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26

Clean Code with Java Examples

Learn Useful Software Principles That You Can Use in Your Everyday Programming

By Liviu Oprisan | in Online Courses

Bad code practices can make development very slow on your medium and big projects. This means a lot of money is wasted on development time. These types of projects are also remarkably unpleasant to work on. This course is about useful clean code principles. It will teach you concepts that you can use every time you write code. The course has Java examples and sometimes mentions things like Spring and Lombok. The same clean code principles also apply to PHP, C#, and Python.

4.6/5 average rating: ★ ★ ★ ★

  • Access 26 lectures & 2 hours of content 24/7
  • Learn how to write clean code
  • Keep your functions small
  • Name your variables, functions, & classes
  • Know how many parameters a function should have
  • Know what to do about boolean, nullable, & return parameters
  • Understand how clean code affects MVC
  • Learn the difference between an OOP object & a Data Structure Object
Liviu Oprisan | Developer
4.2/5 Instructor Rating: ★ ★ ★ ★
Liviu has been a programmer for 13 years. He has 10 years of PHP experience, 2 years of Java and C#, a year of Python, and nobody is safe from JavaScript. He’s also a big fan of Scrum and how you can build motivated, happy teams around it. Liviu has worked on a lot of types of projects, from small to medium projects where he was the only developer to medium and big projects in teams of 30+ people.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Know the basics of writing code (in a language like Java, PHP, Python, or C#)

Course Outline

  • Your First Program
  • Introduction
    • 1. Clean code - From art to science - 2:48
    • 2. What people say - 4:39
    • 3. Sample code - 3:03
    • 4. Empathy - 3:12
  • Clan code: Small functions
    • 5. Small functions are easy to understand - 18:15
    • 6. More advantages of small functions - 8:17
    • 7. How many lines should a function have - 3:34
    • 8. Single Responsibility Principle - 1:33
    • 9. Levels of Abstraction - An MVC discussion - 11:42
    • 10. High Level First - write code using method calls - 3:13
  • Clean code: Fundamental principles
    • 11. Names - for methods, classes and variables - 7:27
    • 12. Parameters - 4:37
    • 13. Programming line length - 1:52
    • 14. Beautify predicates - 1:11
    • 15. Comments - 4:30
    • 16. Should I use exceptions - 0:41
  • Clean Code: Advanced principles
    • 17. OOP Objects vs Data Structure Objects - 4:38
    • 18. Composition over Inheritance - 5:16
    • 19. Symptoms of Bad Code - 2:03
    • 20. What is state in programming and why is it important - 5:40
    • 21. Low Coupling, High Cohesion - 7:43
    • 22. Command and Query Separation, Tell Don't Ask and The Law of Demeter - 5:22
    • 23. Test Pyramid and Test Driven Development - 3:50
    • 24. Is over-engineering a solution to bad code (spoiler - it isn’t) - 3:02
    • 25. YAGNI - 1:51
    • z. project - 2:03

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26

Clean Code with PHP Examples

Become a Better PHP Developer, Write Easy-to-Maintain Code, & Do Better at Interviews

By Liviu Oprisan | in Online Courses

There's no point wasting our time with useless knowledge that would seem like it's helping you as a programmer, but that you will forget in 2 weeks. This course discusses what clean code is and why it’s important. It will cover small functions, fundamental to advanced principles, and more. The course has PHP examples and mentions things like Symfony and MVC. If you invest as little as two hours of your life in this course, about the time you will spend watching a movie; you will become a much better developer.

4.2/5 average rating: ★ ★ ★ ★

  • Access 26 lectures & 1 hour of content 24/7
  • Know what clean code is & why it’s important
  • Discuss small functions
  • Learn about fundamental things like method & variables, names, parameters, comments, and exceptions
  • Explore more abstract topics like the difference between an OOP object & a Data Structure Object
Liviu Oprisan | Developer
4.2/5 Instructor Rating: ★ ★ ★ ★
Liviu has been a programmer for 13 years. He has 10 years of PHP experience, 2 years of Java and C#, a year of Python, and nobody is safe from JavaScript. He’s also a big fan of Scrum and how you can build motivated, happy teams around it. Liviu has worked on a lot of types of projects, from small to medium projects where he was the only developer to medium and big projects in teams of 30+ people.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: all levels
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Know the basics of writing code in PHP

