The goal of the UTSA Master of Science in Data Analytics (MSDA) program is to produce highly-skilled and educated data analysts who can transform Big Data into usable information for decision makers across a variety of disciplines including business, healthcare and national security.
The curriculum combines a blend of business, information technology, marketing, mathematics and statistics coursework. Core competencies include data analytic algorithms, predictive modeling, data architecture management and analytical interpretation.
The program focuses on traditional business intelligence oriented analytics and provides students specialized expertise in the areas of data science management and data analytic algorithms. Students learn to analyze data sets and develop communication and visualization techniques to shares these insights within organizations.
Drawing upon experiential learning, students will become data savvy professionals and learn the latest tools, techniques and applications used to transform data into meaningful information. Further, they will apply their education by performing real-world data analytics through intensive practicum coursework with strategic business partners.
Make the right choice and earn your Master of Science in Data Analytics degree from The University of Texas at San Antonio College of Business.
Critical Technology Studies Program
As a member of the Intelligence Community Center of Academic Excellence, the UTSA College of Business is offering two options for MSDA students to participate in the Critical Technology Studies Program (CTSP) and receive specialized training regarding the intelligence community and the national security field.
MSDA majors can elect to pursue the CTSP track which provides students with critical technology skills and allows them to specialize in intelligence studies by completing three national security courses and engaging in a hands-on practicum with UTSA’s government and industry partners working in the national security sector.
Students can also elect to pursue the CTSP track, but also complete the Intelligence Studies certificate (pending UTSA approval). In this option, students complete the traditional MSDA program, but complement their knowledge and expertise with additional intelligence-related courses that culminates with the Intelligence Studies certificate.
Funding is available for students pursuing these options. For more information, visit the CTSP Website.
Admission: Fall semester
Credit Hours: 30 credit hours
Format: Daytime and evening cohorts
Duration: 12 months for daytime cohort/21 months for evening cohort
Funding: Scholarships are available
How to Apply
The online application form is available through UTSA’s Graduate School website, and it may be saved and updated until you are ready to pay the application fee and submit.
In addition to the application requirements below, applicants must also meet university-wide graduate admission requirements.
- Completed application form: Includes loading all required documents, paying your application fee and clicking on the “Submit” button
- Current resume: Include employment or other related experience
- Letters of reference
- Statement of academic and personal goals: Approximately 1-2 pages, there are no specific format requirements
- Requirement for standardized tests: GRE/GMAT are waived for Summer and Fall 2020 admission
- Transcripts: Unofficial transcripts must be submitted with application materials. If your application is successful, official transcripts are required by the end of your first semester.
- International applicant TOEFL test scores: Until further notice, any applicants with a TOEFL score on hand should submit their result (regardless of date) as part of the application. Applicants without TOEFL scores, will complete alternate evaluations to demonstrate English proficiency following admission.
- Applicant Evaluation: Applicants will be evaluated for success in the program based on demonstrable academic preparation and/or experience with respect to mathematics, statistics and information technology. Coursework in calculus, differential equations, stochastic processes, statistics and data mining are not required, but show foundational mathematical preparation and are preferred in some combination.
Information systems/technology courses, computer science courses, and/or professional experience related to databases, networks, distributed and cloud infrastructures, and programming are not required, but show foundational information technology preparation and are preferred in some combination.
- Data Analytics Tools and Techniques
- Data Analytics Visualization and Communication
- Data-Driven Decision Making and Design
- Data Analytics Application Studies
- Data Foundations
- Big Data Technology
- Data Analytics Algorithms
- Data Analytics Practicum