Online Master of Science in Applied Statistics

To apply, please submit these materials:

• Completed graduate application form
• Unofficial transcripts 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 UD.

• Three letters of recommendation
• Up-to-date resume or C.V.

Essay 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 UD’s online MS 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.

• Supplemental document: Table that lists and describes all courses you have taken in calculus and linear algebra and relevant statistics courses. These courses include differential and integral calculus through multivariate calculus including multiple integrals, partial derivatives and change of variables for multiple integrals, as well as linear algebra including matrix algebra, determinants, systems of linear equations, inverses and eigenvalues and eigenvectors for symmetric matrices.
• $75 nonrefundable application fee: Credit card payment is accepted with the online application. Checks must be made payable to the University of Delaware. Applications received without the fee will not be processed.

No GRE Required

Online Master of Science in Applied Statistics Courses

The University of Delaware’s 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 requires 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 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’s interests, professional experience, and educational background.

Core Courses (15 Credits)

STAT 611: Regression Analysis - 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 - 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 – Probability - 3 Credits

Basic probability, De Morgan’s laws, conditional probabilities, Bayes’ 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 – Mathematical Statistics - 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 - 3 Credits

Fundamental topics in time series analysis — 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 ‘vocabulary’ 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

$1,069 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’s degree in statistics from UD’s Department of Applied Economics and Statistics (APEC) find employment within six months.

Jobs in the field 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) = $1,028
• Full Tuition = $30,840

Tuition includes a license for the Statistical Analysis System (SAS) JMP interface, which has an annual value of $1,500. Students in the online master’s degree program in applied statistics 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 Student Financial Services resource page.

The FAFSA school code for the University of Delaware is 001431.

Military and Veterans Services

UD’s Military and Veterans Services office provides support and encouragement to all students using VA education benefits. Student Veteran Services Coordinator Brooks Raup is a dedicated resource for students using VA education benefits at UD.

Thomas Ilvento, Ph.D.

Professor and Director of MS 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 University of Delaware, he was an Associate Professor at the University of Kentucky.

Career Landscape for Professionals with a Master’s 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, “Data and AI Leadership Executive Survey, 2022”). 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’s 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’s 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’s. For example, Salary.com shows that 60% of users who hold the title Statistician I have a master’s 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’ 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 applied statistics master’s degree can help you maximize your earning potential, regardless of the career path you choose. According to PayScale, professionals with a master’s degree in statistics earn 15% more, on average, than those with just a bachelor’s.

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

Master’s 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. The applied statistics and analytics fields have matured significantly over the past several years, and this has put more emphasis on data experts’ 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

“Statistician” 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

To request transfer credits, students must use a Request for Transfer of Graduate Credit form and submit it to the Department of Applied Economics and Statistics for evaluation. No transfers are accepted for core STAT courses 670, 671 and 613. A maximum of 9 credits required for the degree will be accepted provided that:

• Credits were earned with a grade of no less than “B”
• Credits are approved by the student’s adviser and the Chair of the student’s department
• Credits are in accord with the specific degree program of the student as specified by the unit’s Graduate Program Policy Statement
• Credits are not older than five years
• Credits were completed at an accredited college or university
• Credits were not courses used to complete a previous degree

 
The credits, but not the grades or quality points, are transferable to University of Delaware graduate records. Not eligible for transfer to UD are:

• Credits from graduate courses counted toward a degree received elsewhere
• Credits earned at another institution while the student was classified as a continuing education student
• Credits, generally, from institutions outside of the United States