation. This 'Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3' course unravels the wonders of extracting meaningful insights using Python, the worldwide leading language of data experts. Harnessing the strength of Python, you'll delve deep into data analysis, experience the finesse of visualisation tools, and master the art of Machine Learning. The need to understand, interpret, and act on this data has become paramount, with vast amounts of data increasing the digital sphere. Envision a canvas where raw numbers are transformed into visually compelling stories, and machine learning models foretell future trends. This course provides a meticulous pathway for anyone eager to learn the data representation paradigms backed by Python's robust libraries. Dive into a curriculum rich with analytical explorations, visual artistry, and machine learning predictions. Learning Outcomes Understanding the foundations and functionalities of Python, focusing on its application in data science. Applying various Python libraries like NumPy and Pandas for effective data analysis. Demonstrating proficiency in creating detailed visual narratives using tools like matplotlib, Seaborn, and Plotly. Implementing Machine Learning algorithms in Python using scikit-learn, ranging from regression models to clustering techniques. Designing and executing a holistic data analysis and visualisation project, encapsulating all learned techniques. Exploring advanced topics, encompassing recommender systems and natural language processing with Python. Attaining the confidence to independently analyse complex data sets and translate them into actionable insights.   Video Playerhttps://studyhub.org.uk/wp-content/uploads/2021/03/Data-Science-and-Visualisation-with-Machine-Learning.mp400:0000:0000:00Use Up/Down Arrow keys to increase or decrease volume. Why buy this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments are designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Who is this Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 course for? Aspiring data scientists aiming to harness the power of Python. Researchers keen to enrich their analytical and visualisation skills. Analysts aiming to add machine learning to their toolkit. Developers striving to integrate data analytics into applications. Business professionals desiring data-driven decision-making capabilities. Career path Data Scientist: £55,000 - £85,000 Per Annum Machine Learning Engineer: £60,000 - £90,000 Per Annum Data Analyst: £30,000 - £50,000 Per Annum Data Visualisation Specialist: £45,000 - £70,000 Per Annum Natural Language Processing Specialist: £65,000 - £95,000 Per Annum Business Intelligence Developer: £40,000 - £65,000 Per Annum Prerequisites This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Certificate in Data Science and Visualisation with Machine Learning at QLS Level 3 was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Endorsed Certificate of Achievement from the Quality Licence Scheme Learners will be able to achieve an endorsed certificate after completing the course as proof of their achievement. You can order the endorsed certificate for only £85 to be delivered to your home by post. For international students, there is an additional postage charge of £10. Endorsement The Quality Licence Scheme (QLS) has endorsed this course for its high-quality, non-regulated provision and training programmes. The QLS is a UK-based organisation that sets standards for non-regulated training and learning. This endorsement means that the course has been reviewed and approved by the QLS and meets the highest quality standards. Please Note: Studyhub is a Compliance Central approved resale partner for Quality Licence Scheme Endorsed courses. Course Curriculum Welcome, Course Introduction & overview, and Environment set-up Welcome & Course Overview 00:07:00 Set-up the Environment for the Course (lecture 1) 00:09:00 Set-up the Environment for the Course (lecture 2) 00:25:00 Two other options to setup environment 00:04:00 Python Essentials Python data types Part 1 00:21:00 Python Data Types Part 2 00:15:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 1) 00:16:00 Loops, List Comprehension, Functions, Lambda Expression, Map and Filter (Part 2) 00:20:00 Python Essentials Exercises Overview 00:02:00 Python Essentials Exercises Solutions 00:22:00 Python for Data Analysis using NumPy What is Numpy? A brief introduction and installation instructions. 00:03:00 NumPy Essentials - NumPy arrays, built-in methods, array methods and attributes. 00:28:00 NumPy Essentials - Indexing, slicing, broadcasting & boolean masking 00:26:00 NumPy Essentials - Arithmetic Operations & Universal Functions 00:07:00 NumPy Essentials Exercises Overview 00:02:00 NumPy Essentials Exercises Solutions 00:25:00 Python for Data Analysis using Pandas What is pandas? A brief introduction and installation instructions. 00:02:00 Pandas Introduction 00:02:00 Pandas Essentials - Pandas Data Structures - Series 00:20:00 Pandas Essentials - Pandas Data Structures - DataFrame 00:30:00 Pandas Essentials - Handling Missing Data 00:12:00 Pandas Essentials - Data Wrangling - Combining, merging, joining 00:20:00 Pandas Essentials - Groupby 00:10:00 Pandas Essentials - Useful Methods and Operations 00:26:00 Pandas Essentials - Project 1 (Overview) Customer Purchases Data 00:08:00 Pandas Essentials - Project 1 (Solutions) Customer Purchases Data 00:31:00 Pandas Essentials - Project 2 (Overview) Chicago Payroll Data 00:04:00 Pandas Essentials - Project 2 (Solutions Part 1) Chicago Payroll Data 00:18:00 Python for Data Visualization using matplotlib Matplotlib Essentials (Part 1) - Basic Plotting & Object Oriented Approach 00:13:00 Matplotlib Essentials (Part 2) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials (Part 3) - Basic Plotting & Object Oriented Approach 00:22:00 Matplotlib Essentials - Exercises Overview 00:06:00 Matplotlib Essentials - Exercises Solutions 00:21:00 Python for Data Visualization using Seaborn Seaborn - Introduction & Installation 00:04:00 Seaborn - Distribution Plots 00:25:00 Seaborn - Categorical Plots (Part 1) 00:21:00 Seaborn - Categorical Plots (Part 2) 00:16:00 Seborn-Axis Grids 00:25:00 Seaborn - Matrix Plots 00:13:00 Seaborn - Regression Plots 00:11:00 Seaborn - Controlling Figure Aesthetics 00:10:00 Seaborn - Exercises Overview 00:04:00 Seaborn - Exercise Solutions 00:19:00 Python for Data Visualization using pandas Pandas Built-in Data Visualization 00:34:00 Pandas Data Visualization Exercises Overview 00:03:00 Panda Data Visualization Exercises Solutions 00:13:00 Python for interactive & geographical plotting using Plotly and Cufflinks Plotly & Cufflinks - Interactive & Geographical Plotting (Part 1) 00:19:00 Plotly & Cufflinks - Interactive & Geographical Plotting (Part 2) 00:14:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Overview) 00:11:00 Plotly & Cufflinks - Interactive & Geographical Plotting Exercises (Solutions) 00:37:00 Capstone Project - Python for Data Analysis & Visualization Project 1 - Oil vs Banks Stock Price during recession (Overview) 00:15:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 1) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 2) 00:18:00 Project 1 - Oil vs Banks Stock Price during recession (Solutions Part 3) 00:17:00 Project 2 (Optional) - Emergency Calls from Montgomery County, PA (Overview) 00:03:00 Python for Machine Learning (ML) - scikit-learn - Linear Regression Model Introduction to ML - What, Why and Types.. 00:15:00 Theory Lecture on Linear Regression Model, No Free Lunch, Bias Variance Tradeoff 00:15:00 scikit-learn - Linear Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Linear Regression Model Hands-on (Part 2) 00:19:00 Good to know! How to save and load your trained Machine Learning Model! 