Introduction to R Programming for Data Science from Coursera

When working in the data science field you will definitely become acquainted with the R language and the role it plays in data analysis. This course introduces you to the basics of the R language such as data types, techniques for manipulation, and how to implement fundamental programming tasks.

You will begin the process of understanding common data structures, programming fundamentals and how to manipulate data all with the help of the R programming language.

The emphasis in this course is hands-on and practical learning . You will write a simple program using RStudio, manipulate data in a data frame or matrix, and complete a final project as a data analyst using Watson Studio and Jupyter notebooks to acquire and analyze data-driven insights.

No prior knowledge of R, or programming is required.

What's inside

Syllabus

R Basics

Regardless of the programming language you use, all share some commonalities. For example, you’ll likely need to perform basic operations on different data types, like applying mathematical equations to numeric data. You’ll also need an environment in which to write your code, anbbd most modern integrated development environments (or IDEs) provide features that make writing code easier, like syntax checking, color coding, and integrated help. This module introduces you to the R language, its common data types, and techniques for manipulating them. You’ll also learn about the role of the R interpreter and how it transforms code into executable objects. Finally, you’ll be introduced to two of the most common IDEs for R development: RStudio and Jupyter Notebook.

Common Data Structures

The R language supports many types of data structures that you can use to organize and store values in your code, including vectors, factors, lists, arrays, matrices, and data frames. Each data structure type serves a specific purpose and can contain specific kinds of data. So, it’s important to understand the differences between them so you can make the right choice based on your scenario. In this module, you’ll learn about the types of data you can store in each data structure and how to add, remove, or manipulate its contents.

R Programming Fundamentals

As with most programming languages, R supports coding features that you can use to control the flow of program execution, define functions that can perform specific tasks, work with common data types, like strings and dates, and make your code more robust by intercepting likely errors and handling them before they interrupt the execution of your code. In this module, you’ll learn how to implement these fundamental programming tasks in R.

Working with Data

Data is everywhere! The data you need to analyze may come from a traditional database, but it may also come from a variety of different sources and systems, and it may come to you in one or more formats. For example, your data might be in text, Excel, .JSON, or .XML files. Or it may not be stored in a file at all, but instead lives on the pages of a website. How will you take all these different file formats and load them into your R working environment? This module provides you with the tools you need to read data from some common file formats and sources into data objects that you can then use and combine with other data objects in your data analysis.

Final Project

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Geared towards beginners, this course establishes a knowledge foundation in R programming

Taught in a hands-on and practical manner, this course provides interactive learning opportunities

Familiarizes learners with the RStudio and Jupyter Notebook environments, widely used for R development

Covers essential data structures and programming fundamentals in R, providing a solid understanding of data manipulation and analysis techniques

Focuses on practical data analysis skills, preparing learners to handle various data sources and file formats commonly encountered in industry

Reviews summary

Excellent r programming course for data science

Learners say this course is largely positive with engaging assignments that build a strong foundation in R programming for data analysis and visualization. The course is well-organized and suitable for beginners and those with some prior programming knowledge. Instructors are knowledgeable and helpful and provide hands-on practice exercises that help reinforce learning and develop proficiency in R programming.

This course has a supportive community with helpful instructors and active discussion forums.

"The support from the instructor and the vibrant community fostered within the course forums enhance the overall learning journey."

"Thank you so much"

"Very good course, and easy to engage to finish it, thank you."

This course focuses on using R for data science applications, including data analysis, visualization, and statistical modeling.

"Excellent introductory course with very useful hands-on lab sessions."

"It's an amazing course with very interesting quizzes and assignments. A must for Data Science aspirants."

"Great course for learning R and Data Science!"

This course is suitable for beginners with no prior programming experience in R.

"I really enjoyed this course and think it was a great introduction to R."

"Iam new beginner to the R-programming. It was taught very well to make me understand R basic skills."

