We may earn an affiliate commission when you visit our partners.

Course image

Introduction to R Programming for Data Science

Yan Luo

When working in the data science field you will definitely become acquainted with the R language and the role it plays in data analysis. This course introduces you to the basics of the R language such as data types, techniques for manipulation, and how to implement fundamental programming tasks.

You will begin the process of understanding common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language.

Read more

When working in the data science field you will definitely become acquainted with the R language and the role it plays in data analysis. This course introduces you to the basics of the R language such as data types, techniques for manipulation, and how to implement fundamental programming tasks.

You will begin the process of understanding common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language.

The emphasis in this course is hands-on and practical learning . You will write a simple program using RStudio, manipulate data in a data frame or matrix, and complete a final project as a data analyst using Watson Studio and Jupyter notebooks to acquire and analyze data-driven insights.

No prior knowledge of R, or programming is required.

Or subscribe to Coursera Plus

And get unlimited access to Coursera

Two deals to help you save

Save money when you learn. Here are two deals and offers that may be relevant to this course.

All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.

Valid until July 14

Coursera Plus Annual

Work toward what's next for your career with access to 10,000+ programs. Discover emerging skills and save.

Valid until August 30

Google AI App Builder

Learn how to use Gemini API and API Studio with a three-course series from Google DeepMind

What's inside

Syllabus

R Basics

Regardless of the programming language you use, all share some commonalities. For example, you’ll likely need to perform basic operations on different data types, like applying mathematical equations to numeric data. You’ll also need an environment in which to write your code, anbbd most modern integrated development environments (or IDEs) provide features that make writing code easier, like syntax checking, color coding, and integrated help. This module introduces you to the R language, its common data types, and techniques for manipulating them. You’ll also learn about the role of the R interpreter and how it transforms code into executable objects. Finally, you’ll be introduced to two of the most common IDEs for R development: RStudio and Jupyter Notebook.

Read more

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Geared towards beginners, this course establishes a knowledge foundation in R programming

Taught in a hands-on and practical manner, this course provides interactive learning opportunities

Familiarizes learners with the RStudio and Jupyter Notebook environments, widely used for R development

Covers essential data structures and programming fundamentals in R, providing a solid understanding of data manipulation and analysis techniques

Focuses on practical data analysis skills, preparing learners to handle various data sources and file formats commonly encountered in industry

Save this course

Create your own learning path. Save this course to your list so you can find it easily later.

Save

Reviews summary

Foundational r for data science beginners

According to learners, this course is a highly effective starting point for individuals with no prior programming experience aiming to learn R for data science. Students consistently praise its hands-on approach, particularly the practical labs and final project that provide real-world application using RStudio and Jupyter notebooks. While some found the pacing uneven or occasionally needed external resources for deeper understanding, recent feedback suggests improvements in course clarity. A notable concern for a few learners was technical setup challenges with environments, although this appears less prominent in newer reviews. Overall, it's recognized for building a solid foundation in R programming fundamentals and data manipulation.

Strong emphasis on hands-on application and real-world projects.

"The hands-on labs using RStudio and Jupyter notebooks were incredibly helpful."

"The practical exercises were crucial for my learning. I now feel confident to tackle more advanced R concepts."

"The final project felt like real-world application. Highly recommend for anyone looking to enter data science."

Excellent for beginners with no prior R knowledge.

"This course was an excellent introduction to R programming for someone with no prior coding experience."

"As a complete novice, I truly enjoyed this course. The instructor broke down complex topics into digestible chunks."

"Perfect for absolute beginners. I found the common data structures module extremely well explained."

Pacing can be inconsistent; some concepts may require external study.

"The course provides a decent overview, but I felt it moved too quickly through some fundamental programming concepts."

"I frequently had to pause and re-watch lectures or look up external resources to fully grasp concepts."

"For someone with a bit of programming background, it felt a bit too slow in the initial modules and then too fast in the later ones."

Some learners faced frustrating technical hurdles with environment setup.

"I encountered a lot of issues with setting up the environments (RStudio and Watson Studio)."

"Highly disappointed. Faced constant technical issues with the provided environments."

"The instructions sometimes felt vague, leading to frustration. Felt very frustrating for a beginner."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to R Programming for Data Science with these activities:

Organizing Your Course Materials

Show steps

Organize your course materials, such as notes, assignments, and quizzes. This will make it easier to study for exams and review the material later on.

Browse courses on Organization

Show steps

Create a dedicated folder for the course on your computer.
File your notes, assignments, and quizzes in the folder.
Use a note-taking app to organize your notes.

Reviewing Basic Statistics

Show steps

Review the basic concepts of statistics, such as mean, median, standard deviation, and hypothesis testing. This will provide a solid foundation for understanding the R language and its applications in data analysis.

