Admission Requirements for Statistics M.S.

Admission Requirements


  1. Online Application

    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.

    Admission applications are available here.

    Please submit a completed application by the following deadlines:


    February-02 To be considered for departmental funding

    August-22: Final deadline to apply

    August-22 International application deadline*

    *International students who need visa support should be aware that it can take weeks or even months to get visa approved and should apply as early as possible.


    January-25 Final deadline to apply


  2. Application Fee

    A $75 application fee is due when you submit your online application.

  3. Bachelor’s Degree

    A four-year U.S. Bachelor’s degree in any academic field from an accredited college or university is required. If you have a three-year non-U.S. degree, you may request a review for determination as to equivalency by emailing the graduate program director after you have uploaded your transcripts, recommendations, personal statement, as well as TOEFL/IELTS scores to the application.

    On a 4.0 system, applicants should have a G.P.A. of at least 2.7 and an average of at least 3.0 in mathematics and related areas. Applicants who have completed an advanced degree must have done so with a G.P.A. of at least 3.0.

  4. Prerequisite requirements

    Candidates for admission to the statistics program need not have majored in any specific undergraduate field as a prerequisite for admission. However, the Master of Science in Statistics requires solid preparation in both calculus (generally three semesters) and linear algebra along with some preparation in statistics. Applicants should have taken at least two semesters of Calculus (three semesters of Calculus are preferred), one semester of linear algebra and at least one statistics course before the enrollment. Conditional admission is possible if the applicant can make up all the missing courses with at least B grades before enrollment. Although it is not required, some programming experience will be a plus.

    Please put all the relevant math, statistics and programming courses that you have taken in the table of the supplementary document here. Please complete the required supplementary document and submit it together with other application materials. This document is very important for the graduate admission committee to decide whether you have appropriate background.


  1. Transcripts

    Unofficial transcripts for all institutions attended are required with your application. You should visit the Registrar’s page of your home institution (post-high school institutions only) to print an unofficial copy to create your scan. If your home institution does not provide this service, ask for a student copy to create your scan. Do not mail official transcripts during the applicant stage. Please visit for more detailed information.

  2. GRE

    GRE is not required for general admission, and applicants will be automatically considered for departmental funding. Priority for funding will be given to students demonstrating high proficiency in mathematics, statistics, or other quantitative skills. Submitted GRE scores and/or previous graduate education would aid in demonstrating this proficiency.

  3.  Personal Statement / Essay

    Applicants must submit a personal statement describing their motivation and how their academic, professional and personal background has prepared them to be successful in the Master of Science in Statistics program and explaining how the completion of the degree will contribute to their professional goals.

  4. Letters of Recommendation

    At least two letters of recommendation from individuals familiar with the candidate’s academic and/or professional background and capabilities are required. Please 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.International Student Requirements and Programs

  5. International applicants must submit one of the following:

    • proof of having earned a degree in either the United States or a country where the primary language is English;

    • TOEFL score of 85 or higher and a minimum speaking score of 18;

    Applicants must score 100 or higher on the TOEFL or the equivalent on the IELTS to be considered as a Teaching Assistant.

    The Graduate & Professional Education office provides more detailed TOEFL information.

    Applications are evaluated based on a combination of record of academic achievement, recommendations, and the applicant’s statement of professional goals and values.

    Admission to the Statistics Program is based on selections made by the department graduate committee in compliance with University policies and procedures. Admission is selective and competitive based on the number of well-qualified applicants and the limits of available faculty and facilities. Those who meet stated minimum academic requirements are not guaranteed admission, nor are those who fail to meet those requirements necessarily precluded from admission if they offer other appropriate strengths. Accepted students will be assigned to faculty advisors upon matriculation.

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

Frequently asked questions

Answer: Yes, there is limited funding available for Fall enrollment. In general, a full Teaching Assistantship includes a full  tuition waiver and stipend. In February,  the graduate admissions committee will discuss the qualification of all applicants that apply before the funding deadline (February 2) and make recommendations about funding and admission. Any applicants who want to be considered for the department funding should  submit their  application before February 2. 

Although the first year TA positions are  limited,  the chance for a student to get financial support in his/her second year is very high. In the last six years, 80% of our students received financial support  in their second year.  

Answer: Many of our students, usually beginning in their second year, are able to intern as statisticians at major corporations or other local businesses that have headquarters or major operations near the University of Delaware. Participating companies often include: Chase, Barclays, SallieMae and others. These companies start the interview/selection process in January or early February, selecting students to intern the following year.

All first year students are eligible to apply and submit their resumes after finishing one semester of courses. Students will be picked by the companies based on the students’ resume and  interview performance. Students often get multiple interview opportunities. The selected students will usually sign a year long internship contract beginning in the summer after the first year. Sponsored students receive competitive stipends and important real world work experience.

Each year, the participating companies vary as does the number of interns hired. Since 2014, the average number of internship positions offered is 18, and for the year 2021/2022, three-fourths of eligible students received an internship offer.  

Answer: It is possible to start the MS Statistics program in the Spring Semester, provided the student is properly prepared. Unfortunately, departmental funding is often limited due to the timing of our budget process.

Our graduate course offerings and curriculum are designed mainly for students starting the program in the Fall. Therefore, Spring enrollment is usually appropriate only for students who have already taken some graduate-level statistics coursework. See details in the sample curriculum for the MS Statistics program below. 

Finally, only students who have successfully completed at least one semester in our program are qualified for the internship program.  Therefore students who enroll in the spring often have to wait for their third semester to have interview opportunities, while the students enrolled in the fall will have the opportunity at the end of their first semester.  In addition, some companies only consider students who can intern for one year. Therefore, the spring enrolled students may not be able to intern in those companies even if they are well qualified. 

Course highlights

(Sample curriculum file)

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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