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Online Master of Science in Applied Statistics

Become a qualified candidate for the rapidly growing number of positions for data professionals

Applied statistics is about real-world problem-solving. Its practitioners use statistical tools to conduct data analysis for companies, organizations and clients. Skilled statisticians are in high demand across an array of industries throughout the public and private sectors.

The completely online Master of Science in Applied Statistics (ASTAT) is built with career success in mind. Its students range from recent graduates to professionals who want to enhance their analytical abilities. The ultimate program goal is to transform students into data professionals who can satisfy the increasing demand for data analytics expertise and enjoy long-term success in their careers.

91原创鈥檚 online program includes numerous courses developed by the same faculty members who teach our on-campus students. Online courses are managed by statisticians who are full-time faculty members in the Department of Applied Economics and Statistics.

Program Highlights

The online master鈥檚 degree in applied statistics provides students with a range of knowledge, skills, and experiential training.

Including:

- A theoretical foundation in probability and mathematical statistics

- Applied applications in Regression, Experiment Design, Logistic Regression and Multivariate Methods

- Exposure to and the opportunity to acquire proficiency in essential technology including Statistical Analysis System (SAS), JMP and R; a JMP software license is included in tuition, a value of more than $1,500 annually

- The ability, acquired through case study, to analyze a wide variety of data and apply appropriate techniques, according to data type and research objective

- The opportunity to design and conduct an applied research project with advisor assistance and approval

Watch: Online M.S. in Applied Statistics Program Overview: https://capture.udel.edu/media/1_6yk5uqzs/

Program Benefits

More info about the Online Master of Science in Applied Statistics

Applicants from a range of professional and educational backgrounds are eligible for this program and need not have majored in a specific undergraduate field as a prerequisite for admission.

To be considered for the听online M.S. in Applied Statistics, candidates must meet these requirements:

  • Four-year undergraduate degree, or equivalent, from an accredited institution
  • Minimum grade point average (GPA) of 2.5 on a 4.0 system in mathematics, business, economics, or related discipline
  • Competence in basic statistics, linear algebra, and advanced calculus
    • 91原创 offers a bridge course to prepare students who require a more sufficient background in statistics. These students can take STAT 608 (Statistical Research Methods) online any semester prior to beginning the program. Credits for STAT 608 do not count toward the degree鈥檚 30-credit requirement.
    • Students who require math courses can be admitted into the online M.S. in Applied Statistics program on a conditional basis, but must complete required courses to begin the program.
    • A 1-credit math review course is available to students who meet the program鈥檚 admissions requirement for math. Students may find this refresher course beneficial and can take it for credit toward the degree as they begin the program.
  • Computer programming experience

Note: Candidates who meet stated minimum academic requirements are not guaranteed admission, and candidates who do not meet these requirements may be admitted if they offer other related strengths.

Info for International Students

International students must meet all of the admissions requirements and provide all of the application materials described on our听. Additionally, students whose first language is not English must supply a TOEFL score to demonstrate English competence.

TOEFL (Test of English as a Foreign Language) is offered by the Educational Testing Service (ETS) at test centers throughout the world. To be considered for admission, the University requires applicants to have an official paper-based TOEFL score of at least 550 or an Internet-based TOEFL (iBT) score of at least 85. The University expects a minimum score of 18 on the Internet-based Speaking Test. TOEFL scores more than two years old cannot be validated or considered official.

To pay the application fee, international students must use a check drawn on a U.S. bank account or an International Postal Money Order.

To apply, please submit these materials:

  • Completed graduate application form
  • Unofficial transcripts听from all higher educational institutions that you attended, including those from which you graduated, earned 12 or more credit hours, studied for at least a semester, or took classes related to this degree program. Applicants must provide a list of these institutions and upload the transcripts with the application.

    DO NOT mail official transcripts during the application stage. If you are accepted into the program, you will receive instructions on how and when you must submit official transcripts to 91原创.

