Skip to main content

Statistical Computing

1st Term
Academic Year
2021 - 2022
Instruction Method
Synchronous Online
Class Time(s)
Tu, Th, 1:30 - 2:50pm
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Contact Name
Frequency Schedule
One Year Only
Next Offered
Only offered in 2021

140.621 or equivalent

Covers practical issues in programming and other computer skills required for the research and application of statistical methods. Includes programming in R and the tidyverse, version control, coding best practices, introduction to data visualizations, leveraging Python from R, introduction to basic statistical computing algorithms, creating R packages with documentation, debugging, organizing and commenting code. 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 and with version control
  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. Build and organize a software package with documentation for publishing on the internet
  6. Discuss and implement basic statistical computing algorithms for optimization, linear regression, and Monte Carlo
Methods of Assessment
This course is evaluated as follows:
  • 100% Project(s)