20 courses with the subject STAT, each shown exactly as we captured it from the college's catalog, with every element we hold. Where the wording looks broken, that is our reading of the catalog, not the college's text.
STAT 1132Education Analytics3
Education Analytics is an introductory course in using quantitative methods for inquiry in education. The course covers statistical and graphical tools for summarizing educational data, basic statistical inference, and foundational concepts in the statistical modeling of educational outcomes. Students develop competence in reading the statistical aspects of educational research. Students are introduced to educational data mining, learning analytics, and other uses of data to predict educational outcomes. Students work with the National Childhood Longi Attributes: DA YLIB
Healthcare Analytics is an introductory course in using quantitative methods for inquiry in healthcare. The course will cover statistical and graphical tools for summarizing healthcare data, basic statistical inference, and foundational concepts in the statistical modeling of healthcare outcomes. Students will develop competence in reading and understanding the statistical aspects of healthcare research. Students will work with the Community Health Status Indicators and other large healthcare datasets and develop data analytic and computing skills. Th Typically offered:
This course provides students the opportunity to think clearly and conceptually about quantitative information. Students will become comfortable and familiar with working with and interpreting data. Utilizing a variety of approaches, students will analyze real-world scenarios by interpreting and using data to draw basic conclusions and describe limitations. With an emphasis on the ethical use of data, students will practice communicating their findings in writing. Attributes: DA YLIB 2025-2026 Catalog » Academic Programs » Undergraduate » School of Arts and Sciences » Data Analytics
Total: 39 credits B.S. in Computer Science For students majoring in computer science, required courses from all disciplines (CSCI, MATH, COMM, and ENGL) that may be applied to the major are included in the determination of the grade point average in the major.
This course covers basic and intermediate principles of applied linear regression. The course topics include least-squares estimation; assumptions underlying regression analysis and tests of regression assumptions; residuals analysis; regression with nominal/dummy-coded predictors; stepwise and hierarchical entry strategies; prediction, and testing interaction effects in regression analysis. Emphasis is placed on the analysis of behavioral data using regression methods, the interpretation of regression statistics, and the written communication of resu Attributes: YLIB
Principles of designing and analyzing experiments with applications to behavioral and health science disciplines. Topics covered include randomized and blocked experimental designs, control, and analysis of variance in between-subjects, repeated-measures, and simple factorial designs. Data analytic, statistical computing, and statistical communication skills are developed in the course. Attributes: YLIB
This course covers nonparametric statistical methods, with emphasis on applications, data analysis, and statistical computing. Topics include binomial and sign tests, rank tests for 1-sample and 2-sample designs, contingency table analysis, Kolmogorov-Smirnov tests, nonparametric correlation coefficients, nonparametric regression methods, and computationally-intensive approaches to nonparametric analysis. Cross-listed with ECON 222. Attributes: YLIB
Spatial awareness is a key to understanding our world. This class looks at the use of Geographic Information Systems (GIS) to analyze and answer real world problems. GIS is a multidisciplinary tool that can be utilized by pretty much any researcher because it can showcase a lot of information in addition to the geographic location such as demographics of an area for marketing, looking at population needs. This class is an introduction to the possibilities of GIS, and the goal is to come away with new tools and thought process to help look at data in a Attributes: YLIB
This is a course in supervised statistical learning and predictive modeling, emphasizing the application of statistical learning methods for understanding complex datasets and for addressing regression and classification problems. Methods covered include k-nearest neighbors models, nonlinear regression and spline models, generalized additive models, penalized regression models, tree models including random forests and boosting, and support vector models. Resampling methods for model validation, model bias/variance issues, and using models for predicti Formerly titled Exploratory Data Analysis Attributes: YLIB
This course is an introduction to the statistical analysis of social networks; the structure of network connections introduces a number of unique statistics to networks. Beginning with an introduction to graph theory, it will look at the representation of networks, appropriate descriptive statistics for networks, issues related to sampling networks, and how networks can be compared and modeled. Specific topics to be examined include measures of importance in networks, community structure, and inferential and predictive modeling of networks Attributes: YLIB
Meta-analysis refers to statistical methods for analyzing effect sizes across studies, and is widely-used in the social and health sciences for synthesizing research and establishing evidence-based practice and policy. Topics covered in this course include: effect size estimation, coding, weighting schemes, fixed and random effects models, moderation of meta-effects, meta-regression, and methods to evaluate heterogeneity and publication bias. Computing will use meta-analysis packages in R. Students who have earned credit for STAT 270 may not earn credit for STAT 370. Attributes: YLIB
Mathematics/Actuarial Mathematics Option - 18-19 credits Choose ONE option: Actuarial Mathematics Option (19 credits): MATH 302, 410, 460 (1), 461; STAT 210; ECON 106; plus either MATH 480 Capstone or STAT 490 Internship Mathematics Option (18 credits): FIVE additional 3-credit MATH courses at or beyond the 300 level, plus MATH 480 Capstone Total: 60-61 credits
This course introduces students to basic concepts in machine learning. The primary goal of Machine Learning (ML) is to use self-improving algorithms to help identify patterns within the data. Students will work with real datasets to learn about topics such as classification, regression, decision trees, random forests, neural networks, and support vector machines. In addition to learning about these different approaches, students will choose how to best match them with various situations and problems. Attributes: YLIB
Approaches to statistical inference are covered, framed by frequentist and Bayesian perspectives. Topics include: null hypothesis significance testing; robustness and exact tests, point and interval estimation; confidence interval construction methods; goodness of fit tests; maximum likelihood estimation, Bayesian estimation, and inference using resampling methods. Attributes: YLIB
Students propose, conduct, and present a substantive research project that demonstrates a synthesis of learning accumulated in the statistics major. The research topic is approved by the Program Director and conducted under the supervision of the student’s capstone advisor. The project culminates in a written report and presentation. Permission of the Program Director is required to register. Attributes: YLIB
Other STAT courses at the 300 or 400 level (3) Total: 18 Note: Only one course applied to a student's major may also be used to satisfy a requirement in the minor. 2025-2026 Catalog » Academic Programs » Undergraduate » School of Arts and Sciences » Data Analytics
In-depth study of a statistical topic under the direction of a Statistics faculty member. A written report summarizing the course project, research, or activity is submitted to the supervising faculty member. Completion of the Independent Study/Tutorial Authorization form is required. Typically offered:
