140.776.41
Statistical Computing
- Location:
- Internet
- Term:
- 1st term
- Department:
- Biostatistics
- Credits:
- 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:
- No
- Grading Restriction:
- Letter Grade or Pass/Fail
- Course Instructor:
- Contact:
- Stephanie Hicks
- Resources:
- Prerequisite:
140.621 or equivalent
- Description:
-
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:
- Install and configure software necessary for a statistical programming environment
- Discuss generic programming language concepts as they are implemented in a high-level statistical language
- Write and debug code in base R and the tidyverse (and integrate code from Python modules)
- Build basic data visualizations using R and the tidyverse
- 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.