00:01:00 scikit-learn - Linear Regression Model (Insurance Data Project Overview) 00:08:00 scikit-learn - Linear Regression Model (Insurance Data Project Solutions) 00:30:00 Python for Machine Learning - scikit-learn - Logistic Regression Model Theory: Logistic Regression, conf. mat., TP, TN, Accuracy, Specificityetc. 00:10:00 scikit-learn - Logistic Regression Model - Hands-on (Part 1) 00:17:00 scikit-learn - Logistic Regression Model - Hands-on (Part 2) 00:20:00 scikit-learn - Logistic Regression Model - Hands-on (Part 3) 00:11:00 scikit-learn - Logistic Regression Model - Hands-on (Project Overview) 00:05:00 scikit-learn - Logistic Regression Model - Hands-on (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn - K Nearest Neighbors Theory: K Nearest Neighbors, Curse of dimensionality . 00:08:00 scikit-learn - K Nearest Neighbors - Hands-on 00:25:00 scikt-learn - K Nearest Neighbors (Project Overview) 00:04:00 scikit-learn - K Nearest Neighbors (Project Solutions) 00:14:00 Python for Machine Learning - scikit-learn - Decision Tree and Random Forests Theory: D-Tree & Random Forests, splitting, Entropy, IG, Bootstrap, Bagging. 00:18:00 scikit-learn - Decision Tree and Random Forests - Hands-on (Part 1) 00:19:00 scikit-learn - Decision Tree and Random Forests (Project Overview) 00:05:00 scikit-learn - Decision Tree and Random Forests (Project Solutions) 00:15:00 Python for Machine Learning - scikit-learn -Support Vector Machines (SVMs) Support Vector Machines (SVMs) - (Theory Lecture) 00:07:00 scikit-learn - Support Vector Machines - Hands-on (SVMs) 00:30:00 scikit-learn - Support Vector Machines (Project 1 Overview) 00:07:00 scikit-learn - Support Vector Machines (Project 1 Solutions) 00:20:00 scikit-learn - Support Vector Machines (Optional Project 2 - Overview) 00:02:00 Python for Machine Learning - scikit-learn - K Means Clustering Theory: K Means Clustering, Elbow method .. 00:11:00 scikit-learn - K Means Clustering - Hands-on 00:23:00 scikit-learn - K Means Clustering (Project Overview) 00:07:00 scikit-learn - K Means Clustering (Project Solutions) 00:22:00 Python for Machine Learning - scikit-learn - Principal Component Analysis (PCA) Theory: Principal Component Analysis (PCA) 00:09:00 scikit-learn - Principal Component Analysis (PCA) - Hands-on 00:22:00 scikit-learn - Principal Component Analysis (PCA) - (Project Overview) 00:02:00 scikit-learn - Principal Component Analysis (PCA) - (Project Solutions) 00:17:00 Recommender Systems with Python - (Additional Topic) Theory: Recommender Systems their Types and Importance 00:06:00 Python for Recommender Systems - Hands-on (Part 1) 00:18:00 Python for Recommender Systems - - Hands-on (Part 2) 00:19:00 Python for Natural Language Processing (NLP) - NLTK - (Additional Topic) Natural Language Processing (NLP) - (Theory Lecture) 00:13:00 NLTK - NLP-Challenges, Data Sources, Data Processing .. 00:13:00 NLTK - Feature Engineering and Text Preprocessing in Natural Language Processing 00:19:00 NLTK - NLP - Tokenization, Text Normalization, Vectorization, BoW. 00:19:00 NLTK - BoW, TF-IDF, Machine Learning, Training & Evaluation, Naive Bayes 00:13:00 NLTK - NLP - Pipeline feature to assemble several steps for cross-validation 00:09:00 Resources Resources - Data Science and Visualisation with Machine Learning 00:00:00 Order your QLS Endorsed Certificate Order your QLS Endorsed Certificate 00:00:00

In this course, we will learn about the Google Apps Script to create custom functions within G Suite that will help you increase your productivity. This course contains 100+ coding examples. For this course, you need JavaScript and basic HTML and CSS knowledge, a Google account, and a basic web development understanding.

Welcome to the Complete Strapi Course, where we will start from the basics and learn how to build and deploy a full app with custom features and a full plugin. We will be building our own developer blog application and learning how to deploy it with two different architectures and CI/CD pipeline. Basic knowledge of JavaScript and React is a requirement.

