Biomedical Data Science Certificate
Admission deadlines can be found on the program admission webpage for matriculation in to the fall academic semester.
All students should have a sufficient educational background in the quantitative computational or biomedical sciences prior to admission to the program. It is expected that most students will have a health professional degree (e.g., MD, DDS/DMD, DVM, or BS in nursing and/or allied health) or a BS/BA, MS, or PhD degree with emphasis in statistics, data science or a health-related discipline. The following general requirements will be applied:
- A medical, dental, Ph.D., masters and/or baccalaureate degree from an accredited institution in the United States or an U.S. equivalent degree and training at a foreign institution as determined by an evaluation from the Educational Credential Evaluators, Inc. (ECE) or the World Education Services, Inc. (WES) of the foreign transcripts.
- A Grade Point Average (GPA) no lower than a B (3.00 in a 4.00 system) in the last 60 hours of coursework for a BS/BA degree or a GPA of at least 3.0 for applicants with a MS degree
- A valid TOEFL/ Duolingo/ IELTS score that is less than two years old at the time of submitted application. The minimum TOEFL score for the Graduate School of Biomedical Sciences is 84, the Duolingo is 115 and 7.0 for the academic version of the IELTS. During the application process, unofficial documents will be accepted, but official documentation must be submitted prior to receiving an official offer of admission.
- Three (3) Letters of Recommendation attesting to the applicant's readiness for graduate level studies in Biomedical Data Science. These letters of recommendation should be uploaded by the individual recommenders who will receive an e-mail from the EMBARK online application system with a link to the Recommendation Form.
A. Residents or fellows in an approved UT Health San Antonio residency or fellowship program are required to submit one (1) of the three (3) letters from their departmental chair or program director with a statement indicating the availability and approval of release of time for the completion of the CBDS educational activities.
B. Staff employed at UT Health San Antonio are required to submit one (1) of the three (3) letters from their authorized supervisor with a statement indicating the availability and approval of release time for the completion of the CBDS educational activities.
C. Faculty (non-tenured only) at UT Health San Antonio are required to submit one (1) of the three (3) letters from the chair of their department. In addition, the chair’s letter must have the approval of both the Dean of the school that houses the department and the President (or his designee) of UT Health San Antonio. (See the Handbook of Operating Policies(HOP), Policy 3.2.5)
- A personal statement (1-2 pages) that includes a brief description of the applicant's background, long term research and/or career goals, and an indication of the basis for application into the CBDS program including how this program fits into the applicant's career objectives. The personal statement should be submitted with the online application to the GSBS.
- Current curriculum vitae. This should be submitted with the on-line application to the GSBS.
Program-Specific Policies for Laptop Computers
Students are required to have laptop computer that can connect to and operate over a wireless network.
Laptops with an Apple based operating system must be able to also operate using a Windows based operating system.
The proposed certificate program will target professionals already working in the health care field who are positioned to implement the acquired skills and knowledge. The certificate curriculum will be composed of 13 SCH of required coursework and 3 SCH of electives (non-prescribed). We have created five unique courses that students will be able to absorb and apply to real world health care issues, applications and clinical/educational programs. It is estimated that a student would be able to complete this certificate program within one year.
