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R Programming A-Z™

R For Data Science With Real Exercises!

4.5 Filled star Filled star Filled star Filled star Half star
Based on 56,438 ratings
see reviews
Kirill Eremenko, SuperDataScience Team, and Ligency Team

Learn R Programming by doing.

There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different.

This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.

After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

Read more

Learn R Programming by doing.

There are lots of R courses and lectures out there. However, R has a very steep learning curve and students often get overwhelmed. This course is different.

This course is truly step-by-step. In every new tutorial we build on what had already learned and move one extra step forward.

After every video, you learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples.

This training is packed with real-life analytical challenges which you will learn to solve. Some of these we will solve together, some you will have as homework exercises.

In summary, this course has been designed for all skill levels and even if you have no programming or statistical background you will be successful in this course.

I can't wait to see you in class,

What you will learn:

  • Learn how to use R Studio

  • Learn the core principles of programming

  • Learn how to create vectors in R

  • Learn how to create variables

  • Learn about integer, double, logical, character, and other types in R

  • Learn how to create a while() loop and a for() loop in R

  • Learn how to build and use matrices in R

  • Learn the matrix() function, learn rbind() and cbind()

  • Learn how to install packages in R

Sincerely,

Kirill Eremenko

Enroll now

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What's inside

Learning objectives

  • Learn to program in r at a good level
  • Learn how to use r studio
  • Learn the core principles of programming
  • Learn how to create vectors in r
  • Learn how to create variables
  • Learn about integer, double, logical, character and other types in r
  • Learn how to create a while() loop and a for() loop in r
  • Learn how to build and use matrices in r
  • Learn the matrix() function, learn rbind() and cbind()
  • Learn how to install packages in r
  • Learn how to customize r studio to suit your preferences
  • Understand the law of large numbers
  • Understand the normal distribution
  • Practice working with statistical data in r
  • Practice working with financial data in r
  • Practice working with sports data in r
  • Show more
  • Show less

Syllabus

Hit The Ground Running
Welcome Challenge!
Welcome to the R Programming Course!
Installing R and R Studio (MAC & Windows)
Read more

Traffic lights

Read about what's good
what should give you pause
and possible dealbreakers
Covers basic programming principles, making it suitable for beginners
Emphasizes practical application through hands-on examples
Covers essential R functions and packages for data analysis
Provides step-by-step guidance and exercises for reinforcement
Assumes no prior programming or statistical knowledge
May require additional resources for more advanced concepts

Save this course

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

Practical r fundamentals for data science

According to learners, this course is an excellent starting point for those new to R and programming. Many highlight the clear and engaging teaching style and the well-structured curriculum. Students particularly appreciate the practical, hands-on exercises using real-world datasets, finding them crucial for solidifying concepts. While providing a strong foundation in R programming and data manipulation, some learners note that it serves as an introduction and deeper dives into advanced topics or specific statistical methods would require further study. Overall, reviews suggest it effectively delivers on its promise of teaching R for data science from the ground up through practical application.
Provides a solid basis in R programming.
"This course gave me a strong foundation in R fundamentals."
"I now feel confident in my ability to perform basic data analysis tasks in R."
"Learned all the core principles needed to start working with R."
Instructor explains concepts effectively.
"Kirill's explanations are clear, concise, and easy to grasp."
"The instructor's teaching style kept me engaged throughout the course."
"Everything is explained very simply, building up step-by-step."
Features helpful, real-world examples and homework.
"The hands-on exercises with real data made learning so much more effective."
"Applying the concepts through the homework assignments was the best part for me."
"Loved solving the challenges with the basketball and movie datasets. Very practical!"
Excellent for those new to R and programming.
"I had zero programming experience before this course and found it incredibly easy to follow."
"Perfect introduction to R and RStudio for a complete beginner like me."
"Great starting point if you've never coded before but want to learn R for data."
May not be sufficient for advanced topics.
"Excellent introduction, but definitely need to take more courses for advanced data science."
"It covers the basics well but doesn't go deep into complex statistics or machine learning algorithms."
"Great for fundamentals, but don't expect in-depth coverage of every R package."

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 R Programming A-Z™: R For Data Science With Real Exercises! with these activities:
Follow Along with RStudio Tutorials
Gain familiarity with the RStudio interface and basic R functions.
Browse courses on R Programming
Show steps
  • Complete the RStudio Basics tutorial
  • Try out the RStudio Cheatsheet
  • Practice using the RStudio Console
Create a Reference Guide of R Functions
Organize and document frequently used R functions to facilitate quick reference and improve efficiency.
Browse courses on R Programming
Show steps
  • Identify commonly used R functions
  • Write clear and concise descriptions for each function
  • Organize the functions into logical categories or by purpose
Participate in R Programming Discussion Forum
Engage with other learners to share knowledge, ask questions, and receive feedback.
Browse courses on R Programming
Show steps
  • Join an R programming discussion forum
  • Post a question or comment
  • Respond to others' posts
Five other activities
Expand to see all activities and additional details
Show all eight activities
Practice Data Manipulation in R
Practice manipulating datasets in R to reinforce understanding of data structures and operations.
Browse courses on Data Manipulation
Show steps
  • Import a dataset into R
  • Create a new variable derived from existing variables
  • Subset the dataset based on a condition
  • Calculate summary statistics for a variable
Solve R Coding Challenges
Challenge coding abilities and improve problem-solving skills by attempting to solve R coding challenges.
Browse courses on R Programming
Show steps
  • Find an online platform or resource with R coding challenges
  • Select a challenge and attempt to solve it
  • Review the solution and learn from the approach
Create a Data Visualization in R
Apply visualization techniques to present data insights and improve understanding of data patterns.
Browse courses on Data Visualization
Show steps
  • Choose an appropriate visualization type
  • Select relevant variables for the visualization
  • Create the visualization using ggplot2
  • Interpret the visualization and draw conclusions
Build a Simple R Application
Apply R programming skills to solve a real-world problem and create a functional application.
Browse courses on R Programming
Show steps
  • Identify a problem to solve using R
  • Design and plan the application
  • Implement the application in R
  • Test and debug the application
  • Deploy the application (optional)
Provide Support to New R Learners
Contribute to the R community by sharing knowledge and assisting others with their R learning journey.
Browse courses on R Programming
Show steps
  • Join an R community or discussion forum
  • Offer assistance to learners with R-related questions
  • Provide guidance and encouragement to beginners

Career center

Learners who complete R Programming A-Z™: R For Data Science With Real Exercises! will develop knowledge and skills that may be useful to these careers:
Data Scientist
Data Scientists use their skills in programming, statistics, and machine learning to build models that can predict future events or outcomes. This course can help you develop the skills you need to become a Data Scientist. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform statistical analysis and machine learning.
See salaries and explore the career path for Data Scientist
Statistician
Statisticians use their skills in mathematics, statistics, and data analysis to help businesses make decisions. This course can help you develop the skills you need to become a Statistician. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform statistical analysis.
See salaries and explore the career path for Statistician
Data Analyst
Data Analysts collect, process, and analyze data to help businesses make informed decisions. They use their skills in programming, statistics, and data visualization to identify trends, patterns, and relationships in data. This course can help you develop the skills you need to become a Data Analyst. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform statistical analysis and create visualizations.
See salaries and explore the career path for Data Analyst
Quantitative Analyst
Quantitative Analysts use their skills in mathematics, statistics, and programming to develop and implement trading strategies. This course can help you develop the skills you need to become a Quantitative Analyst. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform financial analysis and develop trading strategies.
See salaries and explore the career path for Quantitative Analyst
Operations Research Analyst
Operations Research Analysts use their skills in mathematics, statistics, and optimization to help businesses make decisions. This course can help you develop the skills you need to become an Operations Research Analyst. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform operations research analysis.
See salaries and explore the career path for Operations Research Analyst
Actuary
Actuaries use their skills in mathematics, statistics, and finance to help businesses manage risk. This course can help you develop the skills you need to become an Actuary. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform financial analysis and manage risk.
See salaries and explore the career path for Actuary
Business Analyst
Business Analysts use their skills in business analysis, data analysis, and problem-solving to help businesses make decisions. This course can help you develop the skills you need to become a Business Analyst. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform business analysis and solve problems.
See salaries and explore the career path for Business Analyst
Marketing Analyst
Marketing Analysts use their skills in marketing, data analysis, and statistics to help businesses make marketing decisions. This course can help you develop the skills you need to become a Marketing Analyst. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform marketing analysis.
See salaries and explore the career path for Marketing Analyst
Research Analyst
Research Analysts use their skills in research, data analysis, and writing to help businesses make decisions. This course can help you develop the skills you need to become a Research Analyst. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform research and write reports.
See salaries and explore the career path for Research Analyst
Financial Analyst
Financial Analysts use their skills in mathematics, statistics, and financial modeling to help businesses make investment decisions. This course can help you develop the skills you need to become a Financial Analyst. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform financial analysis.
See salaries and explore the career path for Financial Analyst
Machine Learning Engineer
Machine Learning Engineers use their skills in machine learning, data analysis, and programming to design, develop, and maintain machine learning models. This course may be useful for you if you are interested in becoming a Machine Learning Engineer. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to design and develop machine learning models.
See salaries and explore the career path for Machine Learning Engineer
Data Engineer
Data Engineers use their skills in data engineering, data analysis, and programming to design, develop, and maintain data pipelines. This course may be useful for you if you are interested in becoming a Data Engineer. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to design and develop data pipelines.
See salaries and explore the career path for Data Engineer
Software Engineer
Software Engineers use their skills in programming, software development, and problem-solving to design, develop, and maintain software applications. This course can help you develop the skills you need to become a Software Engineer. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to develop software applications.
See salaries and explore the career path for Software Engineer
Project Manager
Project Managers use their skills in project management, leadership, and problem-solving to plan, execute, and manage projects. This course may be useful for you if you are interested in becoming a Project Manager. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform project analysis.
See salaries and explore the career path for Project Manager
Product Manager
Product Managers use their skills in product management, marketing, and engineering to develop and manage products. This course may be useful for you if you are interested in becoming a Product Manager. You will learn how to use R, a powerful programming language for data analysis, and you will learn how to use R to perform product analysis.
See salaries and explore the career path for Product Manager