  • Three letters of recommendation:听You provide names and email addresses for your recommenders, and recommendation forms are emailed directly to them from the online application. Instructions are included as to how to return the completed forms electronically.听You can submit your application before your letters of recommendation are complete.
  • Up-to-date resume or C.V.
  • Essay听that outlines your educational plans and career goals in relation to this degree program. It may include information about areas that are of special interest to you and why 91原创鈥檚 online M.S. in Applied Statistics is a good fit for you.
  • The essay is an integral part of your application that we closely review. We encourage applicants to take the time to develop and write a thoughtful and thorough statement.
  • $75 nonrefundable application fee: Credit card payment is accepted with the online application. Checks must be made payable to the 91原创. Applications received without the fee will not be processed.

No GRE Required

Online Master of Science in Applied Statistics Courses

The 91原创鈥檚 online Master of Science in Applied Statistics is designed for working professionals from a range of occupational and educational backgrounds. The flexible online format enables students to continue working while earning their degree and immediately apply what they learn in the classroom to their work environment.

The program听requires听students to complete a minimum of 30 credit hours of graduate-level coursework, divided equally between core and elective courses. Students obtain training in theoretical statistics through courses that cover the disciplines of probability and听mathematical statistics, and training in applied statistical techniques through courses that include regression, experiment design, multivariate analysis, logistic regression, and data management. Students acquire experiential training through case study and an optional research project.

The program is divided into semesters with each course spanning fifteen weeks. Most students will complete the program on a part-time basis, taking 3 to 10 credits per semester, depending on their work and other obligations. Students who are not working are permitted to take up to three 3-credit courses and one 1-credit course per semester.

Our 1-credit, pass/fail courses are intended to give students practice using essential data analysis and statistics software and tools. There are no exams for these courses; however, students will complete assignments at their own pace. With 15 lessons in each course, students are encouraged to complete approximately one lesson per week.

New students are assigned an advisor who will provide advice concerning course selection based on the student鈥檚 interests, professional experience, and educational background.

Core Courses (15 Credits)

STAT 611: Regression Analysis听- 3 Credits

Simple linear and nonlinear regression. Subset regression; principal component and ridge regression. Introduction to experimental design and design models.

STAT 613: Applied Multivariate Methods听- 3 Credits

Explores the main topics of multivariate statistics, including principal components, discrimination, classification procedures, and clustering techniques. Emphasis on how to identify the correct technique for a given problem, computer packages for its computation, and how to interpret the results.

STAT 615: Design and Analysis of Experiments I - 3 Credits

Fundamental principles of design, randomized designs, Latin squares, sources of error, components of error. Factorial designs, response surfaces, models for design.

STAT 670: Intro to Stat Analysis I 鈥 Probability听- 3 Credits

Basic probability, De Morgan鈥檚 laws, conditional probabilities, Bayes鈥 rule; discrete and continuous distributions; Bernoulli, Binomial, Poisson, Normal, Gamma and Cauchy distributions; transformations; joint and marginal distributions; moment generating functions; sums of independent normal and Gamma random variables; Chi-squared distributions; the Central Limit Theorem.

STAT 671: Intro to Stat Analysis II 鈥 Mathematical Statistics听- 3 Credits

Definition of a statistic; distribution of common statistics; sampling, maximum likelihood and moment estimators, unbiased estimators; hypothesis testing, Type I and Type II errors, one- and two-sample tests for the mean; categorical data, the Chi-Squared test; simple linear regression, ANOVA table.

Elective Courses (15 Credits)

STAT 619: Time Series Analysis听- 3 Credits

Fundamental topics in time series analysis 鈥 features the Box and Jenkins techniques of fitting time series data. Includes an introduction to appropriate statistical packages.

STAT 621: Survival Analysis - 3 Credits

Statistical techniques used in the analysis of censored data including the Kaplan-Meier estimator, the analysis of one, two and K sample problems, and regression analysis based on the Cox proportional hazards model.