Course Outline

  • Introduction
    • Intro - 2:07
    • Clean code - From art to science - 2:48
    • What people say - 4:39
    • Looking at some code - 2:38
    • Empathy - 3:12
  • Clean code: Small functions
    • Small functions are easy to understand - 18:15
    • More advantages of small functions - 7:20
    • How many lines should a function have - 3:34
    • Single Responsibility Principle - 1:33
    • Levels of Abstraction - An MVC discussion - 11:42
  • Clean code: Fundamental principles
    • Names - for methods, classes and variables - 6:39
    • Parameters - 4:10
    • Programming line length - 1:47
    • Beautify predicates - 1:03
    • Comments - 4:13
    • Should I use exceptions - 0:32
  • Clean code: Advanced principles
    • OOP Objects vs Data Structure Objects - 4:20
    • Composition over Inheritance - 5:16
    • Symptoms of Bad Code - 2:03
    • What is state in programming and why is it important - 5:34
    • Low Coupling, High Cohesion - 7:27
    • Command and Query Separation, Tell Don't Ask and The Law of Demeter - 4:34
    • Test Pyramid and Test Driven Development - 3:50
    • Is over-engineering a solution to bad code (spoiler - it isn’t) - 3:02
    • YAGNI - 1:51
    • Project - 2:03

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53

How to Learn Programming & Become a Programmer

Learn Python, Java, C#, PHP, & JavaScript

By Liviu Oprisan | in Online Courses

Programming is fun, and you can do great things with it. It also pays rather well, and there are lots of programming jobs available. But programming is a big universe, and for someone considering becoming a software developer, it can be overwhelming and confusing. So the course is for people who know nothing or very little about programming and are interested in becoming programmers. It covers some programming domains like back-end and front-end programming, microservices, APIs, machine learning, and Big Data.

3.9/5 average rating: ★ ★ ★ ★ ★

  • Access 53 lectures & 3 hours of content 24/7
  • Learn a programming language in 5 steps
  • Know 9 things you can do to get a job as a programmer
  • Use variables, operators, functions, & objects in Python, Java, C#, PHP and Javascript
  • Learn what variables, operators, functions, & objects are
  • Understand what programming frameworks are & why they are important
Liviu Oprisan | Developer
4.2/5 Instructor Rating: ★ ★ ★ ★
Liviu has been a programmer for 13 years. He has 10 years of PHP experience, 2 years of Java and C#, a year of Python, and nobody is safe from JavaScript. He’s also a big fan of Scrum and how you can build motivated, happy teams around it. Liviu has worked on a lot of types of projects, from small to medium projects where he was the only developer to medium and big projects in teams of 30+ people.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: beginner
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Know the basics of writing code in PHP

Course Outline

  • Introduction
    • Introduction - 2:22
  • How to learn programming and become a programmer
    • 5 steps to learn a programming language - 4:02
    • 9 things you can do to get a job as a programmer - 10:07
    • 4 jobs in software development that don't require you to know programming - 3:02
  • Coding for beginners - Python
    • Python Variables - 7:15
    • Python Operators - 2:08
    • Python control structures - If - 9:32
    • Python control structures - While - 10:13
    • Python control structures - For - 4:04
    • Python Functions - 5:19
    • Python Objects - 14:40
  • Coding or beginners - PHP
    • PHP Variables - 4:04
    • PHP Operators - 2:12
    • PHP control structures - If - 6:03
    • PHP control structures - While - 6:08
    • PHP control structures - For - 2:34
    • PHP Functions - 4:15
    • PHP Objects - 7:48
  • Coding for beginers - Java
    • Java Variables - 4:12
    • Java Operators - 0:55
    • Java If - 6:28
    • Java While - 4:04
    • Java For - 2:45
    • Java Functions - 8:42
    • Java Objects - 4:30
  • Coding for beginners - C#
    • C# Variables - 2:47
    • C# Operators - 1:05
    • C# If - 3:23
    • C# While - 1:38
    • C# For - 1:37
    • C# Functions - 4:15
    • C# Objects - 6:34
  • Coding for beginners - JavaScript
    • JS Variables - 2:52
    • JS Operators - 0:42
    • JS If - 3:14
    • JS While - 1:54
    • JS For - 1:31
    • JS Functions - 3:39
    • JS Objects - 5:43
  • Coding exercises
    • Coding Exercises - 3:19
    • Which language did you like most - 0:52
    • 6 good resources to continue learning programming - 2:42
    • 2 good resources to teach children programming - 1:09
  • Programming frameworks
    • Programming frameworks introduction and MVC - 2:53
    • Java frameworks - 2:11
    • Python frameworks - 0:58
    • PHP frameworks - 3:12
    • C# frameworks - 1:01
    • JavaScript frameworks - 5:31
  • How to choose a programming language
    • Python vs Java vs PHP vs C# vs JavaScript by purpose - 17:55
    • Python vs Java vs PHP vs C# vs JavaScript by syntax - 4:05
    • Python vs Java vs PHP vs C# vs JavaScript popularity and salaries - 2:36
    • Python vs Java vs PHP vs C# vs JavaScript personal opinion - 1:30