"Great course. What I love about it was the hand on pratice to help the student understand better what was said on the video."

This course is well-organized with clear lesson videos and practical exercises that help you learn R programming effectively.

"The videos were very comprehensive and understandable."

"Great content, perfectly structured for the once who already know programmig."

"I think the course is fairly easy to follow and touches many of the main basic topics for data analysis in R, I found it good."

This course includes practical exercises and hands-on labs that help you apply your learning and build proficiency in R programming.

"The labs provided good reinforcement for concepts learned in lessons."

"I really learned the basic of R from this course."

"Hands IBM and Coursera for offering a splendid opportunity to learn and transform skills"

Some learners may find that this course lacks depth and does not cover more advanced topics in R programming.

"Jumps around a lot, doesn't give all information needed to complete the final assignment."

"The course feels rather disjointed and not logically organized."

"Had a good time with the first two weeks then from the third certain topics is just a thrown pieces of information like "this is how it is done and u are supposed to comprehend it or save it whatever easier""

Some learners have reported technical difficulties with the course platform or software used in the course.

"The last session was short for me , but the course was amazing useful and very informative"

"The IBM Skills Lab can be difficult to use"

"I had alot of problems with the labs and I encountered questions and things in the labs that wouldn't even be introduced until a few videos 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 Introduction to R Programming for Data Science with these activities:

Organizing Your Course Materials

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Organize your course materials, such as notes, assignments, and quizzes. This will make it easier to study for exams and review the material later on.

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Create a dedicated folder for the course on your computer.
File your notes, assignments, and quizzes in the folder.
Use a note-taking app to organize your notes.

Reviewing Basic Statistics

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Review the basic concepts of statistics, such as mean, median, standard deviation, and hypothesis testing. This will provide a solid foundation for understanding the R language and its applications in data analysis.

Browse courses on Statistics

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Go over your notes from a previous statistics course.
Take a practice quiz or exam to test your understanding.
Read a book or article on basic statistics.

Reading 'R for Data Science'

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Read 'R for Data Science' by Hadley Wickham and Garrett Grolemund. This book provides a comprehensive overview of the R programming language and its applications in data science.

View R for Data Science: Import, Tidy, Transform,... on Amazon

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Purchase or borrow a copy of the book.
Read one chapter per week.
Complete the exercises at the end of each chapter.
Take notes and highlight important concepts.

Seven other activities

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Working Through RStudio Tutorials

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Follow along with the tutorials provided by RStudio to learn the basics of the R programming language and the RStudio IDE. This will give you a hands-on experience and help you become familiar with the tools you'll be using throughout the course.

Browse courses on RStudio

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Complete the RStudio Basics tutorial.
Work through the Data Manipulation tutorial.
Try out the Data Visualization tutorial.

Classify Data Structures

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Practice recognizing and using the different types of data structures in R.

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Write a basic program using R to identify the type of a given data structure.
Convert a data structure from one type to another.
Manipulate data within a specific data structure.

Joining a Study Group

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Join a study group with other students in the course. This will give you the opportunity to discuss the material, ask questions, and learn from your peers.

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Find a study group or create your own.
Meet regularly to discuss the course material.
Work together on assignments and projects.

Analyze a Data Set

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Gain hands-on experience loading and analyzing data using R.

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Load a data set from a file or database into R.
Clean and prepare the data for analysis.
Perform basic statistical analysis on the data.
Create a visualization that summarizes the results of the analysis.

Solving R Coding Challenges

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Attempt to solve coding challenges on websites like HackerRank or LeetCode. This will help you practice your R programming skills and learn how to apply them to real-world problems.

Browse courses on R Programming

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Sign up for a coding challenge website.
Choose a challenge that is appropriate for your skill level.
Attempt to solve the challenge on your own.
If you get stuck, consult the discussion forums or ask for help from a mentor.