STAT 656: Biostatistics - 3 Credits

Research designs, review of inference and regression, categorical data, logistic regression, rates and proportions, sample size determination. Additional topics such as nonparametric methods, survival analysis, longitudinal data analysis, and randomized clinical trial may be covered.

STAT 668: Research project - 3 Credits

Research as approved by the Faculty Supervisor. Restrictions: Approval by Faculty Supervisor.

STAT 672: Python and Database Management - 3 Credits

This course gives students an in-depth introduction to Python, to use it as a computing language (focusing on basic 鈥榲ocabulary鈥 and fundamental concepts), as well as an analytical tool for statistical analysis.

STAT 673: Econometrics and Statistics for Economics Research - 3 Credits

This course is designed to enhance the analytical and quantitative skills crucial for conducting applied research in economics and statistics. The course delves into multivariate regression analysis, beginning with Ordinary Least Squares (OLS). It addresses specification diagnostics, real-world challenges, and presents practical solutions to mitigate these issues. The curriculum expands to logistic regression models, systems of equations, introduction to time-series analysis and techniques for analyzing panel data.

STAT 674: Applied Data Base Management - 3 Credits

Provides an in-depth understanding of using computers to manage data, using programs such as SAS and Microsoft/Access.

STAT 675: Logistic Regression - 3 Credits

Practical and computational introduction to logistic regression and related topics. Applications include financial, marketing and biomedical research. The use of SAS and other statistical packages will be emphasized.

One-Credit Elective Courses* (Up to 3 Credits)

STAT 666: Section 16 Introduction to Python - 1 Credit

This course is intended to help students learn Python with introductory lectures and practical exercises using this open source programming language. In addition to the course material, we encourage students to use the software on their own to further build their Python skills.

STAT 666: Section [X] Introduction to R - 1 Credit

This course is designed to help students learn R, with a focus on practical exercises in data manipulation, qualitative data, quantitative data, statistics, coding standards and control structure.

* The availability of one-credit courses may change each semester. Speak with an advisor to learn more.

Tuition Information

$1,149听Per Credit Hour

The online Master of Science in Applied Statistics is an outstanding value. It provides students with a high-quality education from a respected university for an affordable price. Moreover, 90 percent of students who graduate with a master鈥檚 degree in statistics from 91原创鈥檚 Department of Applied Economics and Statistics (APEC) find employment within six months.

Jobs in the field听pay a median annual wage of $92,270, with the highest 10% of employees earning more than $150,840, according to the Bureau of Labor Statistics. APEC graduates working in some areas of the country, including Silicon Valley, report earning annual salaries between $180,000 and $250,000.

  • Tuition/Credit Hour (30 credit hours required) =听$1,153
  • Full Tuition = $33,480

Tuition includes a license for the Statistical Analysis System (SAS) JMP interface, which has an annual value of $1,500. Students in the听online master鈥檚 degree program in applied statistics听pay no other fees, but are responsible for the cost of their textbooks.

Financial Aid

Graduate students enrolled in a degree program may be eligible for federal loans. For information on filing deadlines, eligibility, or how to file a FAFSA, visit our听Student Financial Services resource page.

The FAFSA school code for the 91原创 is听001431.

Military and Veterans Services

91原创鈥檚听听provides support and encouragement to all students using VA education benefits. Student Veteran Services Coordinator听听is a dedicated resource for students using VA education benefits at 91原创.

Thomas Ilvento, Ph.D.

Professor and Director of M.S. in Applied Statistics
Department of Applied Economics and Statistics
Office: 302-831-6773

Bio: Thomas Ilvento specializes in collaborative needs assessment projects, in which he involves industry professionals in the design and implementation of surveys, focus groups, and other methods. His experience also includes public policy, business retention, community needs assessment, and collaborative problem solving.

Ilvento is trained in research methodology, applied statistics, demography, facilitation, mediation, and collaborative problem solving. He teaches graduate and undergraduate courses in applied statistics and has taught online class for more than 15 years. He also runs the StatLab, a statistical consulting opportunity for students, faculty, and outside companies and organizations.

