Program Overview

Statistics and Data Science (MS) Data Science Track

The Master of Science in Statistics and Data Science, Data Science track, focuses on data analytics and its application to business, social, and health problems. This program is particularly suited for individuals who have completed an undergraduate program in mathematics, statistics, economics, business, or other related fields and wish to pursue a career in data science.

Program Overview

Data scientists analyze massive data sets to uncover trends and associations and make theoretically sound decisions on various subjects. The demand for data scientists far exceeds the existing number of qualified persons in the area. Currently, the workforce in the data science industry consists mainly of individuals trained with post-college education. Very few university degree programs exist for training students for such a large and growing industry in the United States.

Program Structure

The Data Science track in the Statistics and Data Science MS program is composed of 24 credit hours of required courses and 6 credit hours of restricted electives. Students must also pass an oral defense of thesis or complete a research project and an additional elective.

Total Credit Hours Required

• 36 Credit Hours Minimum beyond the Bachelor's Degree

Track Prerequisites

Students must have the following background and courses completed before applying to the Statistics & Data Science, Data Science track MS program:

• MAC 2311C: Calculus with Analytic Geometry I
• MAC 2312: Calculus with Analytic Geometry II
• MAC 2313: Calculus with Analytic Geometry III
• MAS 3105: Matrix and Linear Algebra or MAS 3106: Linear Algebra

Degree Requirements

Required Courses

• Complete all of the following:
  • STA5104 - Advanced Computer Processing of Statistical Data (3)
  • STA6714 - Data Preparation (3)
  • STA6238 - Logistic Regression (3)
  • STA6326 - Theoretical Statistics I (3)
  • STA6327 - Theoretical Statistics II (3)
  • STA6236 - Regression Analysis (3)
• Complete at least 1 of the following:
  • STA5703 - Data Mining Methodology I (3)
  • STA6366 - Statistical Methodology for Data Science I (3)
• Complete at least 1 of the following:
  • STA6704 - Data Mining Methodology II (3)
  • STA6367 - Statistical Methodology for Data Science II (3)

Elective Courses

• Complete all of the following:
  • Select electives from the following courses. No more than one Computer Science (COP prefix) course can be selected. Other courses may be included in a Plan of Study with departmental approval.
  • Complete at least 2 of the following:
    • COP5711 - Parallel and Distributed Database Systems (3)
    • COP6730 - Transaction Processing (3)
    • COP6731 - Advanced Database Systems (3)
    • STA5205 - Experimental Design (3)
    • STA5505 - Categorical Data Methods (3)
    • STA5825 - Stochastic Processes and Applied Probability Theory (3)
    • STA6106 - Statistical Computing I (3)
    • STA6226 - Sampling Theory and Applications (3)
    • STA6237 - Nonlinear Regression (3)
    • STA6507 - Nonparametric Statistics (3)
    • STA6707 - Multivariate Statistical Methods (3)
    • STA6857 - Applied Time Series Analysis (3)
    • STA6705 - Data Mining Methodology III (3)
    • FIN6406 - Strategic Financial Management (3)
    • STA6107 - Statistical Computing II (3)
    • STA6329 - Statistical Applications of Matrix Algebra (3)
    • STA6246 - Linear Models (3)
    • STA6346 - Advanced Statistical Inference I (3)
    • STA6347 - Advanced Statistical Inference II (3)
    • STA6662 - Statistical Methods for Industrial Practice (3)
    • STA6709 - Spatial Statistics (3)
    • STA7722 - Statistical Learning Theory (3)
    • STA7734 - Statistical Asymptotic Theory in Big Data (3)
    • STA5738 - Data and Analytical Methodology for Metropolitan and Regional Areas (3)
    • STA6223 - Conventional Survey Methods (3)
    • STA6224 - Bayesian Survey Methods (3)
    • STA7239 - Dimension Reduction in Regression (3)
    • STA7348 - Bayesian Modeling and Computation (3)
    • STA7719 - Survival Analysis (3)
    • STA7935 - Current Topics in Big Data Analytics (3)
    • CNT5805 - Network Science (3)
    • STA5176 - Introduction to Biostatistics (3)

Thesis/Nonthesis Option

• Complete 1 of the following:
  • Thesis Option:
    • Complete all of the following:
      • For this option, the MS degree requires a total of at least 36 credit hours comprised of at least 30 credit hours of course work and 6 credit hours of thesis.
      • Earn at least 6 credits from the following:
        • STA6971 - Thesis (1 - 99)
  • Nonthesis Option:
    • Complete all of the following:
      • Nonthesis students will take an additional 3 credit hours of electives and 3 credit hours of independent study for a research project.
      • Earn at least 3 credits from the following types of courses: Courses listed in "Elective courses" above.
      • Earn at least 3 credits from the following:
        • STA6908 - Directed Independent Studies (1 - 99)

Grand Total Credits: 36

Application Requirements

Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. For more information, see the College of Graduate Studies Funding website.

Financial Information

The Financial Information section of the Graduate Catalog is another key resource.

Fellowship Information

Fellowships are awarded based on academic merit to highly qualified students. They are paid to students through the Office of Student Financial Assistance, based on instructions provided by the College of Graduate Studies. Fellowships are given to support a student's graduate study and do not have a work obligation.

All MS students must have an approved Plan of Study (POS) developed by the student and advisor that lists the specific courses to be taken as part of the degree. Students must maintain a minimum GPA of 3.0 in their POS, as well as a "B" (3.0) in all courses completed toward the degree and since admission to the program.