Statistics for Data Science

1 Course Unit

The course covers the methodological foundations of data science, emphasizing basic concepts in statistics and learning theory, but also modern methodologies. Learning of distributions and their parameters. Testing of multiple hypotheses. Linear and nonlinear regression and prediction. Classification. Uncertainty quantification. Model validation. Clustering. Dimensionality reduction. Probably approximately correct (PAC) learning. Such theoretical concepts are further complemented by exempla r applications, case studies (datasets), and programming exercises (in Python) drawn from electrical engineering, computer science, the life sciences, finance, and social networks. Fall Also Offered As: ENM 4020 Mutually Exclusive: ESE 5420 Prerequisites: Students must take ESE 3010 or STAT 4300 before taking ESE 4020. 1 Course Unit