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MASTER OF SCIENCE IN APPLIED STATISTICS
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Program Overview
Why earn your Online Master of Science in Applied Statistics from OU?
Learn statistical skills highly valued by employers in an increasingly data-driven economy.
Forge new career connections as part of the OU community, joining the worldwide OU alumni network of over 100,000 professionals.
Admissions Requirements
Careers
Course List
Core Courses
Credits – 3
This course teaches students how to ethically conduct statistical analysis in a world where data collection and privacy concerns are becoming ubiquitous. This course provides a broad overview of ethical considerations for conducting statistical analysis. Students will gain perspective through analyzing case studies tied to each of the main topics.
Credits – 3
This course will teach students how to collaborate and communicate effectively with non-statisticians to answer their subject matter questions and will provide a culminating research experience for master’s students in the online Applied Statistics program. It will discuss how to effectively communicate with non-statisticians using many real-world examples. Students will gain hands-on experience by completing a semester-long project with a written report of the results. The course will emphasize the synthesis of skills taught in all prior courses in the program: technical skills, creative thinking, effective writing, and scientific communication.
Total Credit Hours: 6
Elective Courses
Credits – 3
This course will cover the survey of advanced applied statistical methods other than applied regression, including exploratory data analysis, analysis of multivariate data (principal components: analysis, multiple analysis of variance, cluster analysis, etc.), and introduction to non-parametric methods.
Credits – 3
This course will teach mathematical development of basic concepts in statistics: estimation, hypothesis testing, sampling from normal and other populations; regression, goodness of fit.
Credits – 3
This course will introduce students to fundamental computational tools used in statistics. Topics include how to write computer programs and scientific reports for collaboration, automation, and reproducibility. The course will introduce several tools including SAS programming, relational databases R, SQL, the Linux command line, and scientific communication using LaTeX, Markdown, and similar tools.
Credits – 3
Topics covered in this course include estimation, hypothesis testing, analysis of variance, regression and correlation, goodness-of-fit, and other topics as time permits. Emphasis on applications of statistical methods.
Credits – 3
This course has two major components: (1) conceptual foundations of database design and theory and (2) practical applications of design and theory to real-world database designs. For the conceptual and theoretical design component, this class covers data definition and type, entity relationship diagram (ERD) and data normalization. The practical application uses emerging database tools to cover industry critical functions.
Credits – 3
This course covers the general regression problem of fitting an equation involving a single dependent variable and several independent variables, estimation and tests of regression parameters, residual analysis, selecting the “best” regression equation.
Credits – 3
Application of data analytic theories and models to solve real world problems using various unsupervised and supervised models will be covered in this course. Topics include cluster analysis, association rule mining, random forest classifier, neural networks, and naïve Bayesian classifiers.
Credits – 3
Course topics are models, probability, Bayes’ Rule and R; inference to a binomial probability; and the generalized linear model.
Total Credit Hours: 24
Total Credits for the Program: 30
1 “Occupational Outlook Handbook: Mathematics and Statisticians ,” U.S. Bureau of Labor Statistics, last modified September 6, 2023, https://www.bls.gov/ooh/math/mathematicians-and-statisticians.html
