Degree Requirements for Statistics M.S.

Degree Requirements

The total credits required for the degree are 33. If the student lacks background knowledge for one or more courses, prerequisite courses may need to be taken that do not count toward the degree.

21 credits of Core Courses, 12 credits of Approved Elective Courses

Statistics M.S. Core requirements

(21 Credits)

In addition,

students must take four STAT 600 level courses (12 credits)  selected from the following list of Approved Elective Courses*:

STAT 612 - Advanced Regression Techniques Credit(s): 3

STAT 616 - Advanced Design of Experiments Credit(s): 3

STAT 619--Time Series Analysis Credit(s): 3 

STAT 621 - Survival Analysis Credit(s): 3

STAT 622 – Statistical Network Analysis Credit(s): 3

STAT 674 - Applied Data Base Management Credit(s): 3

STAT 675 - Logistic Regression Credit(s): 3 

STAT 668 ----Research Project Credit(s): 3-6 

STAT 669---Masters Thesis Credit(s): 3-6 

STAT 664---Statistics Internship Credits: 1-6

*Note a student can take at most 6 credits from a combination of STAT 668, 669 or 664. 

Other Requirements

Students are expected to participate in StatLab and attend departmental seminars. They are also encouraged to attend the monthly meetings of the Delaware Chapter of the American Statistical Association (ASA); and other area professional meetings, such as the ASA Meetings. 

Admission requirements

Begin assembling your required application materials as electronic documents before completing the online graduate application. Do not mail any documents. Applicants must submit all materials directly to the University Office of Graduate and Professional Education using the online admission process before admission can be considered.

Review complete requirements 


Degree requirements

The total credits required for the degree are 33. If the student lacks background knowledge for one or more courses, prerequisite courses may need to be taken that do not count toward the degree.

Review complete degree requirements

Course highlights

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.

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