Feb 21, 2024  

Data Analytics, MS

Ermelinda DeLaVina, PhD, MSDA Director

The Master of Science (MS) in Data Analytics is a theory and application-based program that will provide students with a broad education in advanced statistics, digital data acquisition, digital data management, data analysis, and data presentation. The MS in Data Analytics is designed to meet the increasing need for highly skilled data analysts who can analyze the growing amount of data confronting in a variety of disciplines, and transform it into usable information for use in decision making. The program is a university-wide collaboration that delivers rigorous training in statistical analysis and computational techniques, and provides mastery of data analysis tied to interdisciplinary applications. Students in the program study 30 hours of foundation courses with topics in regression analysis, multivariate analysis, nonparametric analysis, statistical modeling, data mining, information visualization, simulation, mathematical theory for data analysis, and statistical computing. Students also take 2 courses of interdisciplinary application courses in business management, science, criminal justice, education, communication, and social sciences. In the final 3 hours students will have the opportunity to engage in research or real-world applications with faculty members at UHD and other collaborators of UHD faculty and/or internships with partnering businesses, industry, and government agencies.

Program Outcomes

Students who complete the program will be able to:

  • Organize, manipulate, and summarize data in various formats.
  • Convert a data analytic problem and related information into proper mathematical representation and select appropriate methodologies for analysis based on attributes of the available data sets.
  • Implement security measures and ethical practices for collection and storage of data.
  • Transfer (and transform) data from different platforms into usable contexts.
  • Communicate and summarize results of data analysis in written, oral and visual form.
  • Select the appropriate methods and tools for data analysis in specific organizational contexts.

Admission Requirements

Admission requirements for the MS in data analytics are designed to identify applicants who have the ability, interest, and qualities necessary to complete the program. 

Admission is competitive and selective. Applicants must demonstrate that they possess the abilities, interests, and qualities necessary to successfully complete the program.

Applicants seeking admission will provide the following application materials for review by the Graduate Curriculum Committee:

  • A completed Apply Texas application form which will include a 1000-word essay that addresses why you want to study data analytics
  • Baccalaureate degree conferred by a regionally-accredited institution.
  • Official University transcript(s) from which the applicant earned Bachelor’s degree and any advanced degree (if applicable). Official transcripts must reflect
    • the last 60 semester credit hours of course work and
    • ​evidence of Bachelor’s degree awarded and the cumulative GPA.
  • As admission to the degree program is competitive, candidates with a cumulative GPA of 3.0 or higher will be preferred.
  • Graduate Record Exam (GRE) with high percentile scores unless the applicant has a GPA at least 3.0 in their last 60 hours of undergraduate and/or graduate coursework, or unless the applicant has completed STAT 5301 and CS 5301 with grades of B or better.
  • Resume documenting any work experience that emphasizes personal and professional accomplishments.
  • Two letters of recommendation with accompanying recommendation forms from individuals well-acquainted with your work and who are able to address your academic potential, for example, work supervisors and professors.
  • Test of English as a Foreign Language (TOEFL) score, if you are a graduate of a university where English is not the primary language of instruction (preferred TOEFL scores are: an internet-based score of 81, a paper-based score of 553 or an IELTS score of 6.5 or higher.

Admission Process

The Graduate Advisory Committee will evaluate applications using GPA, GRE score, relevant course work or experience, recommendations, and other written materials in the applicant’s file. The Graduate Advisory Committee will use the results of this evaluation to determine if an applicant is admitted. The Assistant Director of Graduate Studies will notify students, in writing, of the committee’s decision.

Degree Requirements

The Master of Science in Data Analytics requires a minimum of 35-36 semester credit hours. 


Graduation is dependent upon satisfactory completion of all course work (including a capstone project or internship course) with a minimum graduate grade point average of 3.0. 

Capstone Options (3 hours)

All students enter into the program’s capstone portfolio option and may petition to change the program capstone option to one of the following - course option, project option, internship option or thesis option subject to the criteria below.

MSDA - capstone portfolio option (CS/STAT 6382): in a student’s last semester, this option provides students the opportunity to intentionally reflect on, refine, and possibly extend a selection of substantive projects completed in the MSDA program.  These may include projects from class, from programming competitions, or other relevant activities.  The resulting project portfolio will provide students a product to use for job applications and interviews or for doctoral program applications.

MSDA - capstone project option (an approved CS/STAT 6399): in a student’s last semester this option provides students the opportunity to conduct a semester long applied research project individually or as part of a team under the supervision of a professor. To be approved for this option a student must have at least a 3.5 GPA in the program and have advanced agreement from an MSDA professor to serve as their supervisor.

MSDA - capstone internship option (CS/STAT 6380): in a student’s last semester, this option is for students who will participate in an approved internship in their last semester of study. Internship must be approved by the MSDA director in advance of enrolling in this option.

MSDA - course option:  this option is primarily for students who are already professionals working in data analytics and seeking this masters as a credential. If approved, the student is allowed to take an additional elective to meet the 35-36 hours in lieu of a capstone course.

MSDA - thesis option (CS/STAT 6387 and CS/STAT 6388): this option is intended for students who plan to develop further depth of knowledge in this discipline and plan to work towards making an original contribution in the discipline. Typically this will include two semesters of Master’s Thesis. To be approved for this option a student must have at least a 3.5 GPA in the program and have advanced agreement from an MSDA professor to serve as their thesis supervisor. With prior approval a student may use CS 6387 or STAT 6387 as one of their 2 electives.

Note: Graduation is dependent upon satisfactory completion of all course work (including a capstone project or internship course) with a minimum graduate grade point average of 3.0. 

Additional Requirements

Minimum Grade Point Average to Remain in the MSDA Program

The expectation for satisfactory completion of graduate courses is completion of all course work with a grade of “B” or better.  At most two grades of “C” will be allowed (not including STAT 5301 and CS 5301) and then only if the cumulative GPA is 3.0 or higher. Also see the catalog section Academic Probation and Suspension for Graduate Students in Degree Granting Program  .

Transfer of Graduate Credits

A maximum of two approved courses may be transferred from another accredited university. A minimum grade of “B” is required for the course to transfer. All petitions for transfer credit into the MSDA must be submitted to the MSDA director.