Steve Skiena

This multidisciplinary course introduces both theoretical concepts and practical approaches to extract knowledge from data. Topics include linear algebra, probability, statistics, machine learning, and programming. Using large data sets collected from real-world problems in areas of science, technology, and medicine, we introduce how to preprocess data, identify the best model that describes the data, make predictions, evaluate the results, and finally report the results using proper visualization methods. This course also teaches state-of-the art tools for data analysis, such as Python and its scientific libraries.

Bulletin Link

CSE351