INFO 2950 - Introduction to Data Science

(MQR-AS, SDS-AS)

Fall. 4 credits. Letter grades only.

Prerequisite: MATH 1710 or equivalent, CS 1110 or CS 1112, or permission of instructor. Information Science majors must complete this class prior to their senior year.

A. Koenecke.

INFO 2950 is an applied introductory course on the foundations of data science, focusing on using data to identify patterns, evaluating the strength and significance of relationships, and generating predictions using data. Topics covered include the core principles of statistical programming (such as data frames, Python/R packages, reproducible workflows, and version control), univariate and multivariate statistical analysis of small and medium-size datasets, regression methods, hypothesis testing, probability models, basic supervised and unsupervised machine learning, data visualization, and network analysis. Students will learn how to use data to make effective arguments in a way that promotes the ethical usage of data. Students who complete the course will be able to produce meaningful, data-driven analyses of real-world problems and will be prepared to begin more advanced work in data-intensive domains.