Ilvento has co-authored numerous published studies and is the author of the text, 2013 Statistics, Plain and Simple. He earned his Ph.D. in rural sociology and his B.S. degree in Community Development, both from Pennsylvania State University. He holds an M.S. in Resource Economics/Community Development from the University of New Hampshire. Prior to joining the 91原创, he was an Associate Professor at the University of Kentucky.

Patrick DeFeo, Ph.D.

Adjunct Instructor
Department of Applied Economics and Statistics
Course Developer
Online M.S. in Applied Statistics

Bio:听Patrick DeFeo is an Adjunct Instructor for the Department of Applied Economics and Statistics and a Course Developer for the department鈥檚 Online M.S. in Applied Statistics degree program.

As the Principal Consultant Statistician for the DuPont Company, DeFeo has provided statistical leadership working with multidisciplinary teams in product development and process improvement. He has lead project teams, designed studies, and used advanced statistical analyses to provide practical guidance for complex business projects.

During his 29 years at DuPont, DeFeo has taught training in design of experiments, data analysis, and statistical process control. He has also taught Six Sigma training for Black Belts and Master Black Belts at the organization and is a DuPont certified Master Black Belt.

DeFeo holds a Ph.D. and M.S. in Statistics, both from Virginia Tech, and a B.S. in Mathematics from Montclair State College.

Steven P. Bailey, Ph.D., CSSBB, CMBB

Course Developer
Online M.S. in Applied Statistics

Bio:听Steven P. Bailey was with DuPont鈥檚 corporate Applied Statistics Group for over 36 years until his retirement as a principal consultant in 2016. During his last 16 years with DuPont, Bailey led DuPont鈥檚 corporate Six Sigma Master Black Belt Network. A past president and chairman of the board of the American Society for Quality (ASQ), he is certified as a Six Sigma Black Belt and Master Black Belt by both DuPont and ASQ.

Bailey, who served as an adjunct faculty member in 91原创鈥檚 Department of Applied Economics and Statistics, has been an instructor for the Predictive Analytics and Data Mining Certificate program since 2012. He also provides statistics and Six Sigma training and consulting services for a variety of businesses. He earned his B.S., M.S. and doctorate in statistics at the University of Wisconsin in 1974, 1975 and 1979, respectively.

Chunbo Fan, Ph.D.

Assistant Professor and Course Developer
Online M.S. in Applied Statistics

Bio:听Chunbo Fan鈥檚 focus is on promoting students鈥 academic success which extends to their professional achievements. She emphasizes on combining real-world practices to theoretical training in statistics, and encourage students to apply critical thinking in their daily situations.

Before joining 91原创, Fan was an Associate Director at Bayer Pharmaceutical, and a manager of Quantitative Commercial Insight at Astrazeneca Pharmaceutical. She has more than 11 years of experience in the industry, specializing in commercial analytics, marketing mix modeling, and commercial experiment and pilot programs.

Chunbo earned her Ph.D. and her M.S. from the 91原创, and she holds a B.S. from the Central University of Finance and Economics (China).

Hemei Liu, M.S.

Course Developer
Online M.S. in Applied Statistics

Bio:听Hemei Liu is a Supervisory Specialist in the Federal Reserve Bank of Philadelphia, and a Course Developer for the Online M.S. in Applied Statistics Degree Program. Prior to joining the Federal Reserve Bank, she was a quantitative operation manager at the Bank of American.

She has nineteen year of banking experience with proven modeling skills, in-depth knowledge on portfolio profit growth through credit line optimization, subject-expert on data quality monitor, model performance tracking, and experimental design.

Her modeling skills include binary logistic regression, multinomial logit analysis, logit analysis for longitudinal data, discrete time survival analysis, multivariate linear regression, time series model, valuation-framework profit model, optimization, machine learning, cluster analysis, multi-level experimental design, model performance evaluation, variable screening, model validation, data quality monitoring, and model examination.

