R Programming for Data Science and Machine Learning

Learn R programming to implement Machine Learning & DS algorithms

R is one of the most popular and widely used tools for statistical programming.
It is a powerful, versatile, and easy to use tool for data analytics, and data visualization.
It is the first choice for thousands of data analysts working in both companies and academia.

What you’ll learn

  • Learn to program in R at a good level and how to use R Studio.
  • Learn the core principles of R programming.
  • Learn how to create vectors in R.
  • Learn how to create variables.
  • Learn Data types in R.
  • Decision Making in R.
  • Learn how to build and use matrices in R.
  • Learn how to use Functions in R.
  • Learn to use Factors in R.
  • Learn to use Data Frames in R.
  • Learn how to install packages in R.
  • Learn how to use charts and Graphs in R.
  • Learn to read data from CSV files, Excel files.
  • Learn Data Analysis in R.
  • Learn how to use charts & graphs in R for data visualization.

Course Content

  • Introduction –> 25 lectures • 3hr 40min.

R Programming for Data Science and Machine Learning

Requirements

  • Basic Understanding of Computers.
  • Keen to learn R.

R is one of the most popular and widely used tools for statistical programming.
It is a powerful, versatile, and easy to use tool for data analytics, and data visualization.
It is the first choice for thousands of data analysts working in both companies and academia.

This course will help you master R programming, as a first step to become a skilled R data scientist.

Got the R knowledge and not sure what to do with it?

This is your chance to finally get your hands dirty with the R and data science.
Each lecture will you give you the tools to achieve the following two goals:

Analyze your data using R.
Find the right visualization for your data using various R tools and packages taught in the course.
Draw some useful visualization and find some useful predictions based on the outcome.
It will take just a few lines of code to get you started.

For an added challenge, repeat the same analysis for your own data.
Share your progress for a chance to receive feedback and suggestions.
That way, this feedback can come not just from me but also from other students in the course.

 

 

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