IBM - Introduction to R Programming for Data Science
4.8 /5- Offered byCoursera
- Public/Government Institute
Introduction to R Programming for Data Science at Coursera Overview
Duration | 10 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course  |
Credential | Certificate |
Introduction to R Programming for Data Science at Coursera Highlights
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Beginner Level
- Approx. 10 hours to complete
- English Subtitles: English
Introduction to R Programming for Data Science at Coursera Course details
- 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.
- This course is part of multiple programs
- This course can be applied to multiple Specializations or Professional Certificates programs. Completing this course will count towards your learning in any of the following programs:
- IBM Data Analytics with Excel and R Professional Certificate
- Applied Data Science with R Specialization
Introduction to R Programming for Data Science at Coursera Curriculum
R Basics
Welcome to Introduction to R Programming for Data Science
Introduction to R Language
Basic Data Types
Math, Variables, and Strings
R Environment
Introduction to RStudio
Writing and Running R in Jupyter Notebooks
Summary & Highlights
Practice Quiz
Graded Quiz
Common Data Structures
Vectors and Factors
Vector Operations
Lists
Arrays and Matrices
Data Frames
Summary & Highlights
Practice Quiz
Graded Quiz
R Programming Fundamentals
Conditions and Loops
Functions in R
String Operations in R
Regular Expressions
Date Format in R
Debugging
Summary & Highlights
Practice Quiz
Graded Quiz
Working with Data
Reading CSV, Excel, and Built-in Datasets
Reading Text Files in R
Writing and Saving to Files
HTTP Request and REST API
Web Scraping in R
Summary & Highlights
Practice Quiz
Graded Quiz
Final Project
Download and Complete the Tasks in a Notebook
Congratulations & Next Steps
Credits and Acknowledgments
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