CS 07342 - Algorithms for the Data Scientist

Credits: 3

This course will cover the basic approaches and mindsets for analyzing and designing algorithms and data structures. Topics include the following: Worst and average case analysis. Recurrences and asymptotics. Efficient algorithms for sorting, searching, and selection. Data structures: binary search trees, heaps, hash tables. Algorithm design techniques: divide-and-conquer, dynamic programming, greedy algorithms, randomization. Algorithms for fundamental graph problems: minimum-cost spanning tree, connected components, topological sort, and shortest paths. Possible additional topics: network flow, string searching, amortized analysis, stable matchings, and approximation algorithms.

Prerequisite Courses: CS 04225 with a minimum grade of D- and CS 04222 with a minimum grade of D-
Course Attributes: CAT, UGRD