Data Science

The growth and use of data is increasingly vital for many disciplines, from the natural sciences to the social sciences, and from business to the humanities. This course introduces students to data science and informatics that study how to collect, manage, process, analyze, and visualize data from a computational perspective. Topics include computational thinking, understanding different types of data, database techniques, and a variety of data analysis approaches. Focus will be on gaining a breadth of knowledge and the exploration of applications of data science and informatics.

Taught by

Adam Eck

An introduction to statistics and, in particular, linear models for students with some background in statistics and a good background in mathematics. Topics covered include exploratory data analysis, probability, sampling, estimation, statistical inference, multiple regression, one-factor and multi-factor analysis of variance, and analysis of categorical data via logistic regression. Statistical software is used heavily.

Taught by

Jeffrey (Jeff) Witmer

This is an introduction to the application of statistical methods to the estimation of economic models and the testing of economic hypotheses using non-experimental data. The central statistical tool is multivariate regression analysis. Topics covered include: the Gauss-Markov theorem, testing hypotheses, and correcting for heteroskedasticity, autocorrelation and simultaneous equation bias. In the weekly computer lab sessions econometric software is used to analyze real-world data.

Taught by

Maggie Brehm

This course introduces students to theoretical and methodological considerations for conducting research with diverse populations (e.g., ethnic and racial minorities). Content areas may include (but are not limited to) cultural socialization, interracial relationships, and mental health. Students will gain further experience with various stages of the research process (e.g., conceptualization, literature review, study design, data analysis, report-writing).

Taught by

Christine S. Wu