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Rafael Irizarry

The first in our Professional Certificate Program in Data Science, this course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you'll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

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The first in our Professional Certificate Program in Data Science, this course will introduce you to the basics of R programming. You can better retain R when you learn it to solve a specific problem, so you'll use a real-world dataset about crime in the United States. You will learn the R skills needed to answer essential questions about differences in crime across the different states.

We'll cover R's functions and data types, then tackle how to operate on vectors and when to use advanced functions like sorting. You'll learn how to apply general programming features like "if-else," and "for loop" commands, and how to wrangle, analyze and visualize data.

Rather than covering every R skill you might need, you'll build a strong foundation to prepare you for the more in-depth courses later in the series, where we cover concepts like probability, inference, regression, and machine learning. We help you develop a skill set that includes R programming, data wrangling with dplyr, data visualization with ggplot2, file organization with UNIX/Linux, version control with git and GitHub, and reproducible document preparation with RStudio.

The demand for skilled data science practitioners is rapidly growing, and this series prepares you to tackle real-world data analysis challenges.

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What's inside

Learning objectives

  • Basic r syntax
  • Foundational r programming concepts such as data types, vectors arithmetic, and indexing
  • How to perform operations in r including sorting, data wrangling using dplyr, and making plots

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Designed for learners with no prior coding experience
Helps learners develop foundational R programming concepts
Builds a strong foundation for more advanced data science courses
Covers essential data science skills such as data wrangling, visualization, and analysis
Taught by instructors recognized for their expertise in data science education

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Reviews summary

Well-received r programming course for beginners

Learners say that this well-received course is a great introduction to R programming. They describe it as engaging and well-paced, featuring clear, concise video lectures. The practical exercises (assessments) help reinforce the concepts. Overall, the course is recommended for those new to programming or those looking to refresh their skills.
Practical exercises reinforce concepts.
""The course covers most of the basics you'll need in R.""
""Practice exercises were done in the Datacamp console which I appreciated.""
""If you have some programming experience you will certainly face no problems.""
Organized with clear video lectures.
""The course is well designed and is very good introduction to R.""
""I did not think the course was particularly hard.""
""The course is well structured, video lectures are good.""
Easy to follow for those new to R.
""I have had a little experience with data science and R language in general, and the course sufficed as a refresher for me.""
""If you are new to programming this is also quite doable""
""I believe it provides a fairly simple introduction to R programming.""

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 Science: R Basics with these activities:
Seek guidance from an experienced R programmer
Facilitates personalized learning and access to expert advice.
Browse courses on R Programming
Show steps
  • Identify R programmers in your network or online communities.
  • Reach out to potential mentors and briefly introduce yourself and your learning goals.
Follow along with the RStudio Tutorial for Beginners
Refreshes R programming syntax and foundational concepts.
Browse courses on R Programming
Show steps
  • Visit the RStudio website and navigate to the tutorial section.
  • Follow the instructions in the tutorial to set up RStudio and your first R project.
  • Complete the exercises in the tutorial to practice basic R syntax and operations.
Complete R exercises on DataCamp
Provides additional practice with R syntax and data manipulation techniques.
Browse courses on R Programming
Show steps
  • Create a free account on DataCamp if you don't have one already.
  • Find the 'Intro to R' course or similar.
  • Complete the exercises and quizzes in the course to test your understanding of R concepts.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Create a cheat sheet of R functions and syntax
Solidifies understanding of R syntax and functions by creating a reference guide.
Browse courses on R Programming
Show steps
  • Gather a list of the most important R functions and syntax.
  • Create a visually appealing cheat sheet that organizes the information clearly.
Join a study group or online forum for R learners
Provides opportunities to collaborate with other learners and clarify concepts.
Browse courses on R Programming
Show steps
  • Search for R study groups or online forums.
  • Join a group that aligns with your learning style and schedule.
  • Participate in discussions, ask questions, and share your understanding of R concepts.
Solve coding challenges on LeetCode
Sharpens problem-solving skills and reinforces R syntax and data manipulation techniques.
Browse courses on R Programming
Show steps
  • Create an account on LeetCode if you don't have one already.
  • Select the 'Easy' or 'Medium' difficulty problems tagged with 'R'.
  • Attempt to solve the problems and review the solutions to learn from your mistakes.
Analyze a real-world dataset using R
Applies R skills to a practical problem, enhancing data analysis and visualization abilities.
Browse courses on R Programming
Show steps
  • Find a publicly available dataset that interests you.
  • Load the dataset into R and explore its structure.
  • Perform data cleaning and manipulation to prepare it for analysis.
  • Use R functions and libraries to analyze the data and draw conclusions.
  • Create data visualizations to present your findings.

