Introduction to R Programming for Data Science
- 4.8
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Course Summary
Learn the basics of programming in R to analyze data and create data visualizations. This course introduces you to R and its applications in data science.Key Learning Points
- Learn the basic syntax of R programming language
- Understand data structures and functions in R
- Create data visualizations using R packages
Job Positions & Salaries of people who have taken this course might have
- USA: $62,453
- India: ₹5,01,870
- Spain: €28,000
- USA: $62,453
- India: ₹5,01,870
- Spain: €28,000
- USA: $70,000
- India: ₹6,00,000
- Spain: €36,000
- USA: $62,453
- India: ₹5,01,870
- Spain: €28,000
- USA: $70,000
- India: ₹6,00,000
- Spain: €36,000
- USA: $110,000
- India: ₹10,00,000
- Spain: €50,000
Related Topics for further study
Learning Outcomes
- Understand the basic syntax of R programming language
- Create data visualizations using R packages
- Apply R in data analysis and data science projects
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of statistics
- Familiarity with programming concepts
Course Difficulty Level
BeginnerCourse Format
- Online
- Self-paced
Similar Courses
- Data Science Essentials
- Python Data Structures
- Applied Data Science with Python
Notable People in This Field
- Hadley Wickham
- Andrew Gelman
- David Robinson
Related Books
Description
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.
Knowledge
- Manipulate numeric and textual data types using the R programming language and RStudio or Jupyter Notebooks.
- Define and manipulate R data structures, including vectors, factors, lists, and data frames.
- Control program flow, define functions, perform character string and date operations, define regular expressions, and handle errors.
- Read, write, and save data files and scrape web pages using R.
Outline
- 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
Summary of User Reviews
Read reviews of Coursera's Introduction to R Programming for Data Science course. Users generally recommend this course and praise its comprehensive curriculum. One key aspect that many users appreciated is the hands-on approach to learning R programming.Pros from User Reviews
- Comprehensive curriculum
- Hands-on approach to learning R programming
- Great for beginners
Cons from User Reviews
- Some users found the course to be too basic
- Occasional technical issues with the platform
- Limited interaction with instructors
Yan Luo
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