Why Pursue a Graduate Certificate in Data Science
This graduate certificate is designed to give you essential data analytics skills and enhance your value as an essential part of any team. This certificate program is administered by the University College in conjunction with the School of Data Science. The Graduate Certificate in Data Science prepares you for the future of work, with essential knowledge and skills to solve real-world challenges in almost any business setting. Individuals completing this certificate will gain practical data science knowledge as well as hands-on skills in data organization, data visualization, data analytics, data mining, and machine learning.
The certificate is designed for students holding a bachelor’s degree in any discipline and includes fundamental knowledge for those without a programming or data science background. This certificate is perfect for students or current professionals who want to learn data analytics or are required to learn data science for their current role, individuals with a background in statistics looking to increase skills in programming and data management, or developers and tech people who might be looking to change their career or focus on data analytics.
Online Options: Students can also take this graduate certificate 100% online.
Admission & Application Requirements
Applications are submitted through the UTSA Graduate Application. Please upload all required documents (listed below) on your UTSA Graduate Application. It is the applicant’s responsibility to ensure completion and submission of the application, a nonrefundable application fee, and all required supporting documents are on file with UTSA by the appropriate application deadline.
For international students, please note that student visas are not issued at UTSA for non-degree-seeking students, including certificate programs. For more information, visit our international students admission page.
|Data Science (CERT)|
|Required Degree||Bachelor's Degree from an accredited college or university in the United States or have proof of equivalent training at a foreign institution.|
|Minimum GPA||3.0 (on a 4.0 scale) Departments may consider GPA of last 60 semester credit hours|
|Transcripts*||Required from all institutions attended; international transcripts must be recorded/translated to English|
|Credential Evaluation||Required if you have earned university-level credit from foreign institutions. Submit an evaluation of your transcripts from Educational Credential Evaluators (ECE) directly from the graduate admission application platform|
|English Language Proficiency||550 TOEFL Paper / 79 TOEFL Internet / 6.5 IELTS / Duolingo 100|
|*Unofficial transcripts will be taken into consideration for admissions; however, if admitted into the program, you must submit official transcripts to the University.|
Applicants are encouraged to have their admission file completed as early as possible. All applications, required documents and letters of recommendation, if applicable, must be submitted by 5:00 PM U.S. Central Time on the day of the deadline. Deadlines are subject to change.
|Data Science (CERT)|
|Completed applications will be reviewed for admission on a rolling basis. Decisions generally will be made and sent to applicants within 4 to 6 weeks of receiving the application.|
|Academic Year 2023-24||International||Domestic|
|Fall 2023||Not Available||Not Available|
|Spring 2024||Not Available||Not Available|
|Summer 2024||April 1||May 15|
|Academic Year 2024-25||International||Domestic|
|Fall 2024||June 1||August 1|
|Spring 2025||October 1||October 1|
|Summer 2025||March 1||May 1|
UTSA prepares you for future careers that are in demand. The possible careers below is data pulled by a third-party tool called Emsi, which pulls information from sources like the U.S. Bureau of Labor Statistics, U.S. Census Bureau, online job postings, other government databases and more to give you regional and national career outlook related to this academic program.
Coursework in this degree program covers:
- Computational and statistical foundations of data science
- Assumptions and limitations of different data analytics techniques
- Applying data science methodologies and tools to data-driven problems across disciplines
- Extracting knowledge from big data to address real-world challenges
- Retrieving, manipulating, analyzing, and visualizing data utilizing Python, R, and other data science tools
- Data Analyst
- Business Intelligence
- Defense and Security
- Natural Sciences
- Health Care
- Social Sciences