Plan of Study
| First Year | ||
|---|---|---|
| Fall | Credit Hours | |
| TSCI 5070 | Responsible Conduct of Research | 2 |
| CSAT 6005 | Rigor & Reproducibility | 1 |
| TSCI 5201 | Statistical Principles of Machine Learning for Biomedical Data | 3 |
| TSCI 5230 | Analytical Programming for Biomedical Data Science | 3 |
| Total Credit Hours: | 9.0 | |
| First Year | ||
|---|---|---|
| Spring | Credit Hours | |
| TSCI 6201 | Data Science Leadership in Healthcare | 1 |
| TSCI 6202 | Data Visualization and Building Applications | 2 |
| TSCI 6203 | Practicum in Biomedical Data Science | 1 |
| Elective courses | 3 | |
| Total Credit Hours: | 7.0 | |
Elective courses:
| TSCI 5073 | Integrated Molecular Biology With Patient-Oriented Clinical Research | 1 |
| TSCI 5074 | Data Management, Quality Control And Regulatory Issues | 2 |
| TSCI 5075 | Scientific Communication | 2 |
| TSCI 6060 | Patient-Oriented Clinical Research Methods-2 | 2 |
| TSCI 6061 | Patient-Oriented Clinical Research Biostatistics-2 | 2 |
| TSCI 6100 | Practicum In IACUC Procedures | 1 |
| TSCI 6102 | Practicum In IRB Procedures | 1 |
| CSAT 6095 | Analysis and Visualization of Genomic Data | 2 |
| CSAT 5024 | RNA Biology and Genomics II | 1 |
| INTD 6062 | Next-Generation Sequencing Data Analysis | 2 |
The Program Objectives for the Certificate in Biomedical Data Science program consist of the following:
- Lead, manage and collaborate data science teams with data driven approaches to healthcare organizations.
- Recognize the social and ethical responsibilities of data scientists.
- Utilize programming languages and software to implement data science capabilities such as machine learning, data visualization, statistical and causal inference.
- Assess and optimize artificial intelligence solutions for real-world performance.
- Three (3) program Student Learning Outcomes (SLOs) (see below) have been established by the program leadership to identify and develop direct measures of student assessment and to ensure student success.
Students will be able to:
- Use statistical methods, programming languages, and analytic software with biomedical data to develop data science applications. Assessment: use of rubric to assess final project with data visualizations and building data science applications
- Apply techniques for machine learning to healthcare data. Assessment: use of rubric to assess final project and building models that predict healthcare outcomes using machine learning.
- Measure, assess, and analyze health outcomes within healthcare organizations. Assessment: use of rubric to assess final project with data visualizations and data science applications
Software required:
- Microsoft Office Suite (A personal copy of the latest version can be purchased at the health science center bookstore at student pricing with a student ID)
Laptops with an Apple based Operating System must be able to also operate using a Windows based Operating System.
Attendance Policy
The UT Health Science Center at San Antonio MSCI-TS faculty believe that attendance at scheduled classes and examinations is crucial to meeting course and program objectives. Therefore, regular attendance in class is expected of each student. Attendance is defined as being present within 15 minutes after the scheduled beginning of the class and until 15 minutes before the scheduled ending of the class.
Excused absences may be granted by the course director in cases such as formal presentations at scientific meetings, illness, or personal emergency. Excused absences are considered on an individual basis and require electronic communication with the course director to request an excused absence. The e-mail request to the course director for consideration of an excused absence must provide details regarding the circumstances and specific dates. It is expected that students will provide advanced notice of absence for scheduled events.
Repeated unexcused absences make it impossible to achieve course objectives. Thus, if a student has excessive unexcused absences in a given course, they will automatically receive a grade of unsatisfactory unless makeup has been approved by the course director (see below). Allowable unexcused absences will be determined by the credit hours of the course as follows:
Absence Makeup. Makeup of absences (both excused and unexcused) is allowed at the discretion of the course director.
Courses
CSAT 5024. RNA Biology and Genomics II. 1 Credit Hour.
The challenges of controlling RNA viruses, the promise of RNA vaccines and the recent findings on the roles of ncRNAs and RNA binding proteins in human disease highlight the importance of studying RNA biology. This course, coupled with MMED 6001, covers all aspects of RNA expression and metabolism, such as RNA processing, decay, transport, alternative splicing and translation and, the function of RNA binding proteins and non-coding RNAs. We will also discuss recent discoveries, such as RNA vaccines, RNA granules, RNA modification, the impact of RNA mediated processes in metabolic syndrome, neurodegenerative diseases and cancer and, RNA therapeutics. Another important goal of these courses is to teach students to employ omics methods such as RNA-seq, RIP-Seq, BRIC, CLIP, Ribo-seq, and CRISPR to study these processes and their regulators. This includes hands-on training on biological databases and classes covering examples of the use of genomics. We expect students to acquire skills that will help them visualize how RNA genomics can be used in their own research projects. Open for Cross Enrollment on Space Available Basis.
