Fall and spring semesters. The Department.
How to handle overenrollment: null
Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: quantitative work, projects, use of computational software
The application of statistical methods for categorical data is ubiquitous in the modern world, especially in social sciences, biomedical research, and economics and business. However, many traditional statistical tools (like linear regression models) are not designed to handle observations classified into categories and thus inappropriate for analyzing such data.
(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.
Spring semester. 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.
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.
The COVID-19 pandemic has illuminated continuing health inequity. This course examines public health research through the critical lens of research methods and statistical analysis. A variety of health-related issues in which race, ethnicity, biological sex, and/or sexual orientation are associated with negative health outcomes will be examined.
