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Statistical Computing

1st term
3 credits
Academic Year:
2021 - 2022
Instruction Method:
Synchronous Online
Class Times:
  • Tu Th,  1:30 - 2:50pm
Auditors Allowed:
Yes, with instructor consent
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructor:
Stephanie Hicks
Frequency Schedule:
One Year Only

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)

Instructor Consent:

No consent required