Browse courses on Statistics

Show steps

Go over your notes from a previous statistics course.
Take a practice quiz or exam to test your understanding.
Read a book or article on basic statistics.

Reading 'R for Data Science'

Show steps

Read 'R for Data Science' by Hadley Wickham and Garrett Grolemund. This book provides a comprehensive overview of the R programming language and its applications in data science.

View R for Data Science: Import, Tidy, Transform,... on Amazon

Show steps

Purchase or borrow a copy of the book.
Read one chapter per week.
Complete the exercises at the end of each chapter.
Take notes and highlight important concepts.

Seven other activities

Expand to see all activities and additional details

Show all ten activities

Working Through RStudio Tutorials

Show steps

Follow along with the tutorials provided by RStudio to learn the basics of the R programming language and the RStudio IDE. This will give you a hands-on experience and help you become familiar with the tools you'll be using throughout the course.

Browse courses on RStudio

Show steps

Complete the RStudio Basics tutorial.
Work through the Data Manipulation tutorial.
Try out the Data Visualization tutorial.

Classify Data Structures

Show steps

Practice recognizing and using the different types of data structures in R.

Show steps

Write a basic program using R to identify the type of a given data structure.
Convert a data structure from one type to another.
Manipulate data within a specific data structure.

Joining a Study Group

Show steps

Join a study group with other students in the course. This will give you the opportunity to discuss the material, ask questions, and learn from your peers.

Show steps

Find a study group or create your own.
Meet regularly to discuss the course material.
Work together on assignments and projects.

Analyze a Data Set

Show steps

Gain hands-on experience loading and analyzing data using R.

Show steps

Load a data set from a file or database into R.
Clean and prepare the data for analysis.
Perform basic statistical analysis on the data.
Create a visualization that summarizes the results of the analysis.

Solving R Coding Challenges

Show steps

Attempt to solve coding challenges on websites like HackerRank or LeetCode. This will help you practice your R programming skills and learn how to apply them to real-world problems.

Browse courses on R Programming

Show steps

Sign up for a coding challenge website.
Choose a challenge that is appropriate for your skill level.
Attempt to solve the challenge on your own.
If you get stuck, consult the discussion forums or ask for help from a mentor.

Creating a Data Analysis Blog

Show steps

Start a blog where you share your experiences learning R and applying it to data analysis projects. This will help you solidify your understanding of the concepts and improve your communication skills.

Browse courses on Data Analysis

Show steps

Choose a blogging platform and set up your blog.
Write a series of posts on different aspects of R programming and data analysis.
Promote your blog on social media and other online platforms.
Engage with your readers and respond to comments.

Building a Data Analysis Portfolio

Show steps

Create a portfolio of data analysis projects that showcase your skills and knowledge. This will give you a tangible demonstration of your abilities and help you stand out to potential employers.

Browse courses on Data Analysis

Show steps

Identify a few data analysis projects that you can complete.
Collect the data and clean it.
Analyze the data and draw conclusions.
Create a report or presentation to showcase your findings.

Career center

Learners who complete Introduction to R Programming for Data Science will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course can help you develop the skills needed to become a successful Data Scientist, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Data Scientist.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use data to solve business problems and make recommendations. This course can help you develop the skills needed to become a successful Data Analyst, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Data Analyst.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians collect, analyze, and interpret data to help businesses and organizations make informed decisions. This course can help you develop the skills needed to become a successful Statistician, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Statistician.

See salaries and explore the career path for Statistician

Machine Learning Engineer

Machine Learning Engineers design and build machine learning models to solve business problems. This course can help you develop the skills needed to become a successful Machine Learning Engineer, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to help businesses make better decisions. This course can help you develop the skills needed to become a successful Business Analyst, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Business Analyst.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers build and maintain the infrastructure that stores and processes data. This course can help you develop the skills needed to become a successful Data Engineer, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Data Engineer.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers design, develop, and maintain software applications. This course can help you develop the skills needed to become a successful Software Engineer, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Software Engineer.

See salaries and explore the career path for Software Engineer

Database Administrator

Database Administrators manage and maintain databases. This course can help you develop the skills needed to become a successful Database Administrator, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Database Administrator.

See salaries and explore the career path for Database Administrator

Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve business problems. This course can help you develop the skills needed to become a successful Operations Research Analyst, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use data to analyze financial performance and make investment recommendations. This course can help you develop the skills needed to become a successful Financial Analyst, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Market Researcher

Market Researchers collect and analyze data to understand consumer behavior. This course can help you develop the skills needed to become a successful Market Researcher, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Market Researcher.

See salaries and explore the career path for Market Researcher

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course can help you develop the skills needed to become a successful Actuary, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as an Actuary.