Her honor and achievements include a US patent for the novel methodology of monitoring model score migration, generating incremental $60mm profit per year from her credit line increase model, generating $6.6mm expense saving per year from her collection model, and generating $22mm capital saving per year from her reward point breakage model.

Hemei holds a M.S. in Statistic from the 91原创, a M.A. in Energy and Environmental Policy from the 91原创, and a B.S. in Mathematical Statistics from the NanKai University.

Joseph Scocas, M.S.

Course Developer
Online M.S. in Applied Statistics

Bio:听Joseph Scocas is an Adjunct Instructor for the Department of Applied Economics and a Course Developer for the Online M.S. in Applied Statistics degree program.

He has worked at DuPont since 2007 and, in his current role as Statistician/Research Investigator with the Crop Protection division, his responsibilities include statistical simulations; analysis, using generalized linear mixed models; project management; support of discovery; and development and product support. He also has developed Design of Experiments (DoE) training, supervised master-level contractors and mentored graduate-level interns. Scocas previously served as a Statistician/Consulting Statistician at DuPont, working with multiple divisions as a member of DuET鈥檚 Applied Statistics Group.

Prior to joining DuPont, Scocas served as a Financial Officer, Mortgage Officer, and Management Analyst for the Delaware State Housing Authority, where he provided financial reports to investors, performed financial and regulatory compliance on loan applications, and provided internal and external statistical support.

He is Six Sigma Green Belt certified and Black Belt trained. Scocas is also an expert in the use of SAS programming including SQL coding, SAS macro language, statistical procedures, and advanced graph programming.

Scocas holds a M.S. in Statistics and a B.B.A. in Operations Management and Supervision, both from the 91原创.

Yihuan Xu, Ph.D.

Course Developer
Online M.S. in Applied Statistics

Bio:听Yihuan Xu is an associate director in the biometrics department at BeiGene Ltd where she works as a biostatistician to support clinical development of multiple oncology drugs. Prior to joining BeiGene, Xu worked as a biostatistician at Eli Lilly and Company in New Jersey for 10 years, supporting the Cyramza program on multiple phase II/III clinical trials. Before that, Xu was a biostatistician in Thomas Jefferson University Cancer center where she provided statistical support on cancer research across cancer biology to clinical studies.

Xu earned her medical degree from Peking University Health Science Center. She also received her Ph.D. in Statistics from Temple University and her M.S. degrees in Molecular Biology and Statistics from 91原创.

Her research interest is in survival analysis in oncology clinical trials, especially in biomarker enrichment study design and non-proportional hazard survival analysis.

Yan Yuan, Ph.D.

Assistant Professor
Online M.S. in Applied Statistics

Bio: Dr.听Yuan is an active teacher and researcher with extensive experience in economics, econometrics and general data analysis. She holds a Ph.D. in Agricultural Economics from Texas A&M University and an M.S. in Food and Resource Economics from the 91原创. Dr. Yuan has taught at several universities, including the 91原创, Texas Christian University and Southwestern University of Finance and Economics in China.

Dr. Yuan鈥檚 teaching experience includes a diverse range of courses in economics and research methods. For the M.S. in Applied Statistics, Dr. Yuan teaches STAT619 Time Series; STAT613 Applied Multivariate Analysis; STAT674 SAS; and the math review class. In addition, Dr. Yuan is developing a new class in applied econometrics, to be offered in Fall 2023.

Dr. Yuan鈥檚 research interests include financial literacy, credit accessibility and small business dynamics in China. She has published papers in China Economic Review, Pacific-Basin Finance Journal and Journal of Family and Economic Issues. She has received the 2015 Best Paper Award from China Economic Review and honorable mention for the Dr. Werner Jackst盲dt Best Paper Award for Chinese Economic and Business Studies in 2014. Dr. Yuan builds economic data into many of her courses, including exploring some of the issues and problems inherent to data that reflects prices, demand and supply.

