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The Certificate in Biomedical Data Science (CBDS) program is designed to provide specialized education  where students will gain experience to speed the development of Biomedical Data Science strategies, to refine education programs for future leaders in the field of Biomedical Data Science (BDS), and to translate BDS research findings into public health messages. As our students go through our program and they will begin directly implementing the acquired knowledge on data science into existing research being performed at Íø±¬³Ô¹Ï.

This 16 semester-credit-hour (SCH) program within the Íø±¬³Ô¹Ï San Antonio Graduate School of Biomedical Sciences (GSBS) offers formal coursework in topics relevant to Biomedical Data Science. The educational objectives for the CBDS consist of the students’ fundamental understanding and abilities to utilize standard Data Science programming and platforms such as R, Python and SQL and the software tools used to develop user friendly and powerful data science applications. Students will also become proficient in cross-cutting best practices for developing informative visualizations, in application of supervised and unsupervised techniques for machine learning, and in the ability to assess and optimizes artificial intelligence solutions for real-world performance.

The CBDS program is structured to accommodate  working healthcare professionals who desire additional training in the evolving field of Biomedical Data Science.

Program Curriculum:

Our curriculum consists of 13 semester credit hours of required coursework and 3 semester credit hours of elective coursework. The required coursework will be composed of 7 courses. These courses will be: 

Students Elective coursework (3 SCH) will enable students to focus in either Clinical applications or in Bioinformatic applications.   

Marketable Skills our Certificate Offers:

Upon completion of the CBDS Degree in Data Science, students will be able to:

• Manage Data Science teams
• Collaboration effectively within healthcare organizations
• Lead data-driven approaches to healthcare
• Assess and optimizes machine learning and artificial intelligence solutions for real-world performance
• Explain the social and ethical responsibilities of data scientists
• Utilize programming in R & Python to solve analytical problems in medical practice
• Develop user friendly and powerful data science applications
• Utilize cross-cutting best practices for developing informative visualizations.
• Apply supervised and unsupervised techniques for machine learning
• Utilize random forests, support vector machines, boosting, deep learning, K-means clustering and mixture models
• Apply data scientific algorithms to overcome healthcare challenges