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.

  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 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 2.5 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

    The Master of Science in Statistics requires solid preparation in both calculus (generally three semesters) and linear algebra along with some preparation in statistics.  If you don’t have three semesters of  Calculus and one semester of linear algebra, you are not prepared for the program. However, conditional admission is possible if you can find a way to complete all these courses  with at least B grades before enrollment.

    At least one statistics course is desired before your enrollment. If you have no statistics courses before enrollment, you will be required to take a one-credit review course for statistics and mathematics in the first semester.

    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 download and complete the required supplementary document to complete your application. Without it, your application will be considered incomplete and will not be reviewed.

  5. 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.

  6. GRE

    In addition, applicants must take the GRE Aptitude Test with at least a score of 1050 in quantitative math and verbal (with an emphasis on the quantitative) for the traditional test. Students taking the new GRE exam should have a minimum of 300 combined on verbal and quantitative reasoning.  The program will also accept the GMAT if the applicant requests substituting for the GRE during the application process. Any subject GRE in a STEM field may be used as well.

    The GRE is waived if the student has another Master’s degree, or if the student has completed a graduate certificate that has 12 or more graduate credits in a STEM or quantitative field.  Applicants with more than 5 years of work experience that is directly related to degree program topics may request a waiver during the application process. The decision regarding the waiver rests with the MS in STAT program and cannot be appealed.  Applicants for the Bachelor/MS 4+1 option (See the  are not required to take the GRE, and should request a waiver during the application process.

  7. Personal Statement / Essay

    Applicants must submit a personal statement describing 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.

  8. Three Letters of Recommendation

    Three 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.

  9. International Student Requirements and Programs

    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 

Frequently ask questions

Degree Requirements for the Master’s Degree in Statistics

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 Course, 3 credits of Signature Courses, and 9 credits of Approved Optional Courses.

Statistics M.S. Core requirements

(21 Credits)

In addition

students must take one elected course from this Approved List of Signature Courses (3 credits)

Approved Optional Courses

(9 Credits)

Three STAT 600 level courses with the exception of:

Key approved optional courses include:

Students are expected to participate in StatLab; attend departmental seminars, attend the monthly meetings of the Delaware Chapter of the American Statistical Society; and area professional meetings, such as the ASA Meetings or the Merk-Temple Conference. T

he Department provides support for graduate students for these activities.


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  • This is an applied multivariate analysis and statistical machine learning course intended for graduate students in statistics or related fields. The aim of this course is to introduce 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 (if time permits), Partial least squares (if time permits), Sufficient dimension reduction (if time permits). 


  • This is an applied time series analysis course designed for graduate students in statistics and related fields. This 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). The focus will be more on methodology and data analysis than theory, involving an introduction to appropriate statistical packages in R and SAS software.


  • This course will present students with the basics of managing and summarizing data using The SAS System. An emphasis will be on 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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