Teaching

Three principles for modernizing an undergraduate regression analysis course

As data have become more prevalent in many fields, it is imperative that undergraduate students are equipped with the skills necessary for working with data in this modern environment. There has been significant innovation in introductory statistics and data science courses; however, there has not been as much focus on innovating subsequent courses. In this talk I will share innovations to an undergraduate regression analysis course, the second statistics course taken by many students from a variety of disciplines.

Effective communication as a learning objective in an intermediate statistics course

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.

Using Quarto for Making and Organzing Teaching Materials

In addition to creating reproducible documents, Quarto offers a rich suite of options for creation slide decks, books, and websites, allowing instructors to create all of their teaching materials reproducibly. In this talk we will demo how we’ve been using Quarto to create and organize teaching materials, introduce resources and templates for getting started with Quarto for organizing your own teaching materials, and share best practices.

Teaching intro data science

Talk for the 2022 Preparing to Teach Workshop about teaching introductory data science and how it differs from teaching traditional introductory statistics.

Modernizing the undergraduate regression analysis course

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.

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.

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.

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.

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.