DS 3010: Applied Data Modeling and Predictive Analysis
Credits: 3. Contact Hours: Lecture 3.
Prereq: DS 2020 and STAT 1010, STAT 1040, STAT 1050, STAT 2010, STAT 2260, STAT 2310, STAT 3050, STAT 3220 or STAT 3300
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)