Data Analysis in R with RStudio & Tidyverse from Coursera

Code and run your first R program in minutes without installing anything!

This course is designed for learners with no prior coding experience, providing foundational knowledge of data analysis in R. The modules in this course cover descriptive statistics, importing and wrangling data, and using statistical tests to compare populations and describe relationships. This course presents examples in R using the industry-standard Integrated Development Environment (IDE) RStudio.

To allow for a truly hands-on, self-paced learning experience, this course is video-free.

Assignments contain short explanations with images and runnable code examples with suggested edits to explore code examples further, building a deeper understanding by doing. You’ll benefit from instant feedback from a variety of assessment items along the way, gently progressing from quick understanding checks (multiple choice, fill in the blank, and un-scrambling code blocks) to small, approachable coding exercises that take minutes instead of hours. Finally, a cumulative lab at the end of the course will provide you an opportunity to apply all learned concepts within a real-world context.

What's inside

Syllabus

Describing a Numerical Data Set

Create and store data in variables as well as apply functions on them.

Importing and Describing Mixed Data Sets

Import, extract, and use built-in functions on a data set(s).

Using Statistical Tests to Compare Populations

Use statistical tests to compare data between different populations or groups or among the same.

Using Statistical Tests to Describe Relationships

Use statistical tests to describe if a relationship exists between data sets or not.

R Data Analysis Lab

Import, extract, and perform calculations on a data set.

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Explores R programming, which is standard in data analytics

Develops data analysis skills, which are core for many jobs

Uses RStudio, which is the industry-standard IDE

Offers hands-on experience, which is vital in data analysis

Provides instant feedback, which is useful for learning

Has a cumulative lab, which allows for practical application

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 in R with RStudio & Tidyverse with these activities:

Complete R practice problems

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Engaging in practice problems helps solidify the foundational understanding of R.

Browse courses on R Programming

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Identify practice problems related to R programming concepts.
Solve the practice problems using R.
Review the solutions to ensure accuracy and identify areas for improvement.

Read Introduction to Statistical Learning with R

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Reading this book provides a comprehensive introduction to statistical learning and its applications in R.

View An Introduction to Statistical Learning: with... on Amazon

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Read the book and take notes on key concepts.
Work through the examples and exercises provided in the book.
Apply the techniques learned in the book to real-world datasets.

Use interactive coding exercises on websites like HackerRank or LeetCode

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Interactive coding exercises provide a gamified way to practice and improve R programming skills.

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Identify and register for an online coding platform.
Select coding exercises related to R programming.
Solve the exercises and review the solutions to identify areas for improvement.

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Explore RStudio tutorials

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Following guided tutorials helps students become familiar with the RStudio environment and its functionalities.

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Navigate to the RStudio website or open the RStudio application.
Locate and access the tutorials section.
Select a tutorial relevant to the course content.
Follow the step-by-step instructions provided in the tutorial.
Experiment with the code examples and modify them to gain a deeper understanding.

Join a study group or online forum for R learners

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Engaging with peers helps reinforce understanding, provides support, and fosters a sense of community.

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Identify and join a study group or online forum related to R.
Participate in discussions, ask questions, and share knowledge.
Collaborate with others on projects or assignments.

Attend a workshop on R data analysis

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Attending a workshop provides an opportunity to learn from experts, connect with peers, and gain practical skills.

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Identify and register for a workshop aligned with your learning goals.
Attend the workshop and actively participate in discussions and activities.
Take notes and ask questions to maximize learning.

Develop a data analysis project using R

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Creating a data analysis project provides hands-on experience in applying R skills and deepens understanding of data analysis techniques.

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Identify a dataset that aligns with your interests or the course content.
Explore the dataset and identify patterns or trends.
Develop a research question or hypothesis to guide your analysis.
Apply appropriate statistical methods and visualizations using R.
Interpret the results and draw meaningful conclusions.

Create a blog post or video tutorial on R programming

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Creating content helps reinforce understanding and allows students to share their knowledge with others.

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Choose a specific topic related to R programming that you are comfortable with.
Research and gather information to support your content.
Write a blog post or create a video tutorial explaining the topic in a clear and engaging way.
Share your content online and encourage feedback from others.

Career center

Learners who complete Data Analysis in R with RStudio & Tidyverse will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist collects, analyzes, and interprets a variety of data using advanced statistical techniques. The insights gained from analyzing large datasets can be used in a variety of domains, including business, healthcare, and science. This course in Data Analysis in R with RStudio & Tidyverse may be useful for a Data Scientist by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer designs and builds machine learning models to solve complex problems. They use their skills to develop new products and services that can automate tasks and improve decision-making. This course in Data Analysis in R with RStudio & Tidyverse may be useful for a Machine Learning Engineer by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

A Business Analyst uses data analysis techniques to help businesses make better decisions. They use their findings to identify trends, develop strategies, and improve operations. This course in Data Analysis in R with RStudio & Tidyverse may be useful for a Business Analyst by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Business Analyst

Operations Research Analyst

An Operations Research Analyst uses mathematical and statistical techniques to solve complex business problems. They use their findings to develop strategies that improve efficiency and productivity. This course in Data Analysis in R with RStudio & Tidyverse may be useful for an Operations Research Analyst by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Operations Research Analyst

Statistician

A Statistician uses mathematical and statistical techniques to collect, analyze, and interpret data. They use their findings to make informed decisions about a variety of topics, such as public policy, healthcare, and business. This course in Data Analysis in R with RStudio & Tidyverse may be useful for a Statistician by providing a strong foundation in using R for statistical analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Statistician

Data Analyst

A Data Analyst collects, analyzes, and interprets data to help organizations make better decisions. They use their findings to identify trends, develop strategies, and improve operations. This course in Data Analysis in R with RStudio & Tidyverse may be useful for a Data Analyst by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Data Analyst

Data Engineer

A Data Engineer designs and builds the infrastructure that stores and processes large amounts of data. They use their skills to ensure that data is accessible, reliable, and secure. This course in Data Analysis in R with RStudio & Tidyverse may be useful for a Data Engineer by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Data Engineer

Quantitative Analyst

A Quantitative Analyst uses mathematical and statistical techniques to analyze financial data and make investment decisions. They use their findings to develop trading strategies and manage risk. This course in Data Analysis in R with RStudio & Tidyverse may be useful for a Quantitative Analyst by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Quantitative Analyst

Actuary

An Actuary uses mathematical and statistical techniques to assess risk and uncertainty. They use their findings to develop insurance policies and investment strategies. This course in Data Analysis in R with RStudio & Tidyverse may be useful for an Actuary by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Actuary

Epidemiologist

An Epidemiologist studies the distribution and determinants of health-related states or events in specified populations. They use their findings to develop and evaluate public health programs. This course in Data Analysis in R with RStudio & Tidyverse may be useful for an Epidemiologist by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Epidemiologist

Biostatistician

A Biostatistician uses statistical methods to design and analyze studies in the field of medicine. They use their findings to develop new treatments and improve patient care. This course in Data Analysis in R with RStudio & Tidyverse may be useful for a Biostatistician by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Biostatistician

Market Researcher

A Market Researcher collects and analyzes data to understand consumer behavior and trends. They use their findings to develop marketing strategies and improve products and services. This course in Data Analysis in R with RStudio & Tidyverse may be useful for a Market Researcher by providing a strong foundation in using R for data analysis. The course covers a variety of topics, including importing and wrangling data, using statistical tests to compare populations and describe relationships, and creating visualizations.

See salaries and explore the career path for Market Researcher

Data Analysis in R with RStudio & Tidyverse

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Syllabus

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