In this competitive job market, you need to have some specific skills and knowledge to start your career and establish your position. This AWS Solutions Architect & SysOps Administrator will help you understand the current demands, trends and skills in the sector. The course will provide you with the essential skills you need to boost your career growth in no time. The AWS Solutions Architect & SysOps Administrator will give you clear insight and understanding about your roles and responsibilities, job perspective and future opportunities in this field. You will be familiarised with various actionable techniques, career mindset, regulations and how to work efficiently. This course is designed to provide an introduction to AWS Solutions Architect & SysOps Administrator and offers an excellent way to gain the vital skills and confidence to work toward a successful career. It also provides access to proven educational knowledge about the subject and will support those wanting to attain personal goals in this area. Learning Objectives Learn the fundamental skills you require to be an expert Explore different techniques used by professionals Find out the relevant job skills & knowledge to excel in this profession Get a clear understanding of the job market and current demand Update your skills and fill any knowledge gap to compete in the relevant industry CPD accreditation for proof of acquired skills and knowledge Who is this Course for? Whether you are a beginner or an existing practitioner, our CPD accredited AWS Solutions Architect & SysOps Administrator is perfect for you to gain extensive knowledge about different aspects of the relevant industry to hone your skill further. It is also great for working professionals who have acquired practical experience but require theoretical knowledge with a credential to support their skill, as we offer CPD accredited certification to boost up your resume and promotion prospects. Entry Requirement Anyone interested in learning more about this subject should take this AWS Solutions Architect & SysOps Administrator. This course will help you grasp the basic concepts as well as develop a thorough understanding of the subject. The course is open to students from any academic background, as there is no prerequisites to enrol on this course. The course materials are accessible from an internet enabled device at anytime of the day. CPD Certificate from Course Gate At the successful completion of the course, you can obtain your CPD certificate from us. You can order the PDF certificate for £9 and the hard copy for £15. Also, you can order both PDF and hardcopy certificates for £22. Career path The AWS Solutions Architect & SysOps Administrator will help you to enhance your knowledge and skill in this sector. After accomplishing this course, you will enrich and improve yourself and brighten up your career in the relevant job market. Course Curriculum AWS Tutorial | Course Introduction AWS Tutorial Introduction 00:02:00 What You Will Become 00:01:00 Cloud Computing Basics - Introducing IaaS, PaaS & SaaS Service Models Cloud Computing Introduction 00:07:00 Cloud service models 00:10:00 Cloud Deployment Models 00:05:00 Pre-Requisites - Key Skills Required Before Get Started with AWS Linux Basics 00:02:00 AWS Free Tier Account 00:01:00 Desire to adopt Cloud Model 00:01:00 AWS Global Infrastructure AWS Regions 00:03:00 Availability Zones 00:06:00 EC2 Basics- Virtual Servers in AWS Cloud What is Amazon EC2? 00:03:00 How to Create EC2 Instance - Linux 00:13:00 How to Create EC2 Instance - Windows 00:06:00 How to configure EC2 Instance? 00:15:00 Instance States 00:05:00 EC2 Instance Types 00:08:00 EC2 Pricing Models 00:17:00 Networking Basics - Basics of Network Topology and IP address classes Know Networking Terms - IP, Port, Protocol, Service, Daemon 00:15:00 Apache HTTP Server 00:19:00 VPC - Configure Custom Virtual Private Cloud in the AWS Cloud Build Your Own Datacenter 00:04:00 What is AWS VPC? 00:04:00 Create Custom VPC 00:07:00 Create Custom VPC - LAB 00:17:00 Private Subnet Vs Public Subnet 00:08:00 Private Subnet Vs Public Subnet - LAB 00:15:00 VPC Peering Connections 00:04:00 VPC Peering Connections - LAB 00:11:00 Network ACLs Vs Security Groups 00:09:00 Virtual Private Network (VPN) 00:03:00 EC2 Advanced - Virtual Servers in AWS Cloud EC2 Instance Meta Data 00:03:00 How to Manage Instance Settings 00:07:00 BootStrap EC2 instances 00:05:00 AMI - Create, Copy & Manage Permissions 00:10:00 EC2 Placement Groups 00:05:00 Public IP Vs. Elastic IP 00:03:00 EC2 Summary 00:05:00 Introduction to Elastic Block Store - EBS 00:06:00 Instance Store Volumes 00:06:00 EBS Snapshots 00:03:00 Cloud Watch Basics - Monitor AWS Resources & Applications Performance Cloud Watch Metrics 00:04:00 Cloud Watch Dashboards 00:03:00 Cloud Watch Alarms 00:05:00 Simple Notification Service SNS Introduction 00:03:00 Create Topic & Subscriptions to the Topic 00:03:00 Cloud Watch Alarms - LAB 00:11:00 Certificate Manager - Provision, Manage & Deploy SSL/TLS Certificates Data Encryption Introduction & Types 00:04:00 Create SSL/TLS Certificates - LAB 00:05:00 Route 53 - Scalable DNS and Domain Registration What is Route 53? | How DNS works? 