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Introduction to R for Data Science

Microsoft via edX

This course may be unavailable.


R is rapidly becoming the leading language in data science and statistics. Today, R is the tool of choice for data science professionals in every industry and field. Whether you are full-time number cruncher, or just the occasional data analyst, R will suit your needs.

This introduction to R programming course will help you master the basics of R. In seven sections, you will cover its basic syntax, making you ready to undertake your own first data analysis using R. Starting from variables and basic operations, you will eventually learn how to handle data structures such as vectors, matrices, data frames and lists. In the final section, you will dive deeper into the graphical capabilities of R, and create your own stunning data visualizations. No prior knowledge in programming or data science is required.

What makes this course unique is that you will continuously practice your newly acquired skills through interactive in-browser coding challenges using the DataCamp platform. Instead of passively watching videos, you will solve real data problems while receiving instant and personalized feedback that guides you to the correct solution.


edX offers financial assistance for learners who want to earn Verified Certificates but who may not be able to pay the fee. To apply for financial assistance, enroll in the course, then follow this link to complete an application for assistance.

Note: These courses will retire in June. Please enroll only if you are able to finish your coursework in time.


Section 1: Introduction to Basics
Take your first steps with R. Discover the basic data types in R and assign your first variable.

Section 2: Vectors
Analyze gambling behaviour using vectors. Create, name and select elements from vectors.

Section 3: Matrices
Learn how to work with matrices in R. Do basic computations with them and demonstrate your knowledge by analyzing the Star Wars box office figures.

Section 4: Factors
R stores categorical data in factors. Learn how to create, subset and compare categorical data.

Section 5: Data Frames
When working R, you'll probably deal with Data Frames all the time. Therefore, you need to know how to create one, select the most interesting parts of it, and order them.

Section6: Lists
Lists allow you to store components of different types. Section 6 will show you how to deal with lists.

Section 7: Basic Graphics
Discover R's packages to do graphics and create your own data visualizations.

Taught by

Filip Schouwenaars


4.1 rating, based on 27 Class Central reviews

Start your review of Introduction to R for Data Science

  • HChan
    This course is actually more like half a course, but it does that half extremely well. This class will give you solid foundations in the basic data structures of R, and it does so very efficiently and very well - just a few minutes of lectures and exercises and you've learnt what is needed - demonstrating the effectiveness of Datacamp's platform. There is no coverage on control flow, functions, or vectorized operations, which is needed for an actual working knowledge of R. I believe the intention is for you to continue your education at Datacamp, but at the moment of writing that is not free.
  • Profile image for Bob Blackburn
    Bob Blackburn
    This is a very useful introduction to R. I was taking the DAT203X - Data Science and they suggested to take an R or Python to follow along better. It was well worth it. You get a basic understanding of data structures, operators, and basic graphing.

    The labs are done through DataCamp to practice what you just learned. They may seem slow at first; but, they incrementally build on what you know to extend your skill set.
  • A good course overall especially for novices. Material is very basic and covers only simplest data-structures and graphics. Course designed for those who can not program at all. But if you have some programming experience I would rather recommend coursera course about R.
  • Anonymous
    This is an extremely helpful course to get you started in R. The most helpful part to me is the interactive programming exercise. I think they are highly organized and delicately targeted towards different knowledge points.
  • This course can be a very good place to start learning R programming. Only because of one ambiguous assignment I am not giving it 5 stars but I highly recommend this course to anyone who wants to start R programming.
  • Oliver Mcmanus
  • Anonymous
    I'm going to flunk out. Way too difficult; the lab exercises are incomprehensible. I'm not a complete idiot, having published a couple of papers in the journal Science.
  • Chandan
  • Rafal Dobrzynski
  • Ma Junyi
  • ATINDOGBE Gilbert
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