OpenCourser brand icon
Sign in
We may earn an affiliate commission when you visit our partners.
Course image
Course image
Coursera logo

Data Analysis with Tidyverse

Jane Wall

This course continues our gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:

• you want to do data analysis but don’t know programming

• you know programming but aren’t too familiar with R

• you know some R programming but want to learn more about the tidyverse verbs

Read more

This course continues our gentle introduction to programming in R designed for 3 types of learners. It will be right for you, if:

• you want to do data analysis but don’t know programming

• you know programming but aren’t too familiar with R

• you know some R programming but want to learn more about the tidyverse verbs

It is best taken following the first course in the specialization or if you already are familiar with ggplot, RMarkdown, and basic function writing in R. You will use learn to use readr to read in your data, dplyr to analyze your data, and stringr and forcats to manipulate strings and factors.

Enroll now

Here's a deal for you

Save money when you learn with a deal that may be relevant to this course.
All coupon codes, vouchers, and discounts are applied automatically unless otherwise noted.
Valid until March 12, 2027
Save 40% Off 3 Months
Access thousands of top-rated learning programs with a Coursera Plus monthly subscription and take 40% off 3 months for a limited time.
Take
40%
off
Get offer

What's inside

Syllabus

Projects, Tibbles and Importing Data
When analyzing data, you will often be required to import data from CSV or txt files. In this module, you will learn how to import and parse data in base R and the readr library, a package in the Tidyverse. You will also be introduced to R projects, which help store and organize data files associated with an analysis.
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Well-suited for those with little to no R programming experience who want to learn data analysis
Useful for students familiar with R programming that want to strengthen their tidyverse skills
Covers essential tidyverse packages and techniques for data analysis, including readr, dplyr, stringr, and forcats
Provides a solid foundation for beginners in data analysis using R and the tidyverse
Assumes students have some familiarity with ggplot, RMarkdown, and basic function writing in R

Save this course

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

Reviews summary

Practical data analysis with tidyverse

According to students, this course is a highly practical and well-structured program for mastering Data Analysis with Tidyverse. Learners consistently praise its clear explanations of complex topics like string manipulation and relational data, alongside engaging hands-on labs and project-based learning that enable direct application to professional work. While the course is broadly considered a strong follow-up for those with R basics, some learners note a significant jump in complexity, suggesting that a very strong grasp of R fundamentals (including ggplot and RMarkdown) is advisable. Recent reviews highlight notable improvements in course exercises and instructor responsiveness, addressing prior concerns about support and content clarity.
Complex topics are broken down and made digestible for learners.
"The way they break down complex topics like string manipulation and regular expressions made it much more digestible."
"The instructors are clear, and the exercises really reinforce the concepts."
"Some parts felt rushed. The exercises were good once I understood the concepts."
Recent updates enhance exercises and instructor support significantly.
"I particularly appreciated the new exercises added recently, they really solidify understanding."
"The instructor interactions in the forum have improved significantly."
"It's great to see the course addressing previous feedback on content clarity and support."
Comprehensive and accessible instruction on key Tidyverse packages.
"This course was exactly what I needed to deepen my Tidyverse skills."
"The Tidyverse verbs taught here are essential for modern data analysis."
"The course makes readr and dplyr incredibly accessible."
Excellent hands-on exercises and projects for real-world application.
"The dplyr and stringr modules were incredibly well-explained with practical examples."
"The hands-on labs are excellent. I'm now confidently using these tools in my data analysis projects."
"I've been able to apply these skills directly to my job. This course is a game-changer for anyone in data roles."
Requires a solid foundation in R beyond basic programming.
"I found it difficult to follow without a very strong grasp of R fundamentals."
"The jump in complexity was too steep for me, especially in the relational data module."
"You really need to be comfortable with R syntax before starting this."