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Lessons
16

Java Programming & Coding Language for Beginners

Walk Through the Basics of Java Programming, From Classes to Streams, ArrayList, & More

By Abdullah Yildiz | in Online Courses

This course is created for complete beginners. You will learn the basics and structures of this wonderful programming language, one of the best and most popular programming languages. It will walk you through the basics and everything. Learning Java is easier than you think! Your journey to coding starts here.

  • Access 16 lectures & 2 hours of content 24/7
  • Get started with JavaScript
  • Learn Java basics & structures
  • Explore Classes, Functions, Conditions, & Loops
  • Work with Streams, ArrayList, & JDBC
Abdullah Yildiz |Online Business Professional
4.4/5 Instructor Rating: ★ ★ ★ ★
Abdullah Yildiz is a passionate motion designer, with years of experience in graphic design, digital marketing, and freelancing. His goal is to make down-to-the-point courses that are easy to learn. His biggest motivation is to help students acquire skills that are useful in their careers and business.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: beginner
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Any device with basic specifications

Course Outline

  • Course Content
    • Course intro (1) - 0:47
    • Course intro (2) - 0:43
    • Lecture 01-Introduction to Java - 13:13
    • Lecture 02-Input and Wrapper classes - 12:52
    • Lecture 03-Command Line Arguments, OptionPane and Static Functions - 9:47
    • Lecture 04-Conditions and Loops - 16:20
    • Lecture 05-Inheritance, Overloding and Overriding - 14:02
    • Lecture 06-Abstract Classes - 6:09
    • Lecture 07 -Package part 1 - 1:49
    • Lecture 07 -Package part 2 - 1:09
    • Lecture 08-Package part 3 - 3:40
    • Lecture 09-Exceptions Part 1 - 10:34
    • Lecture 10-Exceptions Part 2 - 10:00
    • Lecture 11-Streams - 12:41
    • Lecture 12-ArrayList - 4:07
    • Lecture 13-Java Database Connectivity(JDBC) - 7:56

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23

Julia Programming Language: From Zero to Expert

Learn the Next Gen Language for Data Science, Machine Learning, & Numerical Computing

By Mohammad Nauman | in Online Courses

You don't want to miss out on the latest trend and the tool of the future! Right now, that tool is the Julia programming language. It's the hot new language that all ML and data science experts are very excited about. Learning Julia will open up several doors for you in your career! That is the objective of this course: to give you a strong foundation needed to excel in Julia and learn the core of the language and the applied side in the shortest amount of time possible. You will see how you can start using Julia to excel in your current job without moving the whole stack to Julia immediately.

5.0/5 average rating: ★ ★ ★ ★ ★

  • Access 23 lectures & 3 hours of content 24/7
  • Learn the syntax of Julia & its differences from Python
  • Know the strength of Julia in terms of data science & machine learning
  • Understand data science case studies including analysis & clustering
  • Create ML models from scratch in a way that helps you make modifications easily
Mohammad Nauman | PhD, Programmer, Researcher, Designer, & Teacher
4.4/5 Instructor Rating: ★ ★ ★ ★
Mohammad Nauman has a Ph.D. in Computer Sciences and a PostDoc from the Max Planck Institute for Software Systems. He has been programming since early 2000 and has worked with many different languages, tools, and platforms. He has extensive research experience with many state-of-the-art models to my name. His research in Android security has led to some major shifts in the Android permission model.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: beginner
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Basic understanding of programming
  • Python would be helpful but not necessary
  • Understanding of basic Data Science (reading CSVs etc) would be helpful
  • Understanding of basic concepts of Deep Learning (such as classification) would be useful

