Skip to Content

Data Science with R

Data management and manipulation is an essential task for data scientists who deal with data on a day-to-day basis. In recent years, many R packages were developed to tackle a wide variety of data science challenges. The focus of this course is the tidyverse suite of packages, which contain a large variety of tools to efficiently manage complex and big data. The materials in this course are essential for developing robust and efficient R programs in the data science field.

At the end of this course, students will be able to work independently to solve common data management tasks by developing their own customized and reusable programs in the data science field.

Key topics:

  • Review R Objects and RStudio
  • The tibble object
  • Pipes
  • Data Transformation
  • Data Input and Output
  • Tidy Data
  • Relational Data
  • Data and Time Manipulation
  • String Manipulations
  • Functions
  • Functional Programming

Practical experience:

  • Solve common data management tasks
  • Develop customized and reusable programs in the data science field
  • Grasp the techniques of efficient programming in R language

Software: R, a free software environment for statistical computing and graphics, or RStudio.

Optional reading: R for Data Science by Garrett Grolemund and Hadley Wickham.

Course typically offered: Online during our Spring and Fall academic quarters.

Prerequisites: Basic understanding of R language or complete Introduction to R Programming.

Next steps: Upon completion of this class, consider enrolling in other required coursework in the R for Data Analytics specialized certificate program..

Contact: For more information about this course, please contact

Course Number: CSE-41308
Credit: 3.00 unit(s)
Related Certificate Programs: Business Intelligence AnalysisData Mining for Advanced AnalyticsDatabase ManagementMachine Learning MethodsPython ProgrammingR for Data Analytics