See salaries and explore the career path for Actuary

Epidemiologist

Epidemiologists investigate the causes and patterns of disease. This course can help you develop the skills needed to become a successful Epidemiologist, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as an Epidemiologist.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians use statistical methods to analyze biological data. This course can help you develop the skills needed to become a successful Biostatistician, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Biostatistician.

See salaries and explore the career path for Biostatistician

Data Science Manager

Data Science Managers lead and manage teams of data scientists. This course can help you develop the skills needed to become a successful Data Science Manager, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Data Science Manager.

See salaries and explore the career path for Data Science Manager

Reading list

We've selected ten books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Introduction to R Programming for Data Science.

Cover image

Cover image

R for Data Science

Save

Comprehensive introduction to the R programming language for data science. It covers all the basics, from data types and structures to data manipulation and visualization.

R for Data Science: Import, Tidy, Transform,...

(Deutsch) R für Data Science: Daten importieren, bereinigen,...

Cover image

Cover image

An Introduction to Statistical Learning

Save

Provides a comprehensive introduction to statistical learning. It covers a wide range of topics, from linear regression and classification to support vector machines and neural networks.

An Introduction to Statistical Learning

An Introduction to Statistical Learning

Cover image

Cover image

Tidy Modeling with R

Save

Provides a comprehensive introduction to predictive modeling. It covers a wide range of topics, from predictive modeling basics and techniques to predictive modeling applications.

Tidy Modeling with R

Tidy Modeling with R

Cover image

Cover image

Deep Learning with R, Second Edition

Save

Practical guide to deep learning. It covers a wide range of topics, from neural networks and convolutional neural networks to recurrent neural networks and generative adversarial networks.

Deep Learning with R, Second Edition

Deep Learning with R, Second Edition

Cover image

Cover image

Data Science for Business

Save

Provides a comprehensive introduction to data science for business. It covers a wide range of topics, from data collection and preparation to data analysis and visualization.

Data Science for Business: What You Need to Know...

Data Science for Business: What You Need to Know...

Cover image

Cover image

Machine Learning with R

Save

Practical guide to machine learning with R. It covers a wide range of topics, from linear regression and classification to support vector machines and neural networks.

Machine Learning with R - Fourth Edition: Learn...

Machine Learning with R - Second Edition: Expert...

Machine Learning with R - Third Edition: Expert...

(Italiano) Machine Learning con R: Conoscere le tecniche per...

R: Unleash Machine Learning Techniques

Machine Learning with R: Learn How to Use R to...

(中文) Machine learning and R language (3rd edition of the...

Cover image

Cover image

Text Mining with R

Save

Provides a comprehensive introduction to natural language processing. It covers a wide range of topics, from text mining and sentiment analysis to machine translation and information retrieval.

Text Mining with R

Text Mining with R

Cover image

Cover image

The Art of Machine Learning

Save

Practical guide to R programming. It covers a wide range of topics, from data import and manipulation to statistical analysis and visualization.

The Art of Machine Learning: A Hands-On Guide to...

The Art of Machine Learning: A Hands-On Guide to...

Cover image

Cover image

Save

Collection of recipes for solving common R programming problems. It provides quick and easy solutions to a wide range of tasks.

R Cookbook: Proven Recipes for Data Analysis,...

R Cookbook: Proven Recipes for Data Analysis,...

Cover image

Cover image

Modern Data Visualization with R

Save

Hands-on guide to R programming. It covers a wide range of topics, from data import and manipulation to statistical analysis and visualization.

Modern Data Visualization with R

Modern Data Visualization with R

Share

Help others find this course page by sharing it with your friends and followers:

Copy Link

Similar courses

Similar courses are unavailable at this time. Please try again later.

Course image

Effort

5 weeks of study, 2-4 hours/week

Level

Introductory

Via

Coursera

Institution

IBM

Instructor

Yan Luo

Language

English

Transcripts

Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова magyar nyelv Bahasa Indonesia ไทย қазақша اُردُو, فارسی বাংলা tiếng Việt پښتو آذربایجان دیلی

Introduction to R Programming for Data Science belongs to the following collections:

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Geared towards beginners, this course establishes a knowledge foundation in R programming

Taught in a hands-on and practical manner, this course provides interactive learning opportunities

Familiarizes learners with the RStudio and Jupyter Notebook environments, widely used for R development

Covers essential data structures and programming fundamentals in R, providing a solid understanding of data manipulation and analysis techniques

Focuses on practical data analysis skills, preparing learners to handle various data sources and file formats commonly encountered in industry

Share this

Share to help others discover this course.

Link

Begin learning today

Enroll now to gain full access to Introduction to R Programming for Data Science.

Enroll now Enroll in this course

Save for later

Add this course to your list. Find it anytime.

Save

Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2025 OpenCourser