Course Outline

  • Intro and Setup
    • Installing Julia (Windows, Linux and MacOS) - 3:14
    • Packages and Interactive Notebook - 4:56
  • Core Langauge Basics (Plus Differences from Python)
    • Basic Syntax, Variables and Operations - 11:14
    • Control Structures, Iterations and Ranges - 4:23
    • Data Structures in Julia: Lists/Arrays, Tuples, Named Tuples - 9:49
    • Dictionaries (Maps), Symbols in Julia - 3:47
  • Arrays and Matrices: Native Language Support
    • Arrays, Matrices, Tensors, Reshaping, Helper Functions - 16:07
    • Data Type Details, Casting Among Types - 2:53
  • Functions and Fun Stuff
    • Defining Functions, Overloading, Multiple-Dispatch - 12:19
    • Anonymous Functions (and their importance), Splatting and Slurping - 9:43
    • Functional Programming, Broadcasting - Most Important Concept in Julia - 11:05
    • Interfacing with Python and R - 4:09
  • Getting Started with Data Science
    • Plotting Basics - Prettier Julia Plots - 5:15
    • Data Wrangling, Reading CSV Files, Descriptive Case Study - 14:07
    • Further Data Manipulation, Apache Arrow, Grouping and analysis - 19:18
  • Case Studies in Data Science
    • Case Study: Clustering for Housing/Map Data - 10:23
    • Classification with Decision Trees/Random Forests - 9:54
  • Deep Learning - Flux in Julia
    • Writing a Neural Network from Scratch in a Few Lines - 16:04
    • Multiple Layers, State-of-the-Art in a Few More Lines - 7:35
    • Case Study: MNIST, Modifying Data for Model, Avoiding Pitfalls - 14:14
    • MNIST Continued, Creating the Deep Model, Training and Testing - 9:02
    • Saving and Loading Models, Exploring More Options - 5:38
  • Parting Words
    • Where to Go from Here: Pointers for Further Learning - 3:56

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4.0 hours
Lessons
55

Learn to Create WebXR, VR & AR, Experiences Using Three.JS

Harness the WebXR API and Three.JS to bring immersive experiences, VR and AR, to the browser, including Oculus Quest

By Nik Lever | in Online Courses

WebXR brings both VR and AR to the browser. The API has the massive forces of Google and Amazon behind it, so it looks like immersive experiences, using only a browser, will make a huge impact shortly. You'll learn how easy it is in this course, using Three.JS to create VR and AR experiences. To get the best from the course, you will need to be comfortable with JavaScript coding. However, no other prerequisites are required. The course includes comprehensive resources and code examples. Each code example comes with two versions, one for you to code and another that is complete in case you hit a problem.

4.6/5 average rating: ★ ★ ★ ★

  • Access 55 lectures & 4 hours of content 24/7
  • Easily create VR & AR apps that work in the browser
  • Use the ThreeJS library to create these apps
  • Handle controllers
  • Replace the controllers with custom models
  • Create architectural walk-throughs, games & training apps
  • Add an in world UI
  • Develop AR apps that support real world hit testing
  • Learn game VR techniques such as teleporting & interacting with models in the scene using controllers
Nicholas Lever | Game Developer
4.4/5 Instructor Rating: ★ ★ ★ ★
After getting a degree in Graphic Design, Nik started work in 1980 as a cartoon animator. He developed a few games using Sinclair Basic and then Assembler. He developed more games in the shareware market. Since then the majority of his working life has been creating games, first in Flash and Director, as Director published the first widely available 3D library that would run in a browser using a plugin. Nik has worked for the BBC. Johnson and Johnson. Deloitte, Mars Corporation and many other blue chip clients. The company he’s run for over 30 years has won a number of awards and been nominated for a BAFTA twice, the UK equivalent to the Oscar.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: intermediate
  • Have questions on how digital purchases work? Learn more here

