Statistics M.S.
Program overview
The University of Delaware’s MS in Statistics program provides a comprehensive education in statistics, focusing on key areas such as probability theory, mathematical statistics, regression analysis, and statistical computing, with hands-on experience in SAS and R programming. The program also offers internship opportunities, equipping students for successful careers in technology, finance, agriculture, healthcare, pharmaceuticals, and government sectors.
Application & Admissions
Application process
Applicants must submit all materials via the online application portal at UD graduate college. Required materials include transcripts, two letters of recommendations, essay, resume, and a supplemental document.
A language proficiency test score, such as TOEFL or IELTS, is required for international students whose native language is not English and who have not received a degree from a U.S. college or university. Please refer to the university guidelines for the minimum required test scores.
The GRE score is recommended but not required.
Admissions Requirements
On a 4.0 system, applicants should have a G.P.A. of at least 3.0. Applicants are expected to hold a bachelor's degree or higher by the time of admission. There is no requirement to have majored in a specific undergraduate field as a prerequisite for admission.
At a minimum, applicants should have completed at least two semesters of calculus, one semester of linear algebra, and at least one statistics course prior to enrollment. While not required, some programming experience is considered a plus.
Ready to Apply?
*Disclaimer: The customized GPT is an experimental tool designed to provide real-time answers based on the official curriculum and commonly asked questions. GPT-generated answers may not always be accurate. Please verify all information through the official University of Delaware website.
Application deadlines
Admission decisions are made on a rolling basis every month.
For fall admission, the priority application deadline is February 1st, and the final application deadline is August 1st. For spring admission, the final deadline is January 1st.
We strongly recommend that applicants apply as early as possible, especially international students who will require a visa for study.
Fall semester
- Feb 1 (Priority Application)
- Aug. 1 (Final Application Deadline)
Spring semester
- Jan. 1 (Final Deadline)
Course Highlights
The MS in Statistics requires a total of 33 credits to graduate, including 21 credits from the MS core courses and 12 credits from approved electives. A minimum GPA of 3.0 is required for graduation.
Graduate students learn how to analyze, interpret and assess the validity of logistic regression and generalized linear models, and various applied contexts such as medicine, marketing, risk management, and online learning. Professors introduce modern topics such as high-dimensional logistic regression with Lasso and logistic regression in nonparametric or semi-parametric settings (generalized additive model). In addition to binary or multi-categorical data, Poisson regression and Negative Binomial regression for count data analysis will be studied. The course will primarily use procedures in the SAS system to do data analysis. The course will also introduce R software packages for high-dimensional logistic regression and generalized additive models, two modern machine learning techniques.
This applied multivariate analysis and statistical machine learning course introduces a variety of statistical methods for multivariate analysis and machine learning, involving statistical computing mostly with R and Python. The course topics include:
Random vectors and random matrices,
Multivariate normal distribution,
MANOVA (Multivariate analysis of variance),
Principal component analysis (PCA),
Canonical correlation analysis (CCA),
Linear and Quadratic discriminant analysis (LDA and QDA),
Resampling methods including Cross-Validation (CV) and Bootstrap,
Regression and classification trees (CART),
Random forests,
Support Vector Machines (SVM),
Boosting methods,
Clustering analysis,
Online recommendation system,
Deep neural network,
Partial least squares, and
Sufficient dimension reduction.
This applied time series analysis course covers important topics in time series analysis, including the Box and Jenkins techniques of fitting time series data, ARMA models, ARIMA models, seasonal models, ARCH models, GARCH models, transfer function models, vector autoregression models, forecasting, frequency domain methods, recurrent neural networks, long short-term memory networks, gaussian processes and (hidden) markov models (time permitting). Professors focus more on methodology and data analysis than theory, involving an introduction to appropriate statistical packages in R and SAS software.
This course presents students with the basics of managing and summarizing data using the SAS System. Professors emphasize preparing data for analysis and creating attractive, readable reports for data summaries. Additionally, students will build the foundations and strategies to support future development of their SAS programming skills.
Internship opportunities
While the department does not provide funding for first-year M.S. students, they do have internship opportunities available in the second year of study. The department coordinates interviews between first-year students and participating companies located in Delaware. Successful candidates secure a paid part-time internship for their second year. Please note that internship opportunities for M.S. students are competitive, and the number of available positions is determined by participating companies and may fluctuate each year.