Career Landscape for Professionals with a Master鈥檚 in Applied Statistics

Professionals with statistics and data analytics skills can work in any industry that excites them, because they can solve problems that not only require specialized skills and training, but also imagination and creativity. Data-centric professions are among the best careers available, as the global Big Data market is expected to reach $105.08 billion by 2027.

A survey of executives from a range of industries found that 92.1% are investing in data initiatives (NewVantage Partners, 鈥淒ata and AI Leadership Executive Survey, 2022鈥). However, only 26.5% of those surveyed say their organization has reached its data-driven potential. Across companies, the ability to act on data lags far behind the ability to collect and store it. That is why data expertise remains in high demand.

Analysts, data scientists and statisticians with master鈥檚 degrees can transform the way their organizations leverage data by making it actionable and applying it to a broad array of decisions. From helping governments reduce hunger to helping insurance companies establish viable rates, these professionals play a vital role across numerous industries and types of organizations.

Analyst, Data Scientist and Statistician Degree Requirements

While not all statistics careers require master鈥檚 degrees, advanced education can help you be more competitive in the field. Even for some of the early-stage career paths available, most statistics professionals have at least a master鈥檚. For example, Salary.com shows that听听have a master鈥檚 degree.

Advanced education becomes more essential for getting into analytics and statistics careers that revolve around creating complex models and algorithms. According to Burtch Works鈥 2024 Predictive Analytics, Marketing Research & Data Science Salary report, 80% of all data science and AI professionals surveyed held an advanced degree.

Additionally, earning an听听can help you maximize your earning potential, regardless of the career path you choose. According to PayScale, professionals with a master鈥檚 degree in statistics听, on average, than听.

Statistician degree programs at the master鈥檚 level offer extensive, hands-on experience with advanced statistical methods for modeling and making more accurate predictions. View the 91原创鈥檚听online M.S. in Applied Statistics curriculum听page for more information about what to expect when pursuing a statistics master鈥檚 degree.

Master鈥檚 in Applied Statistics Salary

According to Lightcast, a labor market analytics company, the average salary for statisticians and mathematicians is highly competitive at $99,840. The Bureau of Labor Statistics discovered very similar data, listing an average salary of $98,960 for statisticians and mathematicians.

What Data Experts Do

Data experts try to answer questions by collecting, analyzing, and interpreting data to gain actionable insights. Their work typically involves using computer software to format, clean and manage the data their organizations collect for advanced analysis. From there, they may create models, make predictions, or look for trends that indicate opportunities or problems.听The applied statistics and analytics fields听have matured significantly over the past several years, and this has put more emphasis on data experts鈥 ability to communicate insights to leadership teams. They play an increasingly pivotal role in creating new business strategies as well as identifying ways to make operations more efficient.

Where Data Experts Work

The top 10 industries hiring statisticians are:

1. Scientific research and development services

2. Pharmaceutical and medicine manufacturing

3. Colleges, universities and professional schools

4. Insurance carriers

5. Management, scientific and technical consulting services

6. Hospitals and medical centers

7. Software publishers

8. Banks and credit unions

9. Government agencies

10. Accounting, tax preparation, bookkeeping and payroll services

Source: Burning Glass Technologies, 2022

鈥淪tatistician鈥 is likely one of the first job titles people associate with a statistics degree program. However, graduates hold a range of positions with titles such as:

  • Statistical Programmer
  • Biostatistician
  • Data Analyst
  • Data Scientist
  • Systems Analyst
  • Credit Analyst
  • Compliance Manager
  • Business Intelligence Manager
  • Quality Engineer
  • Marketing Analyst
  • Supply Chain Manager
  • Financial Planner
  • Insurance Researcher
  • Communications Manager
  • Data Modeler
  • Environmental Scientist
  • Network Administrator
  • Pharmaceutical Engineer
  • Sales Engineer

You can transfer up to 9 credits per 91原创 policy.

