Data Science (DS)
DS 101, +EXPLORING CAREERS IN DATA SCIENCE, 1 Credit
Explore the Data Science major and the interdisciplinary options within the major. Examine career goals and pathways to data science careers. Explore the concept of professional development. Expose students to data science applications in various fields and prepare them to be critical thinkers of future career paths.
Attributes: CSC1 – Core Ed - Beyond OSU Career Preparation
Prerequisite: SCI 100 (may be taken concurrently) with D- or better or SCI 300 (may be taken concurrently) with D- or better or CORE 100 (may be taken concurrently) with D- or better or CORE 300 (may be taken concurrently) with D- or better or BA 100 (may be taken concurrently) with D- or better or BA 300 (may be taken concurrently) with D- or better or ENGR 110 (may be taken concurrently) with D- or better or ENGR 310 (may be taken concurrently) with D- or better or LA 100 (may be taken concurrently) with D- or better or LA 300 (may be taken concurrently) with D- or better
DS 453, BAYESIAN MODELS FOR DATA SCIENCE, 4 Credits
Introduces main concepts of Bayesian analysis from the statistical foundations to model implementation. Reviews and implements a variety of widely used models from a Bayesian perspective including linear regression, Poisson and Negative Binomial regression, and logistic regression. Emphasizes the computational implementation of these models and discusses numerical approximations for posterior inference, including Markov Chain Monte Carlo approaches.
DS 455, CAUSAL INFERENCE FOR EXPERIMENTAL AND OBSERVATIONAL DATA, 3 Credits
Imparts methods to make causal conclusions from data. Builds foundations of causal inference for randomized trials through the perspective of potential outcomes. Extends this foundation to causal inference for observational studies. Develops methodologies for causal inference using regression modeling of real and simulated data for randomized experiments and observational studies.
DS 553, BAYESIAN MODELS FOR DATA SCIENCE, 4 Credits
Introduces main concepts of Bayesian analysis from the statistical foundations to model implementation. Reviews and implements a variety of widely used models from a Bayesian perspective including linear regression, Poisson and Negative Binomial regression, and logistic regression. Emphasizes the computational implementation of these models and discusses numerical approximations for posterior inference, including Markov Chain Monte Carlo approaches.