00:06:00 How to Create Hosted Zone 00:07:00 DNS Record Types 00:07:00 Route53 Health Checks 00:03:00 Load Balancing - High Availability Solution for the Applications & Servers High Availability & Load Balancer - Architecture 00:16:00 Classic Load Balancer 00:08:00 Application Load Balancer 00:15:00 Network Load Balancer 00:06:00 Auto Scaling - Scale EC2 instances Proportion to Application Load What is Auto Scaling? 00:08:00 Auto Scaling Group - Architecture 00:08:00 Auto Scaling Group with ELB Association 00:08:00 Create launch Configuration 00:03:00 Create Auto Scaling group 00:16:00 S3 - Scalable Object Storage in the AWS Cloud What is S3? 00:08:00 Create First S3 Bucket 00:03:00 Create First S3 Object 00:04:00 S3 Storage Classes 00:06:00 S3 Versioning 00:10:00 S3 Life Cycle Management 00:08:00 IAM - Manage User Access and Encryption Keys What is IAM ? | IAM Architecture 00:12:00 IAM User, IAM Group - LAB 00:11:00 IAM Policy - LAB 00:07:00 IAM Role - LAB 00:04:00 S3 Cross-Region Replication - LAB 00:10:00 IAM MFA & Security Best Practices 00:07:00 AWS CLI - Manage AWS Services & Resources through AWS CLI How to Install AWS CLI on Windows Systems 00:05:00 How to Install AWS CLI on Linux 00:03:00 Explore AWS CLI Real-time Use Cases 00:07:00 Key Management Service - Generate & Manage AWS Encryption Keys KMS Architecture 00:05:00 Key Management Service (LAB) 00:05:00 EBS Volume Encryption 00:02:00 S3 Encryption 00:02:00 EFS - Managed Network File Storage for EC2 EFS Architecture 00:04:00 Create EFS 00:05:00 Mount on EC2 Instances 00:04:00 S3 Glacier - Data Archival Solution in the AWS Cloud What is S3 Glacier? 00:02:00 Create Vault 00:02:00 Retrieval Policies 00:03:00 Cloud Watch Advanced - Monitor AWS Resources & Applications Performance Cloud Watch Logs 00:13:00 Cloud Watch Custom Metrics 00:04:00 Cloud Watch Events 00:07:00 Cloud Front - Global Content Delivery Network Cloud Front Architecture 00:05:00 Create Cloud Front Distribution - LAB 00:05:00 Cloud Trail - Track IAM user activity & API Usage What is Cloud Trail 00:02:00 Create Trail - LAB 00:05:00 Lambda - Run Code without Thinking About Code | Serverless Architecture What is AWS Lambda? 00:06:00 Getting started with AWS Lambda - LAB 00:08:00 RDS RDS Introduction 00:05:00 Get started with RDS - LAB 00:07:00 DynamoDB Create DynamoDB Table 00:02:00 API Gateway - Build, Deploy & Manage APIs API Gateway Architecture 00:06:00 Create an API - LAB 00:06:00 API Gateway Realtime Use Case 00:03:00 Elastic Beanstalk What is Elastic Beanstalk? 00:06:00 Create a web app - LAB 00:06:00 Cloud Formation - Create & Manager resources with Cloud Formation Templates What is Cloud Formation? 00:06:00 Create Stack - LAB 00:11:00 Cloud Formation Designer - LAB 00:07:00 Systems Manager - Session Manager, Run Command, Parameter Store, Patch Manager What is a Systems Manager? 00:09:00 Session Manager 00:03:00 Run Command 00:06:00 Patch Manager 00:03:00 Secrets Manager - Rotate, Manage & Retrieve Secret through their life cycle What is Secrets Manager? 00:03:00 Store a new secret in Secrets Manager - LAB 00:03:00 Secrets Manager Use Cases 00:02:00 Inspector - Analyze Application Security & Report Inspector Architecture 00:05:00 Inspector Install Run Analyze - LAB 00:06:00 LightSail - Launch & Manage Virtual Private Servers LightSail Architecture 00:05:00 Get Started with LightSail! 00:04:00 AWS Limitations AWS Free Tier Limitations 00:04:00 AWS Service Limits 00:04:00 AWS Cost Explorer What is AWS Cost Explorer? How to Analyze Cost Report? 00:04:00 AWS Support AWS Support Plans 00:04:00 How to raise support tickets with AWS? 00:03:00 AWS Trusted Advisor What is AWS Trusted Advisor? 00:06:00 AWS Project - Realtime 3-Tier Architecture AWS Project - 3-Tier Architecture Deep Drive 00:11:00 Certificate and Transcript Order Your Certificates or Transcripts 00:00:00

You will learn how to develop reliable and scalable serverless applications, following the microservices architecture. We will develop event-driven microservices with AWS serverless services that come with the power of the cloud, high availability and high scalability by nature following serverless design patterns and best practices.

