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About This Course

Who this course is for:

  • Students who want to learn about Data Science and Machine Learning
  • Data analysts
  • Data engineers
  • Data scientists

What you’ll learn: 

  • Become a professional Data Scientist, Data Engineer, Data Analyst or Consultant
  • How to write complex R programs for practical industry scenarios
  • Learn data cleaning, processing, wrangling and manipulation
  • Learn Plotting in R (graphs, charts, plots, histograms etc)

Requirements: 

  • No prior knowledge is required to take this course

In this practical, hands-on course, you’ll learn how to program in R and how to use R for effective data analysis, visualization and how to make use of that data in a practical manner. You will learn how to install and configure software necessary for a statistical programming environment and describe generic programming language concepts as they are implemented in a high-level statistical language.

The course covers practical issues in statistical computing which include programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Blending practical work with solid theoretical training, we take you from the basics of R Programming to mastery.

Our Promise to You

By the end of this course, you will have learned data science and machine learning in R.

30 Day Money Back Guarantee. If you are unsatisfied for any reason, simply contact us and we’ll give you a full refund. No questions asked.

Get started today and learn more about data science.

Course Curriculum

Section 1 - Data Science and Machine Learning Course Intro
Data Science And Machine Learning Intro Section Overview 00:00:00
What Is Data Science? 00:00:00
Machine Learning Overview 00:00:00
Data Science Plus Machine Learning Marketplace 00:00:00
Who Is This Course For? 00:00:00
Data Science And Machine Learning Job Opportunities 00:00:00
Section 2 - Getting Started with R
Getting Started With R 00:00:00
R Basics 00:00:00
Working With Files 00:00:00
The R Studio 00:00:00
Tidyverse Overview 00:00:00
Additional Resources 00:00:00
Section 3 - Data Types And Structures In R
Data Types And Structures in R Section Overview 00:00:00
Basic Types 00:00:00
Vectors Part One 00:00:00
Vectors Part Two 00:00:00
Vectors: Missing Values 00:00:00
Vectors: Coercion 00:00:00
Vectors: Naming 00:00:00
Vectors: Misc. 00:00:00
Working With Matrices 00:00:00
Working With Lists 00:00:00
Introduction To Data Frames 00:00:00
Creating Data Frames 00:00:00
Data Frames: Helper Functions 00:00:00
Data Frames: Tibbles 00:00:00
Section 4 - Intermediate R
Data Manipulation Section Intro 00:00:00
Relational Operators 00:00:00
Logical Operators 00:00:00
Conditional Statements 00:00:00
Working With Loops 00:00:00
Working With Functions 00:00:00
Working With Packages 00:00:00
Working With Factors 00:00:00
Dates And Times 00:00:00
Functional Programming 00:00:00
Data Import And Export 00:00:00
Working With Databases 00:00:00
Section 5 - Data Manipulation In R
Data Manipulation Section Intro 00:00:00
Tidy Data 00:00:00
The Pipe Operator 00:00:00
The Filter Verb: {dplyr} 00:00:00
The Select Verb: {dplyr} 00:00:00
The Mutate Verb: {dplyr} 00:00:00
The Arrange Verb: {dplyr} 00:00:00
The Summarize Verb: {dplyr} 00:00:00
Data Pivoting: {tidyr} 00:00:00
String Manipulation: {stringr} 00:00:00
Web Scraping: {rvest} 00:00:00
JSON Parsing: {jsonlite} 00:00:00
Section 6 - Data Visualization In R
Data Visualization In R Section Intro 00:00:00
Getting Started With Data Visualization In R 00:00:00
Aesthetics Mappings 00:00:00
Single Variable Plots 00:00:00
Two Variable Plots 00:00:00
Facets, Layering, And Coordinate Systems 00:00:00
Styling And Saving 00:00:00
Section 7 - Creating Reports With R Markdown
Introduction To R Markdown 00:00:00
Section 8 - Building Webapps With R Shiny
Introduction To R Shiny 00:00:00
Creating A Basic R Shiny App 00:00:00
Other Examples With R Shiny 00:00:00
Section 9 - Introduction To Machine Learning
Machine Learning Part One 00:00:00
Machine Learning Part Two 00:00:00
Section 10 - Starting A Career in Data Science
Starting A Data Science Career Section Overview 00:00:00
Creating A Data Science Resume 00:00:00
Getting Started With Freelancing 00:00:00
Top Freelance Websites 00:00:00
Personal Branding 00:00:00
Networking Do’s and Don’ts 00:00:00
Setting Up A Website 00:00:00
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