DATA 260P
Fundamentals of Algorithms in Data Science
Covers fundamental concepts in the design and analysis of algorithms and is geared toward data science applications. Topics include greedy algorithms, deterministic and randomized graph algorithms, models of network flow, fundamental algorithmic techniques, and NP-completeness.
Topics
- greedy algorithms
- deterministic and randomized graph algorithms
- models of network flow
- fundamental algorithmic techniques
- and NP-completeness
Topics explicitly listed in official catalogue description
Prerequisites & course information
Covers fundamental concepts in the design and analysis of algorithms and is geared toward data science applications. Topics include greedy algorithms, deterministic and randomized graph algorithms, models of network flow, fundamental algorithmic techniques, and NP-completeness.
Sources
- Course catalogue ↗2026-2027