STATISTICAL METHODS FOR DATA SCIENCE

Prerequisites & course information

Requisites: Declared in Data Science MS or Data Engineering MS Course Designation: Grad 50% - Counts toward 50% graduate coursework requirement Repeatable for Credit: No Last Taught: Fall 2026 Learning Outcomes: 1. Strategize and select a regression equation, examine residuals, transform data, recognize biases due to excluded variables and measurement error Audience: Graduate 2. Conduct general linear modeling for exponential family data and specifically models for binary, count, and categorical data, perform model fitting and inference Audience: Graduate 3. Develop the concepts and relevant methodology and ability to design and analyze experiments Audience: Graduate 4. Use and interpret computer package for regression programs Audience: Graduate 5. Present clear data structure and analysis in the context of data drawn from real-world applications Audience: Graduate Learning Outcomes: 1. Strategize and select a regression equation, examine residuals, transform data, recognize biases due to excluded variables and measurement error Audience: Graduate 2. Conduct general linear modeling for exponential family data and specifically models for binary, count, and categorical data, perform model fitting and inference Audience: Graduate 3. Develop the concepts and relevant methodology and ability to design and analyze experiments Audience: Graduate 4. Use and interpret computer package for regression programs Audience: Graduate 5. Present clear data structure and analysis in the context of data drawn from real-world applications Audience: Graduate