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DATA SCIENCE PROGRAMMING II

COMPSCI 320
Course Description

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

COMPSCI 220 (or COMP SCI 301 prior to Spring 2020), COMPSCI 300 , COMPSCI 319 , graduate/professional standing, or declared in the Computer Sciences for Professionals Capstone Certificate

Satisfies
Credits

4

Offered

Not Applicable

Grade Point Average
3.82

9.69% from Historical

Completion Rate
99.56%

2.98% from Historical

A Rate
80.7%

51.52% from Historical

Class Size
456

29.33% from Historical

2026 Summr Grade Distribution

Instructors (2026 Summr)

Sorted by ratings from Rate My Professors

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