DATA SCIENCE PROGRAMMING I
Introduction to Data Science programming using Python. No previous programming experience required. Emphasis on analyzing real datasets in a variety of forms and visual communication.
Prerequisites & course information
Requisites: Satisfied Quantitative Reasoning (QR) A or declared in the Professional Capstone Program in Computer Sciences. Not open to students with credit for COMP SCI 301. Course Designation: Core GenEd - Mathematics & Quantitative Reasoning Comm QR - Quantitative Reasoning B Breadth - Natural Science Level - Elementary 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. 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