Intro

# edX: Introduction to R for Data Science

with  Filip Schouwenaars

This course is part of the Microsoft Professional Program Certificate in Data Science.

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.

Enjoy!

## Syllabus

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.

Section 6: 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.
28 Student
reviews
Cost Free Online Course
Pace Self Paced
Subject Data Science
Institution Microsoft
Provider edX
Language English
Certificates \$99 Certificate Available
Hours 2-3 hours a week
Calendar 4 weeks long

Disclosure: To support our site, Class Central may be compensated by some course providers.

##### FAQ View All
What are MOOCs?
MOOCs stand for Massive Open Online Courses. These are free online courses from universities around the world (eg. Stanford Harvard MIT) offered to anyone with an internet connection.
How do I register?
To register for a course, click on "Go to Class" button on the course page. This will take you to the providers website where you can register for the course.
How do these MOOCs or free online courses work?
MOOCs are designed for an online audience, teaching primarily through short (5-20 min.) pre recorded video lectures, that you watch on weekly schedule when convenient for you.  They also have student discussion forums, homework/assignments, and online quizzes or exams.

## Reviews for edX's Introduction to R for Data Science 4.2 Based on 28 reviews

• 5 stars 46%
• 4 stars 36%
• 3 stars 11%
• 2 star 4%
• 1 star 4%

Did you take this course? Share your experience with other students.

• 1
3.0 2 years ago
by completed this course, spending 2 hours a week on it and found the course difficulty to be very easy.
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.
1 person found
3.0 2 years ago
by completed this course, spending 1 hours a week on it and found the course difficulty to be very easy.
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.
5.0 2 years ago
by completed this course, spending 2 hours a week on it and found the course difficulty to be very easy.
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.
4.0 2 years ago
by completed this course, spending 2 hours a week on it and found the course difficulty to be easy.
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.
5.0 2 years ago
completed this course.
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.
1.0 2 years ago
is taking this course right now.
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.
0 person found
4.0 2 years ago
completed this course.
4.0 2 years ago
by completed this course.
5.0 11 months ago
audited this course.
5.0 2 years ago
by completed this course.
4.0 2 years ago
by completed this course.
4.0 2 years ago
by completed this course.
5.0 2 years ago
by completed this course.
4.0 3 years ago
partially completed this course.
5.0 2 years ago
by is taking this course right now.
5.0 2 years ago
by completed this course.
5.0 a year ago
by completed this course.
4.0 10 months ago
completed this course.