CSAT 6005. Rigor & Reproducibility. 1 Credit Hour.
This course will focus on two of the cornerstones of science advancement, which are rigor in designing and performing scientific research and the ability to reproduce biomedical research findings. The course will also emphasize the application of rigor that ensures robust and unbiased experimental design, methodology, analysis, interpretation, and reporting of results. The notion that when a result can be reproduced by multiple scientists, it validates the original results and readiness to progress to the next phase of research will be covered in this course. This is especially important for preclinical studies that provide the basis for rigorous clinical trials in humans. In recent years, there has been a growing awareness of the need for rigorously designed published preclinical studies, to ensure that such studies can be reproduced. The aim of this course is to help attendees acquire the skills necessary to meet the need to enhance rigor and reproducibility in preclinical scientific research. Successful completion of CSAT 5095, or an equivalent approved by the Rigor & Reproducibility course director, is a prerequisite for this course.
CSAT 6095. Analysis and Visualization of Genomic Data. 2 Credit Hours.
This course covers the basics of genomic data analysis and visualization. The focus is on general computational methods, their basis in biomedicine, and how to evaluate and visualize analysis results. Students are expected to be able to qualitatively describe the algorithms presented.
Prerequisites: CSAT 5095 or Equivalent.
INTD 6062. Next-Generation Sequencing Data Analysis. 2 Credit Hours.
Next-generation sequencing (NGS) is becoming increasingly commonplace in biomedical research. For many labs, the main bottleneck to implementing NGS applications is data analysis. This course is designed to introduce students to bioinformatics analysis of NGS data. The course consists of two modules: the first module covers working in the Unix/Linux environment, mapping NGS data to a genome of interest, and performing downstream analysis of RNA-seq, ChIP-seq, and ATAC-seq data. The second module will be an introduction to the programming language Perl, which will enable students to perform custom bioinformatics analysis. This course will be taught in the form of interactive hands-on computer classes. No prior knowledge of programming or coding is required.
TSCI 5070. Responsible Conduct of Research. 2 Credit Hours.
This foundational course introduces students to core ethical content necessary for responsible research conduct. Through interactive seminars, students will learn about (1) scientists as responsible members of society (contemporary ethical issues in biomedical research and environmental/social impacts of research), (2) policies for research with human subjects and vertebrate animals, (3) collaborative research, (4) conflicts of interest (personal, professional, financial), (5) data acquisition and laboratory tools (management, sharing, ownership), (6) responsible authorship and publication, (7) mentor/trainee responsibilities and relationships, (8) peer review (9) research misconduct (forms of misconduct and management policies) (10) informed consent, privacy regulations, good clinical practice, and special populations in clinical investigations.
TSCI 5073. Integrated Molecular Biology With Patient-Oriented Clinical Research. 1 Credit Hour.
This interdisciplinary course is designed to train participants on integrating molecular biology methods into patient-oriented clinical research. Students will have the opportunity to learn to: (1) appropriately use molecular terms in clinical investigation; (2) describe the events involved in protein synthesis; (3) describe the principles involved in molecular techniques (e.g., polymerase chain reactions, southern blots); (4) identify the appropriate specimens, collection, and handling requirements for each molecular technique; (5) identify and correct common sources of error in performing molecular techniques; (6) cite examples of clinical applications of molecular techniques in clinical medicine; and (7) apply molecular techniques in the laboratory to specific clinical problems.
TSCI 5074. Data Management, Quality Control And Regulatory Issues. 2 Credit Hours.
This interdisciplinary course is designed to train participants in the necessary data management and quality control procedures required for the conduct of patient-oriented clinical research. It consists of three segments: (1.) introduction to data management principles and standard practices; (2) development of the student's own mentored research; and (3) introduction to bioinformatics.
TSCI 5075. Scientific Communication. 2 Credit Hours.
