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DS 301: Applied Data Modeling and Predictive Analysis

Cr. 3. Alt. F., offered irregularly.Alt. S., offered irregularly.

Prereq: DS 201, one of STAT 101, 104, 105, 201, 226, 231, 305, 322, 330
Elements of predictive analysis such as training and test sets; feature extraction; survey of algorithmic machine learning techniques, e.g. decision trees, Naïve Bayes, and random forests; survey of data modeling techniques, e.g. linear model and regression analysis; assessment and diagnostics: overfitting, error rates, residual analysis, model assumptions checking; communicating findings to stakeholders in written, oral, verbal and electronic form, and ethical issues in data science. Participation in a multi-disciplinary team project.