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 Data Analysis with Tidyverse with these activities:
Review Probability and Statistics
Refresh understanding of probability and statistics concepts
Browse courses on Probability
Show steps
  • Review lecture notes or textbooks on probability and statistics
  • Complete practice problems
Reading: Introduction to Data Science in R
Provide a comprehensive background on data science fundamentals
View An Introduction to Statistical Learning: with... on Amazon
Show steps
  • Read Chapters 1-3
  • Complete the exercises in the book
Review R Markdown
Solidify knowledge of how to create dynamic documents in R Markdown
Browse courses on R Markdown
Show steps
  • Review documentation for R Markdown
  • Create a new R Markdown document
  • Insert code chunks and text
  • Knit the document to HTML
Six other activities
Expand to see all activities and additional details
Show all nine activities
Interactive Tutorials on String Manipulation
Enhance understanding of string manipulation techniques in R
Browse courses on String Manipulation
Show steps
  • Complete the tutorials on the RStudio website
  • Practice string manipulation exercises
Data Manipulation Practice
Practice data manipulation skills using the tidyverse verbs
Browse courses on Data Manipulation
Show steps
  • Load a dataset into R
  • Use dplyr to filter the data
  • Use tidyr to reshape the data
  • Use stringr to manipulate strings
Study Group for Relational Data
Engage with peers to discuss and reinforce concepts of relational data and data analysis
Browse courses on Relational Data
Show steps
  • Form a study group with classmates
  • Meet regularly to discuss course materials and practice exercises
Data Cleaning and Tidying Project
Develop skills in cleaning and tidying real-world datasets
Browse courses on Data Cleaning
Show steps
  • Obtain a messy dataset
  • Use dplyr and tidyr to clean and tidy the data
  • Create a report documenting the cleaning and tidying process
Data Visualization Project
Apply data visualization principles to create a visually appealing and informative plot
Browse courses on Data Visualization
Show steps
  • Choose a dataset and a research question
  • Explore the data and identify patterns
  • Create a ggplot2 plot
  • Refine the plot and add annotations
  • Write a brief report summarizing the findings
Volunteer at a Data Science Project
Gain practical experience in data science by contributing to real-world projects
Browse courses on Data Science
Show steps
  • Find a local data science organization or project
  • Contact the organization and inquire about volunteer opportunities
  • Commit to a regular volunteering schedule
  • Contribute to data analysis, visualization, or other tasks

Career center

Learners who complete Data Analysis with Tidyverse will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts help make sense of raw data. They use their skills to identify trends, patrones, and other useful information that companies can use to make better decisions. As a data analyst, you will apply your skills to interpret large amounts of data using the tidyverse, including data that have been imported from CSV and txt files. By learning how to reorganize data to be "tidy," you will prepare yourself for this role, where data is often stored in a variety of tabular forms and must be organized to be analyzed. This course will be most helpful to you in your role as a data analyst if you are new to the field or are looking to gain experience with a new skill such as tidyverse.
See salaries and explore the career path for Data Analyst
Data Scientist
Data Scientists are responsible for collecting, cleaning, and analyzing data, and then presenting that data in a way that is easy to understand. This course will help you build a foundation in the tidyverse, a collection of packages in R that helps data scientists analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and machine learning techniques.
See salaries and explore the career path for Data Scientist
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. This course will help you build a foundation in data analysis using the tidyverse, a collection of packages in R that helps statisticians analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical techniques.
See salaries and explore the career path for Statistician
Financial Analyst
Financial analysts use data to make sound decisions about investments. As a financial analyst, you will be responsible for analyzing financial data and making recommendations to clients. This course will help you develop the skills you need to analyze financial data using the tidyverse, a collection of packages in R that helps financial analysts analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and financial techniques.
See salaries and explore the career path for Financial Analyst
Market Researcher
Market Researchers use data to understand consumer behavior and make sound decisions about marketing campaigns. This course will help you build a foundation in data analysis using the tidyverse, a collection of packages in R that helps market researchers analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and marketing techniques.
See salaries and explore the career path for Market Researcher
Business Analyst
Business analysts use data to help businesses make better decisions. This course will help you build a foundation in data analysis using the tidyverse, a collection of packages in R that helps business analysts analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and business techniques.
See salaries and explore the career path for Business Analyst
Data Engineer
Data engineers design, build, and maintain the data infrastructure that businesses use to store and process data. This course will help you build a foundation in data engineering using the tidyverse, a collection of packages in R that helps data engineers analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and data engineering techniques.
See salaries and explore the career path for Data Engineer
Software Engineer
Software engineers design, develop, and maintain software applications. This course will help you build a foundation in software engineering using the tidyverse, a collection of packages in R that helps software engineers analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and software engineering techniques.
See salaries and explore the career path for Software Engineer
Quantitative Analyst
Quantitative analysts use data to make investment decisions. This course will help you build a foundation in quantitative analysis using the tidyverse, a collection of packages in R that helps quantitative analysts analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and quantitative analysis techniques.
See salaries and explore the career path for Quantitative Analyst
Actuary
Actuaries use data to assess risk and make sound decisions about insurance and financial products. This course will help you build a foundation in actuarial science using the tidyverse, a collection of packages in R that helps actuaries analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and actuarial science techniques.
See salaries and explore the career path for Actuary
Epidemiologist
Epidemiologists use data to investigate the causes of disease and promote public health. This course will help you build a foundation in epidemiology using the tidyverse, a collection of packages in R that helps epidemiologists analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and epidemiological techniques.
See salaries and explore the career path for Epidemiologist
Biostatistician
Biostatisticians use data to design and analyze clinical trials and other health-related research studies. This course will help you build a foundation in biostatistics using the tidyverse, a collection of packages in R that helps biostatisticians analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and biostatistical techniques.
See salaries and explore the career path for Biostatistician
Data Journalist
Data journalists use data to tell stories and inform the public. This course will help you build a foundation in data journalism using the tidyverse, a collection of packages in R that helps data journalists analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and data journalism techniques.
See salaries and explore the career path for Data Journalist
Librarian
Librarians help people find and access information. This course will help you build a foundation in library science using the tidyverse, a collection of packages in R that helps librarians analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and library science techniques.
See salaries and explore the career path for Librarian
Teacher
Teachers help students learn and grow. This course will help you build a foundation in education using the tidyverse, a collection of packages in R that helps teachers analyze data more efficiently and effectively. You will learn how to use the tidyverse to import data from a variety of sources, clean and tidy your data, and analyze your data using a variety of statistical and educational techniques.
See salaries and explore the career path for Teacher

