DATA SCIENCE PROGRAMMING I FOR RESEARCH
Introduction to Data Science programming using Python. In addition to a survey of programming basics (control flow and data structures), web scraping, database queries, and tabular analysis will be introduced. Projects will emphasize analyzing real datasets in a variety of forms and visual communication using plotting tools. Similar to COMP SCI 220 but the pedagogical style of the projects will be adapted to graduate students in fields other than computer science and data science. No previous programming experience required.
Prerequisites & course information
Requisites: Graduate/professional standing Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. integrate foundational concepts and tools from mathematics, computer science and statistics to solve data science problems Audience: Undergraduate 2. demonstrate competencies with tools and processes necessary for data management and reproducibility Audience: Undergraduate 3. produce meaning from data employing modeling strategies Audience: Undergraduate 4. demonstrate critical thinking related to data science concepts and methods Audience: Undergraduate 5. demonstrate oral, written and visual communication skills related to data science Audience: Undergraduate 6. manipulate quantitative information to create models, and/or devise solutions to problems using multi-step arguments, based on and supported by quantitative information Audience: Undergraduate Learning Outcomes: 1. integrate foundational concepts and tools from mathematics, computer science and statistics to solve data science problems Audience: Undergraduate 2. demonstrate competencies with tools and processes necessary for data management and reproducibility Audience: Undergraduate 3. produce meaning from data employing modeling strategies Audience: Undergraduate 4. demonstrate critical thinking related to data science concepts and methods Audience: Undergraduate 5. demonstrate oral, written and visual communication skills related to data science Audience: Undergraduate 6. manipulate quantitative information to create models, and/or devise solutions to problems using multi-step arguments, based on and supported by quantitative information Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027