DS 3010: Applied Data Modeling and Predictive Analysis
Credits: 3. Contact Hours: Lecture 3.
Prereq: DS 2010, DS 2020 and STAT 1010, STAT 1040, STAT 2010, STAT 2026, STAT 3005, STAT 3022, STAT 3030, or STAT 3031
Elements of predictive analysis such as training and test sets; feature extraction; survey of algorithmic machine learning techniques, e.g. decision trees, Naive 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.
(Typically Offered: Fall, Spring)