Reading list

We've selected 13 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 Data Analysis with Tidyverse.
Cover image
R for Data Science
Save
Provides a comprehensive introduction to R for data science, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about R and its applications in data science.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
Data Manipulation with R
Save
Provides a comprehensive guide to data manipulation in R, covering topics such as data import, cleaning, transformation, and analysis. It valuable resource for anyone who wants to learn how to manipulate data effectively in R.
Data Manipulation with R (Use R!)
Paperback
$$$
Data Manipulation with R (Use R!)
Kindle Edition
$$$
Cover image
Advanced R, Second Edition
Save
Provides a comprehensive guide to ggplot2, a powerful package for data visualization in R. It valuable resource for anyone who wants to learn how to create beautiful and informative data visualizations.
Advanced R, Second Edition (Chapman & Hall/CRC The...
Paperback
$$$
Advanced R, Second Edition (Chapman & Hall/CRC The...
Kindle Edition
$$$
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive guide to statistical learning, covering topics such as linear models, regularization, and ensemble methods. It valuable resource for anyone who wants to learn more about the theory and practice of statistical learning.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
The Elements of Statistical Learning: Data Mining,...
Kindle Edition
$$$
Cover image
Tidy Modeling with R
Save
Provides a comprehensive guide to predictive modeling, covering topics such as data preparation, model selection, and model evaluation. It valuable resource for anyone who wants to learn how to build predictive models that can be used to make informed decisions.
Tidy Modeling with R
Paperback
Check
Tidy Modeling with R
Kindle Edition
Check
Cover image
Data Science for Business
Save
Provides a comprehensive guide to data science for business, covering topics such as data collection, data analysis, and data visualization. It valuable resource for anyone who wants to learn how to use data science to improve business outcomes.
Data Science for Business: What You Need to Know...
Paperback
$$
Data Science for Business: What You Need to Know...
Kindle Edition
$$
Cover image
TinyML
Save
Provides a comprehensive guide to machine learning for business, covering topics such as supervised learning, unsupervised learning, and reinforcement learning. It valuable resource for anyone who wants to learn how to use machine learning to solve business problems.
TinyML: Machine Learning with TensorFlow Lite on...
Paperback
$$
O'Reilly.TinyML. Wykorzystanie TensorFlow Lite do...
Paperback
$$$
TinyML: Machine Learning with TensorFlow Lite on...
Kindle Edition
$$
Cover image
Deep Learning
Save
Provides a comprehensive guide to deep learning for business, covering topics such as convolutional neural networks, recurrent neural networks, and generative adversarial networks. It valuable resource for anyone who wants to learn how to use deep learning to solve business problems.
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Reinforcement Learning, second edition
Save
Provides a comprehensive guide to reinforcement learning for business, covering topics such as Markov decision processes, value iteration, and policy iteration. It valuable resource for anyone who wants to learn how to use reinforcement learning to solve business problems.
Reinforcement Learning, second edition: An...
Hardcover
$$$
Reinforcement Learning, second edition: An...
Kindle Edition
$$$
Cover image
R Packages
Save
Covers advanced topics in R, such as functional programming, object-oriented programming, and data visualization. It good resource for anyone who wants to learn more about the inner workings of R and how to use it to solve complex problems.
R Packages
Paperback
Check
R Packages
Kindle Edition
Check
Cover image
R Markdown
Save
Provides a comprehensive guide to R Markdown, a powerful tool for creating dynamic and reproducible reports, presentations, and dashboards. It valuable resource for anyone who wants to learn how to use R Markdown to communicate their data analysis results effectively.
R Markdown: The Definitive Guide (Chapman &...
Paperback
$$
R Markdown: The Definitive Guide (Chapman &...
Kindle Edition
$$
Cover image
An Introduction to Statistical Learning
Save
Provides a comprehensive introduction to statistical learning, covering topics such as linear regression, logistic regression, and decision trees. It valuable resource for anyone who wants to learn more about the theory and practice of statistical learning.
An Introduction to Statistical Learning
Paperback
Check
An Introduction to Statistical Learning
Kindle Edition
Check
Cover image
The Art of Machine Learning
Save
Provides a comprehensive guide to R programming, covering topics such as data types, control flow, functions, and graphics. It valuable resource for anyone who wants to learn how to program in R effectively.
The Art of Machine Learning: A Hands-On Guide to...
Paperback
$$
The Art of Machine Learning: A Hands-On Guide to...
Kindle Edition
$$

