Introduction to Data Science

Intro to data science and statistical thinking. Learn to explore, visualize, and analyze data to understand natural phenomena, investigate patterns, model outcomes, and make predictions, and do so in a reproducible and shareable manner. Gain experience in data wrangling and munging, exploratory data analysis, predictive modeling, data visualization, and effectively communicating results. Work on problems and case studies inspired by and based on real-world questions and data. The course will focus on the R statistical computing language.

Regression Analysis

Learn approaches for analyzing multivariate data sets, emphasizing analysis of variance, linear regression, and logistic regression. Learn techniques for checking the appropriateness of proposed models, such as residual analyses and case influence diagnostics, and techniques for selecting models. Gain experience dealing with the challenges that arise in practice through assignments that utilize real-world data. This class emphasizes data analysis over mathematical theory.

Regression Analysis: Theory and Applications

In STA 221, students will learn how linear and logistic regression models are used to explore multivariable relationships, apply these methods to answer relevant and engaging questions using a data-driven approach, and learn the mathematical underpinnings of the models. Students will develop computing skills to implement a reproducible data analysis workflow and gain experience communicating statistical results. Throughout the semester, students will work on a team project where they will develop a research question, answer it using methods learned in the course, and share results through a written report and presentation.

Generalized Linear Models

In STA 310 students will be introduced to generalized linear models (GLMs), a broad modeling framework that includes linear and logistic models, among others. Students will learn the basic theory of GLMs and how they can used to model a variety of response variables with non-normal distributions. Students will also learn an extension of GLMs that can be applied to modeling data with correlated observations, such as data with repeated measures.

Designing the Data Science Classroom

There has been significant innovation in introductory statistics and data science courses to equip students with the statistical, computing, and communication skills needed for modern data analysis. Innovating subsequent courses is also important, so students can continue developing these skills beyond the first course. In this session, we’ll present a modern approach to teaching undergraduate regression analysis, the second statistics course for many students. We’ll share strategies for using real-world data sets and examples, teaching modern computing skills, and incorporating non-technical skills such as writing and effective collaboration as part of the course.