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.

“Data analysis, ingenuity, experience with programming languages and developing the mindset of an analyst is a skillset that this program helped initiate and something that I have strived to continuously improve.”
Mark Buchholz, MS in Applied Statistics Graduate

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 666: Independent Studies (up to three 1-credit courses in different topics for a total of up to 3 credits)
  Independent work as specified by the Faculty Advisor.
• STAT 666-010: Intermediate Python (3 credits)
  This course is intended for newcomers to Python or those who have had some exposure to Python programming but desire more in-depth exposition and vocabulary for describing and reasoning about programs. In addition to the basics of Python 3 (including strings, lists and data structures, as well as conditional execution and iteration control structures) the course will extend into intermediate skills of drawing and data visualization. Its teaching also emphasizes “the way of the programmer” — i.e., training the student to think like a computer scientist and treat programming as a communication process between computer and programmer.
• STAT 666-19: Applied Econometrics (3 credits)
  This course is designed to equip you with the analytical and quantitative abilities essential for conducting applied economic and statistical research. It will also deepen your critical comprehension of methodologies and interpretation of empirical research featured in scholarly journals. The course is rooted in the study of multivariate regression analysis. We start with an exploration of Ordinary Least Squares (OLS) models, then expand our understanding to encompass logistic models, simultaneous equations, time series data and panel data analysis. Throughout the course, we will address frequent issues associated with these methods, strategies for identifying and rectifying these issues, and the correct selection of econometric tools tailored to specific problems.
• STAT 668: Research project (3 credits)
  Research as approved by the Faculty Supervisor. Restrictions: Approval by Faculty Supervisor.
• 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.