(Offered as STAT 370 and MATH 370) This course examines the theory underlying common statistical procedures including visualization, exploratory analysis, estimation, hypothesis testing, modeling, and Bayesian inference. Topics include maximum likelihood estimators, sufficient statistics, confidence intervals, hypothesis testing and test selection, non-parametric procedures, and linear models.

Requisite:

Statistical inference using what are called frequentist methods, where only the data are random, has long dominated the manner in which data are analyzed. The rise of computing power this century has unlocked Bayesian inference, a technique that blends prior knowledge with data, as an increasingly popular and powerful alternative approach. This course will explore the theory behind and application of Bayesian inference including situations where Markov Chain Monte Carlo (MCMC) simulation is employed.

Omitted 2023-24. Professor Donges

Text analytics is a form of natural language processing that utilizes computational systems to process, find patterns, classify, and model information contained within unstructured text documents. These methods are attractive because they can be applied to large collections of documents that would be infeasible to undertake by hand. In this course, students will interact with a variety of text sources with the goal of finding insights, identifying patterns, extracting meaning, and communicating results.

Statistical Communication is an important component of the capacity to "think with data." This course will integrate theoretical and practical aspects of statistics with a focus on communicating results and their implications. Students will gain experience clearly synthesizing and explaining complex data using diverse predictive and explanatory models.

This course will explore how to extract meaning from multivariate data sets through a variety of analytical methods, chosen based on the research question(s) being asked. Methods covered include principal components analysis and selected statistical and machine learning techniques, both supervised (e.g. classification trees and random forests) and unsupervised (e.g. clustering), with discussion of the modeling process. Instructors may opt to cover additional methods, such as factor analysis, dimension reduction methods, or network analysis.

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications.

Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications.

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111, STAT 135, or STAT 136. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment.

This course is an intermediate applied statistics course that builds on the statistical data analysis methods introduced in STAT 111, STAT 135, or STAT 136. Students will learn how to pose a statistical question, perform appropriate statistical analysis of the data, and properly interpret and communicate their results. Emphasis will be placed on the use of statistical software, data wrangling, model fitting, and assessment.

This is an interactive course designed to help students understand inequities in mental health issues via statistics. We will begin the course by examining mental health stigmas and practice self-care exercises to train our “happy muscles” together. We will discover the scientific evidence behind those self-care practices and explore existing racial disparities in mental health care systems, while learning about important statistical concepts and mastering our data analysis skills using R (a popular statistical software package).