Algorithmic Methods and Tools

Catalog description

This course teaches students how to formulate data science problems that arise in different applications that involve different types of data (tabular, sequence, network, matrix data); and introduces students to common strategies for formulating, and solving those problems. The course will cover (1) how to formulate data science problems (e.g., text analysis, biological sequence analysis, social network analysis, recommender systems) and how to evaluate alternative formulations, (2) common algorithmic methods and tools (as implemented in software libraries) for representing, processing, and sampling data, (3) how to apply the methods and tools to solve well-formulated data science problems. The course will also teach students how to understand and use results about the correctness, efficiency, and scalability of the techniques and tools, and they will learn to recognize when specific algorithmic tools can be used to improve the performance of their data science tasks. Through exercises, students will gain hands-on experience in problem formulation and solution development for data science problems that arise in applications.