140.776.71
Statistical Computing
 Location:
 Internet
 Term:
 1st term
 Department:
 Biostatistics
 Credits:
 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:
 Contact:
 Stephanie Hicks
 Frequency Schedule:
 One Year Only
 Resources:
 Prerequisite:
140.621 or equivalent
 Description:

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:
 Install and configure software necessary for a statistical programming environment and with version control
 Discuss generic programming language concepts as they are implemented in a highlevel 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
 Build and organize a software package with documentation for publishing on the internet
 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