Spring MVC is a Java framework that is used to build web applications; it follows the MVC design pattern. In this course, we'll learn all key Spring framework features to develop robust enterprise-grade web applications. We'll learn how to create a complete Spring-based web application from scratch, with Spring Security, MVC, Spring Data, View, and more. In the end, we will combine all the modules we have learned and create a real-time Todo application from scratch.

This course covers the complete AWS Application Stacks using Cloud Development Kit. Learn to deploy simple-to-complex resources in AWS using CDK and launch several stacks and templates. This course assumes you know how to use the AWS Cloud. It will help you transform your solution architecting skills into CDK Stack.

Get involved in a learning adventure, mastering R from foundational basics to advanced techniques. This course is a gateway to the realm of data science. Explore statistical machine learning models and intricacies of deep learning and create interactive Shiny apps. Unleash the power of R and elevate your proficiency in data-driven decision-making.

Embark on a transformative journey into the realm of cutting-edge development with our course, 'Master Spring Boot 3.0 and MongoDB.' Crafted with precision and innovation, this course is a gateway to mastering the intricate dance between Spring Boot and MongoDB, providing you with the skills to revolutionize your coding prowess. Immerse yourself in an engaging learning experience, where each section unfolds like a chapter in a captivating novel, unveiling the secrets of MongoDB, the nuances of Bootstrapping applications, and the artistry of integrating OpenAPI. It's more than a course; it's an odyssey into the heart of modern development, beckoning you to embrace a new era of programming possibilities. In this dynamic learning adventure, traverse through the course curriculum like an explorer unraveling a map to success. From laying the groundwork in the introduction to conquering complex queries and MongoDB templates, you'll navigate the landscape of development with confidence. The course is not just about acquiring knowledge; it's about transforming that knowledge into practical expertise. Whether you're a novice eager to embark on a coding expedition or a seasoned developer seeking to enhance your skills, this course is your compass to navigate the vast territories of Spring Boot and MongoDB, promising a voyage filled with discovery and mastery. Learning Outcomes Develop a profound understanding of MongoDB, from environmental preparation to advanced query methods. Master the art of bootstrapping applications, gaining hands-on experience in crafting robust and efficient systems. Uncover the intricacies of collections relationships and the use of DBRef, adding a layer of sophistication to your database interactions. Seamlessly integrate OpenAPI into your projects, enhancing the accessibility and documentation of your applications. Acquire expertise in document queries and MongoDB templates, empowering you to build scalable and efficient databases. Why choose this Master Spring boot 3.0 and MongoDB course? Unlimited access to the course for a lifetime. Opportunity to earn a certificate accredited by the CPD Quality Standards and CIQ after completing this course. Structured lesson planning in line with industry standards. Immerse yourself in innovative and captivating course materials and activities. Assessments designed to evaluate advanced cognitive abilities and skill proficiency. Flexibility to complete the Course at your own pace, on your own schedule. Receive full tutor support throughout the week, from Monday to Friday, to enhance your learning experience. Unlock career resources for CV improvement, interview readiness, and job success. Who is this Master Spring boot 3.0 and MongoDB course for? Aspiring developers