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Statistical Computing

East Baltimore
1st Term
Academic Year
2023 - 2024
Instruction Method
Class Time(s)
Tu, Th, 9:00 - 10:20am
Auditors Allowed
Yes, with instructor consent
Available to Undergraduate
Grading Restriction
Letter Grade or Pass/Fail
Course Instructor(s)
Frequency Schedule
Every Year
Next Offered
2024 - 2025

140.621 or equivalent
Note: For most classes we will be writing code in R. By the end of the course, you will have installed many R packages, checked their documentation, adapted examples, and written R code yourself. If you know the basics of programming in another language or the basics of R, you will be equipped to handle the material in the course, though if you haven't written code in any language in a few years you will face a steeper learning curve early on.

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)