Share

Help others find this course page by sharing it with your friends and followers:
Facebook
LinkedIn
Reddit
X
Link
Email

Similar courses

Here are nine courses similar to Data Analysis with Tidyverse.
Wrangling Data for Data Analysts with Python
Most relevant
Data Analysis with R
Most relevant
Modeling Data in the Tidyverse
Most relevant
Data Analysis with R Programming
Most relevant
Data Analysis in R with RStudio & Tidyverse
Most relevant
Visualizing Data in the Tidyverse
Most relevant
Fundamentals of Data Analytics in the Public Sector with R
Most relevant
R Programming and Tidyverse Capstone Project
Most relevant
Mastering Data Wrangling with dplyr
Most relevant

Coursera Buyer Protection & Guarantee

Verified partner policies & purchase protections

Accredited University Partner
Free Trial & Audit

7-Day Free Trial & Free Audit

7-day free trial on monthly Coursera Plus subscriptions; free audit option available on most individual courses to explore video lectures and readings at no charge.

Return & Refund Policy

14-Day Refund Guarantee

14-day refund window on annual Coursera Plus subscriptions and single-course purchases prior to certificate issuance. Monthly subscriptions do not offer refunds once billed.

Buyer Protections

Verified Academic & Industry Partner

Accredited university and industry certifications (e.g. Stanford, Google, IBM, Yale), secure SSL payment processing, and verifiable digital certificate links.

Key Purchase Protections
14-day refund window on annual Coursera Plus and single courses
7-day free trial on monthly Coursera Plus subscriptions
Free course audit option for lectures and readings
Verified certificates from top universities & companies
OpenCourser Verified Partner Policy details last reviewed: 2026-10-22
Course image
Effort
3 to 4 hours per week for five weeks.
Level
Introductory
Via
Coursera
Institution
University of Colorado Boulder
Instructor
Jane Wall
Language
English
Transcripts
Español Français Português Deutsch Italiano 中文 العربية 한국어 日本語 हिंदी Nederlands Svenska Pусский Türkçe Polski Ελληνικά українська мова Bahasa Indonesia ไทย қазақша
This course belongs to a collection called Expressway to Data Science: R Programming and Tidyverse. It's comprised of three courses:
  1. Introduction to R Programming and Tidyverse
  2. Data Analysis with Tidyverse (viewing)
  3. R Programming and Tidyverse Capstone Project
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Well-suited for those with little to no R programming experience who want to learn data analysis
Useful for students familiar with R programming that want to strengthen their tidyverse skills
Covers essential tidyverse packages and techniques for data analysis, including readr, dplyr, stringr, and forcats
Provides a solid foundation for beginners in data analysis using R and the tidyverse
Assumes students have some familiarity with ggplot, RMarkdown, and basic function writing in R
Share this
Share to help others discover this course.
Facebook LinkedIn X Reddit Link Email
Begin learning today
Enroll now to gain full access to Data Analysis with Tidyverse.
Enroll now
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