0 or 3 credits
Fall 2025 Practice Study Observation Lecture Lower Division
This course gives a broad introduction to the most important data structures and algorithms in computer science. The emphasis is on data structures and their use in algorithms relevant for data science and AI and their applications. The course focuses on developing and comparing efficient implementations, assessing suitability of data structures for massive data sets, and understanding effective use, modifications, and extensions. This course will not fulfill CS 25100 requirement for any Computer Science major or minor.
Learning Outcomes1Apply an asymptotic analysis to given code and explain its significance.
2Analyze asymptotic performance of data structures and algorithms with respect to time and space.
3Describe different implementations of a data structure and explain when what implementation is most efficient.
4Demonstrate how to add functionality to a data structure to efficiently handle new operations and queries.
5Assess how the choice of data structures and algorithm design methods impacts the performance of programs.
6Make effective and appropriate use of data structures such as stacks, queues, linked lists, hash tables, Bloom filters, priority queues, dictionaries, search trees, tries, and graphs.
Prerequisites
Restrictions
made by Purdue students.