Requirements

  • An intermediate level JavaScript ability is assumed

Course Outline

  • Introduction
    • Welcome to the course - 3:40
    • Setting up a secure web server
    • The history of WebXR - 6:09
    • A 3D Primer - 5:11
    • The THREE.js Examples - 6:14
    • What have you learned?
  • A THREE.js Primer
    • Setting up a simple THREE.js page using modules - 6:11
    • Creating a rotating Cube - 9:59
    • The THREE.js editor - 7:36
    • Geometries - 6:30
    • Materials - 5:54
    • Loaders - 6:45
    • What have you learned?
  • Introducing WebXR with THREE.js
    • Converting a standard THREE.js web app to use WebXR - 5:24
    • Customising the VRButton - 11:38
    • Adding user interaction with a controller device - 7:40
    • Adding a button press event to the controller - 9:18
    • Creating a custom controller - 6:40
    • Accessing the gamepad - 9:57
    • Moving around a VR world - 8:13
    • Physics in your VR world - 12:17
    • Creating an AR experience - 6:41
    • Controllers in an AR app - 7:17
    • Touch gestures in an AR app - 10:17
    • Hit testing in AR - 9:29
    • Hand-tracking on the Oculus Quest - 6:15
    • What have you learned?
  • Using complex assets with THREE.js
    • Finding assets online - 6:18
    • Using Mixamo to setup character animations - 2:54
    • Using Blender to export assets - 3:59
    • Using the THREE.js animation system - 7:38
    • What have you learned?
  • The WebXR API
    • XRSystem and XRSession - 3:55
    • XRFrame and XRSpace - 3:25
    • XRInputSource - 5:10
    • What have you learned?
  • Creating an architectural walk-through
    • Moving around an environment - 6:22
    • Interacting with objects in the scene - 6:58
    • Using a GazeController - 3:23
    • Making the app functional on a none XR device - 4:02
    • What have you learned?
  • Fun with AR
    • Using AR to create a tape measure - 10:58
    • Adding dynamic animation to a product - 4:15
    • An AR online shop example - 8:12
    • What have you learned?
  • A VR Game
    • Movement by teleporting - 6:22
    • Interacting with meshes - 5:49
    • Using a controller as a weapon - 8:59
    • What have you learned?
  • VR for training
    • CanvasUI - part 1 - 6:17
    • CanvasUI - part 2 - 3:37
    • Using CanvasUI for training - 8:52
    • What have you learned?
  • Conclusion
    • WebXR examples, articles, videos and repos to inspire and inform you
    • Bonus Lecture

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64

Automate Tasks Using MATLAB

Improve Your Productivity by Automating Repeated Tasks

By Nouman Azam | in Online Courses

You may have been doing programming for a long time, but you may not have seen the fun part of it. This course covers the fun part of programming, which is to automate the boring and tiring jobs you do regularly on computers. This course is about automating repeated tasks and other similar tasks by letting the computer do it for you. This course is designed for people who aim to write small programs that do practical tasks by themselves. The course skips complicated computer science concepts and focuses on writing code that gets the job done.

New course

  • Access 64 lectures & 12 hours of content 24/7
  • Automate various tasks on your computer using MATLAB programs
  • Generate word documents, reports, PDFs & PowerPoint presentations
  • Write programs that send out email notifications & take screenshots
  • Control the mouse & keyboard programmatically to do tasks automatically
  • Search the web for retrieving some textural information
  • Learn the MATLAB debugging feature to quickly figure errors in the code
Nouman Azam | MATLAB Professor
4.3/5 Instructor Rating: ★ ★ ★ ★
Dr. Nouman Azam is an Assistant Professor in Computer Science. He teaches online courses related to MATLAB programming to more than 10,000 students on different online platforms. The focus of his courses is to explain different aspects of MATLAB and how to use them effectively in routine daily life activities. In his courses, students will find topics such as MATLAB programming, designing GUIs, data analysis, and visualization.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: beginner
  • Have questions on how digital purchases work? Learn more here

