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Master of Science in Applied Statistics

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Program Overview

The OU online Master of Science in Applied Statistics (MSAS) is a 100% online, 30-credit program that can be completed in just 18 months. Learning from distinguished OU faculty, the MSAS prepares graduates to meet the challenges of a data-driven economy and impact crucial organizational decisions and solutions. The curriculum trains students in high-demand skills applicable to a wide range of sectors, industries, and fields, covering topics such as scientific computing, statistical paradigms, data management and analytics, and essential tools, techniques, and software.

Gain High-Demand Skills

Learn statistical skills highly prized by employers in an increasingly data-driven economy.

Master Industry Tools

Apply learned skills in cutting-edge tools and techniques, including R, SAS, SQL, Linux Bash Shell, Stan, and JAGS.

Real World Application

Focus on the application of statistical and data analytics methods to solve real business challenges for your organization.

Expand your Connections

Forge new career connections as part of the OU community, joining the worldwide OU alumni network.

Program Requirements

Program Cost

Tuition for the online Master of Science in Applied Statistics program is $24,540* for the entire program ($818/credit hour). Fees associated with the program are included in this cost. Books and materials are additional.

Once accepted into the MSAS program, students are required to submit a $350 non-refundable deposit within two weeks. Deposits are applied toward tuition expenses and secure a student’s place in the upcoming class.

*Tuition and fees are subject to change at the discretion of the Oklahoma State Regents for Higher Education.

Admissions Requirements

To apply to the online Master of Science in Applied Statistics program, students must hold a bachelor’s degree from a regionally accredited college or university (or the international equivalent).

Students who wish to apply must:

Complete the online application.
Submit an official transcript from your undergraduate institution and any graduate institution you have attended.
Write and submit a personal statement on your career goals and reasons for applying to the program.
Submit resume: Include professionally formatted documentation of your past education and work experience.
GRE scores are optional and not required for admission, but they may be required by some potential faculty sponsors to be considered for a Qualifying Graduate Assistantship. International students are required to take the TOEFL exam.

Course Descriptions

The 36-credit hour program consists of six online modules and three in-person residencies. The residencies consist of two weeks spent on the OU campus and an international perspective on the energy industry in Europe.

CORE COURSES (6 CREDITS)

CAS 5773 – Ethics in Statistical Practice
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.

CAS 5873 – Statistical Consulting and Communication
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.

ELECTIVE COURSES (24 CREDITS)

MATH 5743 – Introduction to Mathematical Statistics
This course will teach mathematical development of basic concepts in statistics: estimation, hypothesis testing, sampling from normal and other populations; regression, goodness of fit.

DSP 5673 – Introduction to Scientific Computing
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.

MATH 4753 – Applied Statistical Methods
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.

LIS 5683 – Database Design for Informational Organization
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.

MATH 5773 – Applied Regression Analysis
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.

LIS 5623 – Advanced Data Analytics
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.

DSA 5403 – Bayesian Statistics
Course topics are models, probability, Bayes’ Rule and R; inference to a binomial probability; and the generalized linear model.

What Kind of Career
Can I Pursue?

Data scientist/analyst
Economist
Financial planner/analyst
Insurance researcher
Market researcher
Public health researcher
Statistical consultant
Biostatistician
Consultant

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