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 R Programming A-Z™: R For Data Science With Real Exercises!.
Cover image
R for Data Science
Save
Provides a comprehensive introduction to R, a popular programming language for data science. It covers the basics of R, such as data types, data structures, and control flow, as well as more advanced topics, such as data visualization, statistical modeling, and machine learning. This book valuable resource for anyone who wants to learn R for data science.
R for Data Science: Import, Tidy, Transform,...
Paperback
$$
(Deutsch) R für Data Science: Daten importieren, bereinigen,...
Kindle Edition
$$$
Cover image
Extending R
Save
Comprehensive reference guide to the R programming language. It covers all aspects of R, from the basics to the most advanced features. This book valuable resource for anyone who wants to learn more about R.
Extending R (Chapman & Hall/CRC The R Series)
Paperback
$$$
Extending R (Chapman & Hall/CRC The R Series)
Kindle Edition
$$$
Cover image
Machine Learning with R
Save
Provides a comprehensive introduction to machine learning with R. It covers the basics of machine learning, such as supervised learning, unsupervised learning, and reinforcement learning. This book valuable resource for anyone who wants to learn more about machine learning.
Machine Learning with R - Fourth Edition: Learn...
Paperback
$$
Machine Learning with R - Second Edition: Expert...
Kindle Edition
$$
Machine Learning with R - Third Edition: Expert...
Paperback
$$
(Italiano) Machine Learning con R: Conoscere le tecniche per...
Kindle Edition
$$
R: Unleash Machine Learning Techniques
Kindle Edition
$$$
Machine Learning with R: Learn How to Use R to...
Paperback
$$
(中文) Machine learning and R language (3rd edition of the...
Paperback
$$
Cover image
Deep Learning with TensorFlow and Keras
Save
Provides a comprehensive introduction to deep learning with R. It covers the basics of deep learning, such as neural networks, convolutional neural networks, and recurrent neural networks. This book valuable resource for anyone who wants to learn more about deep learning.
Deep Learning with TensorFlow and Keras - Third...
Paperback
$$
Deep Learning with TensorFlow 2 and Keras - Second...
Paperback
$$
Deep Learning with TensorFlow and Keras: Build and...
Kindle Edition
$$
Deep Learning with TensorFlow 2 and Keras:...
Kindle Edition
$$
Cover image
An Introduction to Statistical Learning
Save
Provides a comprehensive introduction to statistical learning with R. It covers the basics of statistical learning, such as supervised learning, unsupervised learning, and reinforcement learning. This book valuable resource for anyone who wants to learn more about statistical learning.
An Introduction to Statistical Learning: with...
Hardcover
$$$
Cover image
The Elements of Statistical Learning
Save
Provides a comprehensive introduction to the elements of statistical learning. It covers the basics of statistical learning, such as supervised learning, unsupervised learning, and reinforcement learning. This book valuable resource for anyone who wants to learn more about 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
$$$
Cover image
Deep Learning
Save
Provides a comprehensive introduction to deep learning. It covers the basics of deep learning, such as neural networks, convolutional neural networks, and recurrent neural networks. This book valuable resource for anyone who wants to learn more about deep learning.
Deep Learning
Paperback
$$$
Deep Learning (Adaptive Computation and Machine...
Hardcover
$$$