Requirements

  • Basic knowledge of MATLAB
  • MATLAB software

Course Outline

  • Introduction
    • Introduction to the course - 2:48
  • Controlling Mouse and Keyboards to Automate Tasks
    • code and data
    • 1 - Writting to NotePad and then deleting the stuff - 14:47
    • 2 - Saving files and opening up google page - 28:01
    • 3 - Drawing a tiger using mouse and keyboard - 26:04
  • Emails, Web and Running Scripts from Command Window
    • code and data
    • 1 - Sending email, opening a webpage and executing system commands - 21:14
    • 2 - Automating emails and other tasks using batch Files - 21:52
    • 3 - Automatic screen shots and sending them as emails - 18:56
  • Automatically Moving Deleting and Browsing Files
    • code and data
    • 1 - Automating Directory Interactions - 12:02
    • 2 - Automating Directory Interactions_(Part 2) - 9:48
    • 3 - Finding files and arraning them in folder structures - 16:33
  • Exceptions, assert, startup, finish, diary
    • code and data
    • 1 - Automating programs to work during errors - 15:26
    • 2 - Finish and Startup functions for loading preferences - 14:15
    • 3 - Automating program checking and logging - 11:15
  • Automate user interactions
    • code and data
    • 1 - Automating file interactions - 12:47
    • 2 - Automatic arrangment of pictures based on years - 16:48
    • 3 - Automate user interactions - 17:58
  • Code debuggging and analyzer
    • code and data
    • 1 - Automate code inspection - 22:44
    • 2 - Common Errors - 17:30
  • Textual processing for automatic summarization of contents
    • code and data
    • 1- Summaring Textual Contents using Word Cloud - 18:37
    • 2- Summaring Textual contents using topic models - 28:37
    • 3 - Read the html from a google search - 26:27
  • Excel and MATLAB
    • 1 - Installing Spread Sheet link - 7:22
    • 2 - Data Transfer Between Excel and MATLAB - 7:25
    • 3 - Using MATLAB Function and Features from Excel - 7:04
  • Generating ppts, word documents and pdfs
    • code anda data
    • 1 - Creating Presentations - 21:42
    • 2 - Adding Figures, Tables to ppt - 18:20
    • 3 - Generating Word Documents - 24:44
    • 4 - Adding Figures and Tables to Word Documents - 16:45
  • Regular expressions for automating information extraction
    • 1 - Automating the text matching_DF - 12:04
    • 1 - Executing Regular Expressions on Matlab, literals and meta characters - 14:41
    • 2 - The dot metacharacter - 7:01
    • 3 - Character Class Basics - 9:11
    • 3 - Some exceptions with a character class - 6:48
    • 4 - Exclusion of characters using a character class - 10:55
    • 5 - Shorthand for character classes - 6:27
    • 6 - Staring and Ending Anchors - 5:51
    • 7 - Examples of starting and ending Anchors - 4:53
    • 8 - word boundaries (part 1) - 10:09
    • 9 - word boundaries (part 2) - 9:23
    • 10 - Quantifiers - 12:00
    • 11 - Limited Repetition - 8:13
    • 12 - Lazy and Greedy Quantifiers - 11:00
    • 13 - Understanding the Grouping - 9:56
    • 14 - Non-capturing groups - 7:23
    • 15 - Effect of Quantifiers on Groups - 8:38
    • 16 - Alternation - 5:37
    • 17- Look Ahead Assertions - 10:20
    • 18 - Look behing Assertions - 5:48
    • 19 - Backreferencing - 11:21
    • 20 - Named Capturing Groups and Backreferencing - 9:27
    • 21 - Conditions (if then else) - 7:12
    • 23 - Examples 1- Character Ranges - 16:31
    • 24 - Example 2 - Password Checking Example - 11:09
    • 25 - Example 3 - IP Addresses - 8:30
    • 26 - Example 4 - Matching a valid date - 10:46
    • Code and Data

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29.0 hours
Lessons
158

Data Science Master Class with Live Projects & Case Studies

Build Your Data Science Career with 4 Capstone Projects & 8+ Case Studies

By Oindrilla | in Online Courses

The answer to why data science is a must-have nowadays will take a lot of time to explain. Let's look into the company name that is using data science and machine learning; then, you will get the idea of how it boosts your salary if you have in-depth knowledge in data science and machine learning! This course will teach you what data science is and how it helps the modern world. You’ll see its benefits too. It will cover 4 capstone projects and 8+ case studies.

4.8/5 average rating: ★ ★ ★ ★

  • Access 158 lectures & 29 hours of content 24/7
  • Learn what data science is & how it’s helping the modern world
  • Know the benefits of Data Science, Machine Learning, & Artificial Intelligence
  • Solve data science related problem with the help of R programming
  • Understand why R is a must-have for Data Science, AI, & Machine Learning
  • Know how to switch career in Data Science
  • Work with R’s conditional statements, functions, & loops
UpDegree | New Skills Everyday!
UpDegree is a group of IT skilled people having sound technical knowledge on various IT domains. They work for different MNCs including Microsoft, IBM, CISCO, eBay, Amazon, Flipkart, and a lot of startups also. UpDegree teaches you practical hands-on computer skills that you need for a Job in the IT sector. Less theory and more practical! Learn through examples and step by step.

