Skip Navigation

Course Directory

Statistical Computing

1st term
3 credits
Academic Year:
2022 - 2023
Instruction Method:
Synchronous Online with Some Asynchronous Online
Class Times:
  • Tu Th,  1:30 - 2:50pm
Auditors Allowed:
Yes, with instructor consent
Undergrads Allowed:
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Stephanie Hicks

140.621 or equivalent


Covers the basics of practical issues in programming and other computer skills required for the research and application of statistical methods. Includes programming in R and the tidyverse, data ethics, best practices for coding and reproducible research, introduction to data visualizations, best practices for working with special data types (dates/times, text data, etc), best practices for storing data, basics of debugging, organizing and commenting code, basics of leveraging Python from R. Topics in statistical data analysis provide working examples.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Install and configure software necessary for a statistical programming environment
  2. Discuss generic programming language concepts as they are implemented in a high-level statistical language
  3. Write and debug code in base R and the tidyverse (and integrate code from Python modules)
  4. Build basic data visualizations using R and the tidyverse
  5. Discuss best practices for coding and reproducible research, basics of data ethics, basics of working with special data types, and basics of storing data
Methods of Assessment:

This course is evaluated as follows:

  • 100% Project(s)

Instructor Consent:

No consent required

Special Comments:

Please note: This is the online section of a course that is also offered onsite. Students will need to commit to the modality for which they register.