Deep Learning (Adaptive Computation and Machine...
Kindle Edition
$$$
Cover image
Statistical Inference
Save
Provides a comprehensive introduction to statistical inference. It covers the basics of statistical inference, such as point estimation, interval estimation, and hypothesis testing. This book valuable resource for anyone who wants to learn more about statistical inference.
Statistical Inference (Chapman & Hall/CRC Texts in...
Hardcover
$$$
Statistical Inference
Hardcover
$$$
Statistical Inference
Hardcover
$$$
Times Higher Education Abroad excellent selection...
Paperback
$$
Statistical Inference (Chapman & Hall/CRC Texts in...
Kindle Edition
$$$
Statistical Inference
Kindle Edition
$$$
Introduction to Linear Algebra
Save
Provides a comprehensive introduction to linear algebra. It covers the basics of linear algebra, such as vectors, matrices, and linear transformations. This book valuable resource for anyone who wants to learn more about linear algebra.
Introduction to Linear Algebra (Gilbert Strang, 5)
Hardcover
$$$
Introduction to Linear Algebra (Gilbert Strang)
Hardcover
$$$$
Introduction to Linear Algebra, Third Edition
Hardcover
$$$
Introduction to Linear Algebra
Hardcover
$$$
Introduction To Linear Algebra, 5Th Edn
Hardcover
$$$
Introduction to Linear Algebra
Paperback
$$$$
Introduction to Linear Algebra
Hardcover
$$$
Cover image
Graphical Determination of Forces in Engineering...
Save
Provides a comprehensive introduction to calculus. It covers the basics of calculus, such as limits, derivatives, and integrals. This book valuable resource for anyone who wants to learn more about calculus.
“Collector’s Edition” Graphical Determination of...
Leather Bound
$$$$
Graphical Determination of Forces in Engineering...
Leather Bound
$$$
Cover image
Functional Analysis
Save
Provides a comprehensive introduction to real analysis. It covers the basics of real analysis, such as sequences, limits, and continuity. This book valuable resource for anyone who wants to learn more about real analysis.
Functional Analysis: Introduction to Further Topics...
Hardcover
$$$
Functional Analysis: Introduction to Further Topics...
Kindle Edition
$$$
Cover image
R Packages
Save
Provides an in-depth look at the advanced features of R. It covers topics such as data manipulation, statistical modeling, and machine learning. This book valuable resource for anyone who wants to learn more about R.
R Packages
Paperback
Check
R Packages
Kindle Edition
Check
Cover image
R Cookbook
Save
Provides a collection of recipes for common R tasks. It covers topics such as data manipulation, statistical modeling, and machine learning. This book valuable resource for anyone who wants to learn more about R.
R Cookbook: Proven Recipes for Data Analysis,...
Paperback
$$$
R Cookbook: Proven Recipes for Data Analysis,...
Kindle Edition
$$$

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Course image
Rating
4.5 Filled star Filled star Filled star Filled star Half star
Effort
10.5 total hours
Via
Udemy
Instructors
Kirill Eremenko
SuperDataScience Team
Ligency Team
Language
English
Traffic lights
Read about what's good
what should give you pause
and possible dealbreakers
Covers basic programming principles, making it suitable for beginners
Emphasizes practical application through hands-on examples
Covers essential R functions and packages for data analysis
Provides step-by-step guidance and exercises for reinforcement
Assumes no prior programming or statistical knowledge
May require additional resources for more advanced concepts
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