Important Details

  • Length of time users can access this course: lifetime
  • Access options: desktop & mobile
  • Redemption deadline: redeem your code within 30 days of purchase
  • Experience level required: beginner
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Requirements

  • Any device with basic specifications

Course Outline

  • INTRODUCTION TO DATA SCIENCE
    • Trainer Introduction - 2:33
    • Introduction to Business Analytics - 13:38
    • Application of Business Analytics - 12:23
    • Introduction to Machine Learning - 12:31
    • How to switch your career into ML - 14:56
    • How to switch your career into ML #2 - 3:31
  • INTRODUCTION TO R
    • Introduction to R - 15:07
    • Setting up R - 11:08
  • R Programming
    • R Operator - 13:45
    • R Loop - 12:02
    • R Function - 13:20
    • R Function #2 - 10:10
    • R Function part #3 [ Writing Function ] - 5:57
  • R Data Structure
    • An Introduction of R Data Structure + Vector - 11:13
    • Matrix, Array and Data Frame - 14:37
    • A Deep Drive to R Data Frame - 13:02
    • Factor - 4:12
    • List - 6:19
  • Import and Export in R
    • Import CSV Data in R - 9:26
    • Import Text Data in R - 3:19
    • Import Excel, Web Data in R - 12:47
    • Export Data in R - Text - 4:07
    • Export Data in R - CSV & Excel - 2:37
  • Data Manipulation
    • Data Manipulation - Apply Function - 13:15
    • Data Manipulation - select - 11:46
    • Data Manipulation - mutate - 14:28
    • Data Manipulation - filter - 14:11
    • Data Manipulation - arrange - 9:38
    • Data Manipulation - Pipe Operator - 11:26
    • Data Manipulation - group by - 8:30
    • Data Manipulation - Date - 10:35
  • Data Visualization
    • Scatter Plot - 12:01
    • mfrow - 7:37
    • pch - 12:30
    • Color - 1:19
    • Line Chart - 3:21
    • Bar Plot - 7:05
    • Pie Chart - 6:43
    • histogram - 7:06
    • Density Plot - 2:26
    • Boxplot - 5:01
    • Mosaic Plot - 7:59
    • 3D Plot - 10:39
    • Correlation Plot and Word Cloud - 9:02
    • ggplot 2-part1 - 14:03
    • ggplot2-part2 - 8:08
    • ggplot2-part3 - 15:04
  • Introduction To Statistics
    • Intro to Statistics-Part 1 - 13:25
    • Intro To Statistics -Part2 - 8:53
    • Intro To Statistics - Part 3 - 14:55
    • Intro To Statistics -part4 - 4:15
    • Intro To Statistics -Part 5 - 15:09
    • Intro To Statistics -Part 6 - 8:21
    • Intro To Statistics-Part7 - 15:04
    • Intro To Statistics-Part8 - 10:45
    • Intro To Statistics - Part9 - 10:24
    • Intro To Statistics-Part 10 - 14:34
    • Intro To Statistics -Part11 - 7:11
  • HYPOTHESIS Testing
    • Hypothesis Testing Intro -Part 1 - 10:08
    • Hypothesis Testing Intro -Part 2 - 11:28
    • Hypothesis Testing Intro -Part 3 - 14:21
    • Hypothesis Testing Intro -Part 4 - 6:01
  • Hypothesis Testing in Practice
    • Hypothesis Testing in Practice-Part 1 - 15:04
    • Hypothesis Testing in Practice-Part2 - 9:36
    • Hypothesis Testing In Practice - Part3 - 14:16
    • Hypothesis Testing in Practice -Part4 - 12:36
    • Hypothesis Testing in Practice -Part5 - 10:29
    • Hypothesis Testing in Practice -Part6 - 13:46
    • Chi Square -Part1 - 11:19
    • Chi Square -Part2 - 14:57
    • ANOVA-Part 1 - 12:30
    • ANOVA-Part 2 - 14:20
    • Chapter Summary of Hypothesis Testing - 5:08
  • Machine Learning Toolbox
    • Machine Learning Toolbox - Part1 - 14:00
    • Machine Learning Toolbox - Part2 - 12:31
  • Business Use Case Understaing
    • Business Case Understanding - 12:51
  • Data Pre-processing
    • Data Pre-Processing 1 - 14:45
    • Data Pre-Processing 2 - 14:29
    • Data Pre-Processing 3 - 10:25
    • Data Pre-Processing 4 - 9:39
    • Data Pre-Processing 5 - 12:33
    • Data Pre-Processing 6 - 7:19
