Data Science II: Machine Learning

4 credits

Provides theoretical and practical introduction to modern techniques for the analysis of large-scale, heterogeneous data. Covers key concepts in inferential statistics, supervised and unsupervised machine learning, and network analysis. Students learn functional, procedural, and statistical programming techniques for working with real-world data. Prerequisite: IMT 573.