Data Science: R Basics from edX

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.

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

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

Traffic lights

Read about what's good

what should give you pause

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

Reviews summary

Fundamentals of r programming for data science

According to learners, this course provides a strong foundation in R programming basics, making it an ideal starting point for data science novices. Students appreciate the introduction to essential tools like dplyr and ggplot2, as well as practical skills like Git and RStudio, which feel relevant to real-world work. Positioned as the first in a professional certificate program, it's seen as excellent preparation for subsequent courses. While it covers a breadth of topics using a real-world dataset, some students note its limited depth on complex subjects. The pace of the course can also vary, feeling rushed for some absolute beginners but slow for those with prior coding experience. Overall, it's a solid entry point to R for data science.

May feel too fast or too slow depending on background

"For a complete beginner, some sections moved quite quickly."

"If you have some coding background, the first few lectures might feel slow."

"I found the pace challenging but manageable with effort."

Excellent preparation for subsequent courses in the certificate

"This module perfectly set me up for the next courses in the certificate."

"It provides the necessary base knowledge to continue the Data Science program."

"I feel ready to tackle more advanced topics after this intro."

Introduces essential data science packages and tools

"Getting exposed to `dplyr` and `ggplot2` early on was very helpful."

"Learning about Git and RStudio alongside R felt like real-world prep."

"I appreciated the inclusion of UNIX basics, even if brief."

Builds essential basic R programming skills

"I got a really solid understanding of the basics from this course."

"This course covered the fundamental R concepts I needed to get started."

"It's a great starting point if you've never used R before."

Provides breadth but not deep dives on complex topics

"It covers many tools briefly, but I needed more detail on data wrangling techniques."

"Don't expect to be an expert in Git or UNIX after just this module."

"I understand this is just the basics; deeper dives are needed later."

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

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Facilitates personalized learning and access to expert advice.

Browse courses on R Programming

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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

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Refreshes R programming syntax and foundational concepts.

Browse courses on R Programming

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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

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Provides additional practice with R syntax and data manipulation techniques.

Browse courses on R Programming

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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

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Create a cheat sheet of R functions and syntax

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Solidifies understanding of R syntax and functions by creating a reference guide.

Browse courses on R Programming

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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

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Provides opportunities to collaborate with other learners and clarify concepts.

Browse courses on R Programming

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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

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Sharpens problem-solving skills and reinforces R syntax and data manipulation techniques.

Browse courses on R Programming

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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

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Applies R skills to a practical problem, enhancing data analysis and visualization abilities.

Browse courses on R Programming

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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.

See salaries and explore the career path for 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.

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 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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Quantitative Analyst

Quantitative Analysts use mathematical and statistical methods to analyze financial data and make investment decisions.

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