Fundamentals of Data Science

3 credits · Undergraduate

Obtaining, manipulating, processing, cleaning, wrangling, visualizing, and analyzing data; effectively communicating results from data analyses. Imputation, multiple linear and logistic regression, regularization, dimensionality reduction, maximum likelihood, model selection, general linear model. Ethics, privacy, and security in data science. Statistical computing for data science in R. Prerequisites: One of: DS 1000, PSCI 2300; One of: DS 1100, CS 1100, 1101, 1104, 1105, or 2201; One of: DS 2100, BME 2400, BSCI 3270, CE 3300, ECON 1500, ECON 1510, MATH 2810, MATH 2821, PSY 2100, PSY-PC 2110, PSY-PC 2120, or SOC 2100. [3]