keen on mastering the Spring Boot-MongoDB synergy. Software engineers looking to deepen their understanding of advanced database operations. Students and coding enthusiasts eager to explore the latest tools in the development landscape. Professionals seeking to upgrade their skill set in line with industry demands. Entrepreneurs or tech enthusiasts aiming to build robust applications from the ground up. Career path MongoDB Developer: £40,000 - £60,000 Spring Boot Engineer: £45,000 - £70,000 Database Architect: £55,000 - £80,000 Full Stack Developer: £50,000 - £75,000 Java Developer (Specializing in Spring Boot): £45,000 - £65,000 API Integration Specialist: £50,000 - £70,000 Prerequisites This Master Spring boot 3.0 and MongoDB does not require you to have any prior qualifications or experience. You can just enrol and start learning. This Master Spring boot 3.0 and MongoDB was made by professionals and it is compatible with all PC's, Mac's, tablets and smartphones. You will be able to access the course from anywhere at any time as long as you have a good enough internet connection. Certification After studying the course materials, there will be a written assignment test which you can take at the end of the course. After successfully passing the test you will be able to claim the pdf certificate for £4.99 Original Hard Copy certificates need to be ordered at an additional cost of £8. Course Curriculum Section 01: Introduction What is MongoDB 00:02:00 The Difference Between SQL and NoSQL 00:03:00 How to Choose Between SQL and NoSQL 00:04:00 Section 02: Prepare The Mongo Environment MongoDB UI Tools 00:01:00 Why Use Docker for Development 00:01:00 Create an Empty Docker Compose File 00:02:00 Mongo Server (Docker Compose) 00:04:00 Mongo Express (Docker Compose) 00:03:00 Run and Test 00:03:00 Section 03: Mongo Terms Collections and Documents in Mongo 00:03:00 Section 04: Bootstrapping The Application Create a New Spring Boot 3.0 Project 00:04:00 Project Structure 00:02:00 Include the Docker Compose with the Project and Run It 00:04:00 Project Architecture 00:03:00 Connect to MongoDB 00:03:00 Create a New Database 00:01:00 Understand Admin - Local and Config Databases 00:05:00 Create a Java Class 00:04:00 Transform the Java Class into a Collection 00:02:00 Create a Mongo Repository 00:02:00 Insert the First Data into the Collection 00:06:00 Create a Product Service 00:04:00 Save the product 00:03:00 Find Product by ID 00:01:00 Find All Products 00:01:00 Delete Product 00:01:00 Section 05: CRUD Operations Create Product Controller 00:02:00 Save Product Operation 00:02:00 Find All Products 00:01:00 Find Product by ID 00:01:00 Delete Product 00:02:00 Test the CRUD Operations 00:06:00 Section 06: DBRef And Collections Relationships Extend the Product Model 00:02:00 Create Category Java Class 00:02:00 User Category as a Field 00:03:00 Relationship between Category and Product 00:01:00 Transform the Category to a Collection 00:01:00 Create a Category Repository 00:01:00 Insert Categories at Startup 00:02:00 The Issue in Linking Collections 00:03:00 Add Relationship Between Collections 00:04:00 Section 07: OpenApi Integration Add OpenApi Dependency 00:02:00 Test and Play with Swagger UI 00:06:00 Section 08: Documents Queries And Mongo Template Extend the Product Model 00:02:00 Create a Search Service 00:01:00 What is MongoTemplate 00:02:00 Is() Query 00:07:00 Testing the Is() Query 00:02:00 Regex() Query 00:06:00 Lt() Query 00:03:00 Gt() Query 00:02:00 Sorting Data 00:05:00 Paging and Sorting 00:06:00 Section 09: Query Methods Create a Query Method Service 00:03:00 FindByX Query 00:06:00 FinByX Ignore Case Query 00:02:00 FinByX starting with query 00:03:00 FinByX ending with query 00:02:00 FinByX containing query 00:04:00 FinByX less than 00:02:00 FinByX greater than 00:01:00 FinByX between 00:02:00 FinByX and SortByY 00:04:00 Paging and Sorting 00:07:00