Note: No transfers are accepted for core STAT courses 670, 671 and 613.

All requests for transfer credit should be directed to the student鈥檚 major department using a Request for Transfer of Graduate Credit form. Transfer credits will be accepted provided that such credits:

  • Were earned with a grade of no less than B,
  • Are approved by the student鈥檚 adviser and the chair of the student鈥檚 major department,
  • Are in accord with the specific degree program of the student as specified by the unit鈥檚 Graduate Program Policy Statement,
  • Are not older than five years,
  • Are graduate level courses, and
  • Were completed at an accredited college or university.

Graduate courses counted toward a degree received elsewhere may not be transferred into a degree at the 91原创. Credits from institutions outside of the United States are generally not transferable to the 91原创.

Learn more about transfer credit by viewing the Graduate College's Registration and Enrollment Policies.

The online Master of Science in Applied Statistics program provides students with three opportunities to begin the program each year: Fall, Spring and Summer.

Please review the chart below for the application deadlines.

SessionApplication DeadlineSession Start Date
Summer IMay 29, 2026June 8, 2026
Fall IAugust 1, 2026August 25, 2026
Spring IJanuary 1, 2027February 2, 2027

"The online program was much more suited to my situation. I could choose any time to do my assignments while having time to work or take care of my daughter, whatever I needed."

Yaxi Huang

Senior Statistical Analyst

M.S. in Applied Statistics Graduate, 91原创 Class of 2024

Yaxi Huang, Applied Statistics Student

Frequently Asked Questions

Yes. There is no campus component to this program.

The program requires a minimum of 30 credit hours of coursework: 15 credits of core courses and 15 credits of electives.

Yes, graduate departments generally allow you to transfer up to 9 credits per 91原创 policy. View Transfer Credit for more information.

Competence is expected in basic statistics, linear algebra, and advanced calculus, and applicants must submit detailed information about their math background as part of the听application process. 91原创 offers a bridge course to prepare students who require a more sufficient background in statistics. These students can take STAT 608 (Statistical Research Methods) online any semester prior to beginning the program. Credits for STAT 608 do not count toward the degree鈥檚 30-credit requirement.

Students who require math courses can be admitted into the online M.S. in Applied Statistics program on a conditional basis, but must complete required courses to begin the program.

A 1-credit math review course is available to students who meet the program鈥檚 admissions requirement for math. Students may find this refresher course beneficial and can take it for credit toward the degree as they begin the program.

ASTAT is an all-encompassing applied statistics program, offered by the Department of Applied Economics and Statistics, which is housed in the College of Agriculture and Natural Resources. ASTAT is not an agricultural statistics program.

Agriculture, however, was one of the first industries to adopt applied statistics and there is a strong historical link between the agricultural industry and the discipline. Statistics programs at universities are housed in a range of departments and colleges including mathematics, business and agriculture.

Most students will complete the program in 2 1/2 years.

Note:听Students are required to complete the degree within six years of starting the program.

Canvas, which is currently used by over 2,000 schools throughout the United States.

Students typically spend three hours of study/reading/homework for every hour of lecture.

No. The online M.S. in Applied Statistics program is asynchronous and there are no set log in times. Students are provided with flexibility to complete their coursework largely on their schedule.

Assignment deadlines vary by course and instructor. Some instructors may require students to meet specific deadlines throughout the course. Other instructors may only require students to complete all assignments before the section exam.

Yes. Students may choose to complete this program part time. It is geared toward working professionals.

Yes. The program accepts international students, but does not sponsor visas. View additional information in our Admission Requirements section further up this page.

You may start this program in the fall, spring, or summer semesters. Applicants are considered for admission on a rolling basis, which makes now the perfect time to apply.

Please review the chart above for the application deadlines.

Ready to Learn More?

Experience what it鈥檚 like to earn your Master of Science in Applied Statistics in as little as 18-24 Months, all from the comfort of your own home.