DATA SCIENCE PROGRAMMING II
Intermediate approach to Data Science programming using Python. Experience with basic tabular analysis in Python is assumed. Learn to implement data structures (e.g., graphs) to efficiently represent datasets. Software-engineering tools such as version control and Python virtual environments will be introduced, with an emphasis on reproducibility of analysis. Tracing and A/B testing will be introduced as techniques for generating meaningful datasets. Introduces basic classification, clustering, optimization, and simulation techniques. Plotting and visual communication will be emphasized throughout the course.
Prerequisites & course information
Requisites: COMP SCI 220 , 300 , 319 , graduate/professional standing, or declared in the Computer Sciences for Professionals Capstone Certificate Course Designation: Core GenEd - Natural Science & Wellness + Lab 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. 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. conduct data science activities aware of and according to policy, privacy, security and ethical considerations Audience: Undergraduate 6. demonstrate oral, written and visual communication skills related to data science 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. conduct data science activities aware of and according to policy, privacy, security and ethical considerations Audience: Undergraduate 6. demonstrate oral, written and visual communication skills related to data science Audience: Undergraduate
Sources
- Course catalogue ↗2026-2027