Career center

Learners who complete Data Science: R Basics will develop knowledge and skills that may be useful to these careers:
Data Analyst
Data Analysts clean, manage, and interpret data to draw relevant conclusions. The course teaches the foundational concepts of R programming, which is essential for data analysis. The skills gained from this course will be useful for anyone looking to pursue a career as a Data Analyst.
Data Scientist
The Data Scientist builds and uses models to make predictions. With the help of this course, a Data Scientist will be able to perform operations in R, such as data wrangling and visualization. These skills will help them to analyze data and build models that can be used to make better decisions.
Statistician
Statisticians use mathematical and statistical methods to collect, analyze, interpret, and present data. This course introduces the basics of R programming, which is used in many statistical analyses. Taking this course may be helpful for those interested in a career as a Statistician.
Business Analyst
Business Analysts use data and analysis to help businesses make better decisions. This course provides a foundation in R programming, which is beneficial for anyone looking to pursue a career as a Business Analyst.
Financial Analyst
Financial Analysts use financial data to make investment recommendations and provide guidance to clients. This course introduces the basics of R programming, which is beneficial for anyone looking to pursue a career as a Financial Analyst.
Market Researcher
Market Researchers gather and analyze data to understand market trends and consumer behavior. This course introduces the basics of R programming, which is beneficial for anyone looking to pursue a career as a Market Researcher.
Operations Research Analyst
Operations Research Analysts use mathematical and analytical methods to solve complex problems in business and industry. This course introduces the basics of R programming, which is beneficial for anyone looking to pursue a career as an Operations Research Analyst.
Software Engineer
Software Engineers design, develop, and maintain software applications. This course introduces the basics of R programming, which can be beneficial for those looking to pursue a career as a Software Engineer.
Data Engineer
Data Engineers design, build, and maintain data pipelines and infrastructure. This course introduces the basics of R programming, which can be beneficial for those looking to pursue a career as a Data Engineer.
Database Administrator
Database Administrators manage and maintain databases. This course introduces the basics of R programming, which can be beneficial for those looking to pursue a career as a Database Administrator.
Information Security Analyst
Information Security Analysts protect computer systems and networks from unauthorized access, use, disclosure, disruption, modification, or destruction. This course introduces the basics of R programming, which can be beneficial for those looking to pursue a career as an Information Security Analyst.
Actuary
Actuaries use mathematical and statistical methods to assess risk and uncertainty. This course introduces the basics of R programming, which can be beneficial for those looking to pursue a career as an Actuary.
Epidemiologist
Epidemiologists investigate the causes of disease and other health problems in populations. This course introduces the basics of R programming, which can be beneficial for those looking to pursue a career as an Epidemiologist.
Biostatistician
Biostatisticians apply statistical methods to solve problems in biology and medicine. This course introduces the basics of R programming, which can be beneficial for those looking to pursue a career as a Biostatistician.
Quantitative Analyst
Quantitative Analysts use mathematical and statistical methods to analyze financial data and make investment decisions.

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 Science: R Basics.
Provides a comprehensive introduction to R programming for data science. It covers the basics of R syntax, data types, and data structures, as well as more advanced topics such as data wrangling, visualization, and statistical modeling. This book great resource for anyone who wants to learn R for data science.
Comprehensive reference for R programming. It covers everything from basic R syntax to advanced R topics such as object-oriented programming and statistical modeling. This book great resource for anyone who wants to learn R in depth.
Comprehensive guide to ggplot2, a popular R package for data visualization. It covers everything from basic ggplot2 concepts to advanced ggplot2 techniques. This book great resource for anyone who wants to learn how to create beautiful and informative data visualizations with R.
Practical guide to R programming. It covers the basics of R syntax and data structures, as well as more advanced topics such as data visualization and statistical modeling. This book great resource for anyone who wants to learn R for data analysis.
Comprehensive guide to data manipulation with R. It covers everything from basic data cleaning to advanced data wrangling techniques. This book great resource for anyone who wants to learn how to manipulate data in R.
Comprehensive guide to deep learning with R. It covers everything from basic deep learning concepts to advanced deep learning algorithms. This book great resource for anyone who wants to learn how to use R for deep learning.
Comprehensive guide to R programming. It covers everything from the basics of R syntax to advanced topics such as object-oriented programming and data analysis. This book great resource for anyone who wants to learn R in depth.
Collection of recipes for common R tasks. It covers everything from data manipulation to statistical analysis to data visualization. This book great resource for anyone who wants to learn R or who needs a quick reference for R tasks.
Comprehensive guide to data science with R. It covers the basics of R programming, data analysis, and machine learning. This book great resource for anyone who wants to learn data science with R.
Gentle introduction to R programming. It covers the basics of R syntax and data structures, as well as more advanced topics such as data visualization and statistical modeling. This book great resource for anyone who wants to learn R with no prior programming experience.
Gentle introduction to R programming. It covers the basics of R syntax and data structures, as well as more advanced topics such as data visualization and statistical modeling. This book great resource for anyone who wants to learn R with no prior programming experience.

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