    • Data Pre-Processing 7 - 15:04
  • Supervised Learning: Regression
    • Linear Regression #1 - 11:47
    • Linear Regression #2 - 14:23
    • Linear Regression #3 - 20:21
    • Linear Regression #4 - 19:02
    • Linear Regression #5 - 25:00
    • Linear Regression #6 - 15:02
    • Linear Regression #7 - [Part 1] - 14:31
    • Linear Regression #7 - [Part 2] - 13:44
    • Linear Regression #8 - 12:52
    • Linear Regression #9 - 16:03
    • Dummy Variable - 12:34
    • Non Linear Regression - 10:55
  • Logistic Regression
    • Classification Overview - 13:29
    • Logistics Regression Intuition - 14:04
    • R Code Implementation - [Part 1] - 5:09
    • R Code Implementation - [Part2 ] - 10:37
    • Model Evaluation - 12:28
    • Telecom Churn Case Study - 22:27
    • Chapter Summary - 6:41
  • K-NN
    • KNN Intuition - 13:26
    • KNN Case Study - 13:59
    • KNN R Code Implementation - 12:48
  • SVM
    • SVM Intuition - 8:44
    • SVM R Code Implementation - 8:22
    • SVM Model Tuning - 9:00
    • SVM Telecom Case Study - 7:56
    • SVM Pros and Cons - 7:27
    • SVM Chapter Summary - 3:49
  • Naive Bayes
    • Naive Bayes Intuition - 19:56
    • Naive Bayes R Code Implementation - 8:25
    • Naive Bayes Case Study - 8:25
  • Decision Tree
    • Decision Tree Intuition - 14:54
    • How Decision Tree works - 7:40
    • Decision Tree R Code Implementation - 13:44
    • Decision Tree Pruning - 15:36
    • Decision Tree Case Study - 14:24
  • Random Forest
    • Random Forest Intuition - 11:37
    • Random Forest R Code Implementation - 5:13
    • Random Forest Case Study - 10:02
  • Capstone Project - Titanic Survival Project
    • Introduction to the Titanic Survival Project - 11:03
    • Capstone Project - Data Understanding - 5:10
    • Capstone Project - Lazy Predictor - 7:20
    • Capstone Project - Data Preparation - 6:12
    • Capstone Project - Data Exploration - 4:34
    • Capstone Project - Feature Engineering - 7:33
    • Capstone Project - Logistics Regression - 16:10
    • Capstone Project - Logistics Regression #2 - 4:06
    • Capstone Project - Decision Tree - 18:28
    • Capstone Project - Random Forest - 3:06
  • K-Mean Clustering
    • Unsupervised Learning Introduction - 6:12
    • K-Mean Clustering Intuition - 19:40
    • K-Mean Clustering -R Code Implementation - 15:28
    • K-Mean Clustering -Case Study - 12:04
  • Hierarchical Clustering
    • Hierarchical Clustering Intuition - 12:44
    • Hierarchical Clustering - R Code Implementation - 16:49
    • Hierarchical Clustering - Case Study - 7:35
  • DBScan Clustering
    • DBScan Clustering - Case Study - 8:59
    • DBScan Clustering - Intuition and R Code - 11:37
  • Principal Component Analysis [ PCA ]
    • PCA Intuition - 12:05
    • PCA R Code Implementation - 16:35
    • PCA Case Study - 16:39
  • Association Rule Mining
    • Association Rule Mining -Introduction - 16:16
    • Association Rule Mining - Pre-Processing - 7:14
    • Association Rule Mining -R Code Implementation - 10:56
    • Association Rule Mining - Case Study - 18:16
  • Capstone Project - Big Mart
    • Big Mart Sale - Data Structure - 15:23
    • Big Mart Sale - Univariate Analysis - 11:14
    • Big Mart Sale - Bi-Variate Analysis - 7:15
    • Big Mart Sale - Fetature Engineering - 13:21
    • Big Mart Sale - Pre-Processing - 19:08
    • Big Mart Sale - Model Building & Evaluation - 18:18
  • Model Deployment
    • Model Deployment - Workflow - 10:01
    • Model Deployment - Pre Requisite - 9:39
    • Model Deployment - Steps To Follow - 19:07
    • Model Deployment - Azure ML DEMO - 11:06

View Full Curriculum



Terms

  • Unredeemed licenses can be returned for store credit within 30 days of purchase. Once your license is redeemed, all sales are final.