Creating a Data Analysis Blog

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Start a blog where you share your experiences learning R and applying it to data analysis projects. This will help you solidify your understanding of the concepts and improve your communication skills.

Browse courses on Data Analysis

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Choose a blogging platform and set up your blog.
Write a series of posts on different aspects of R programming and data analysis.
Promote your blog on social media and other online platforms.
Engage with your readers and respond to comments.

Building a Data Analysis Portfolio

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Create a portfolio of data analysis projects that showcase your skills and knowledge. This will give you a tangible demonstration of your abilities and help you stand out to potential employers.

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Identify a few data analysis projects that you can complete.
Collect the data and clean it.
Analyze the data and draw conclusions.
Create a report or presentation to showcase your findings.

Career center

Learners who complete Introduction to R Programming for Data Science will develop knowledge and skills that may be useful to these careers:

Data Scientist

Data Scientists are responsible for collecting, analyzing, and interpreting data to help businesses make informed decisions. This course can help you develop the skills needed to become a successful Data Scientist, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Data Scientist.

See salaries and explore the career path for Data Scientist

Data Analyst

Data Analysts use data to solve business problems and make recommendations. This course can help you develop the skills needed to become a successful Data Analyst, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Data Analyst.

See salaries and explore the career path for Data Analyst

Statistician

Statisticians collect, analyze, and interpret data to help businesses and organizations make informed decisions. This course can help you develop the skills needed to become a successful Statistician, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Statistician.

See salaries and explore the career path for Statistician

Machine Learning Engineer

Machine Learning Engineers design and build machine learning models to solve business problems. This course can help you develop the skills needed to become a successful Machine Learning Engineer, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Business Analyst

Business Analysts use data to help businesses make better decisions. This course can help you develop the skills needed to become a successful Business Analyst, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Business Analyst.

See salaries and explore the career path for Business Analyst

Data Engineer

Data Engineers build and maintain the infrastructure that stores and processes data. This course can help you develop the skills needed to become a successful Data Engineer, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Data Engineer.

See salaries and explore the career path for Data Engineer

Software Engineer

Software Engineers design, develop, and maintain software applications. This course can help you develop the skills needed to become a successful Software Engineer, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Software Engineer.

See salaries and explore the career path for Software Engineer

Database Administrator

Database Administrators manage and maintain databases. This course can help you develop the skills needed to become a successful Database Administrator, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Database Administrator.

See salaries and explore the career path for Database Administrator

Operations Research Analyst

Operations Research Analysts use mathematical and statistical techniques to solve business problems. This course can help you develop the skills needed to become a successful Operations Research Analyst, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as an Operations Research Analyst.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

Financial Analysts use data to analyze financial performance and make investment recommendations. This course can help you develop the skills needed to become a successful Financial Analyst, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Financial Analyst.

See salaries and explore the career path for Financial Analyst

Market Researcher

Market Researchers collect and analyze data to understand consumer behavior. This course can help you develop the skills needed to become a successful Market Researcher, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Market Researcher.

See salaries and explore the career path for Market Researcher

Actuary

Actuaries use mathematical and statistical techniques to assess risk and uncertainty. This course can help you develop the skills needed to become a successful Actuary, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as an Actuary.

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Epidemiologist

Epidemiologists investigate the causes and patterns of disease. This course can help you develop the skills needed to become a successful Epidemiologist, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as an Epidemiologist.

See salaries and explore the career path for Epidemiologist

Biostatistician

Biostatisticians use statistical methods to analyze biological data. This course can help you develop the skills needed to become a successful Biostatistician, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Biostatistician.

See salaries and explore the career path for Biostatistician

Data Science Manager

Data Science Managers lead and manage teams of data scientists. This course can help you develop the skills needed to become a successful Data Science Manager, including data manipulation, programming, and statistical analysis. The course also provides hands-on experience with real-world data sets, which will help you prepare for the challenges of working as a Data Science Manager.

See salaries and explore the career path for Data Science Manager