DATA SCIENCE COMPUTING PROJECT
The development of tools necessary for collecting, managing, and analyzing large data sets. Examples of techniques and programs used include Linux, R, distributed computing, text editor(s), git/github, and other related tools. Work in teams to research, develop, write, and make presentations related to a variety of data analysis projects.
Prerequisites & course information
Requisites: ( STAT 240 or 303 ) and ( COMP SCI 200 , 220 , 300 , or placement into COMP SCI 300 ), or graduate/professional standing Course Designation: Core GenEd - Natural Science & Wellness Breadth - Natural Science Level - Intermediate L&S Credit - L&S Liberal Arts and Science Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Collect and manage data and write programs and documentation viatools suited to large computations including an operating system, aneditor, and a version control system. Audience: Undergraduate 2. Run analyses too large for a laptop on cluster, grid, and/or cloudcomputing environments. Audience: Undergraduate 3. Work in teams to research, develop, write, and make presentations ona data analysis proposal, a draft data analysis, and a revised dataanalysis. Audience: Undergraduate Learning Outcomes: 1. Collect and manage data and write programs and documentation viatools suited to large computations including an operating system, aneditor, and a version control system. Audience: Undergraduate 2. Run analyses too large for a laptop on cluster, grid, and/or cloudcomputing environments. Audience: Undergraduate 3. Work in teams to research, develop, write, and make presentations ona data analysis proposal, a draft data analysis, and a revised dataanalysis. Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027