This interdisciplinary course is designed to train participants to write effectively in all aspects of conducting patient-oriented clinical research. Students will have the opportunity to learn to and, by the end of the course, be required to: (1) recognize and avoid errors in grammar, punctuation, and usage that are common in scientific writing; (2) construct units of writing whose structure, style, and logical continuity allows instant and clear comprehension; (3) construct concise, informative titles; (4) develop clear, comprehensive, abstracts for papers and grant proposals; (5) construct complete, well-rationalized sets of specific aims for grant proposals; and (6) effectively apply the 4-Point Rule (What is the question? How did we approach it? What happened? What does it mean?) to all forms of scientific writing.
TSCI 5201. Statistical Principles of Machine Learning for Biomedical Data. 3 Credit Hours.
This class offers a hands-on approach to machine learning and data science. The class discusses the application of supervised and unsupervised techniques for machine learning including random forests, support vector machines, boosting, deep learning, K-means clustering and mixture models. The course focuses on real data application with open source implementations in Python and R. Prerequisites: Introductory-level course in probability and statistics; comfort with a programming language (R and/or Python) will be essential for completing the homework assignments; basic linear algebra and calculus are plus.
TSCI 5230. Analytical Programming for Biomedical Data Science. 3 Credit Hours.
This class offers a hands-on approach to data science programming for biomedical research. We will introduce R, Python, SQL, and the software tools that interoperate with them. We will also cover cross-cutting best practices for organizing one's work to facilitate collaboration, reproducibility, and portability. Students who already have data they want to analyze are encouraged to use it in their assignments.
TSCI 6060. Patient-Oriented Clinical Research Methods-2. 2 Credit Hours.
This interdisciplinary course is the second in a two-semester sequence designed to train participants in the conduct of patient-oriented clinical research. Students will have the opportunity to learn to and, by the end of the course be required to: (1) define criteria for inferring causation from observational studies; (2) design strategies for subject retention in a prospective study; (3) design strategies for monitoring progress in a randomized control trial; (4) delineate strategies for minimizing bias in cohort studies and randomized control trials; (5) compare and contrast the uses, strengths, and weaknesses of different clinical trial designs; (6) read and interpret research reports of cohort studies and randomized control trials; and (7) describe the steps in conducting a meta-analysis.
Prerequisites: TSCI 5071.
TSCI 6061. Patient-Oriented Clinical Research Biostatistics-2. 2 Credit Hours.
This interdisciplinary course is the second in a two-semester sequence designed to train participants in the biostatistical analysis and patient-oriented clinical research. Students will have the opportunity to learn to and, by the end of the course, be required to: (1) perform a two-way analysis of variance and explain the results; (2) perform survival analysis; (3) compare and contrast the purpose and characteristics of different forms of interventional trials; and (4) plan the sample size, analysis, and stopping rules of a randomized clinical trial.
Prerequisites: TSCI 5072.
TSCI 6100. Practicum In IACUC Procedures. 1 Credit Hour.
This elective course presents an in-depth introduction to the institutional program that provides oversight and regular review of projects that involve the care and use of animals. This includes consideration of the operational procedures of the Institutional Animal Care and Use Committee (IACUC) of the UT Health Science Center at San Antonio. Course objectives are achieved through a combination of readings, monthly attendance at selected IACUC meetings, and discussions with faculty.
TSCI 6102. Practicum In IRB Procedures. 1 Credit Hour.
This elective course presents an in-depth introduction to the institutional program that provides oversight and regular review of research projects that involve human subjects. This includes consideration of the operational procedures of the multiple Institution Review Boards (IRB) of the UT Health Science Center at San Antonio. Course objectives are achieved through a combination of readings, monthly attendance at selected IRB meetings, and discussions with faculty.
TSCI 6201. Data Science Leadership in Healthcare. 1 Credit Hour.
This class offers a hands-on approach to data science operations in biomedical science. The class discusses the management of data science teams, collaboration within healthcare organizations, and the social and ethical responsibility of data scientists. The course focuses on real world applications.
TSCI 6202. Data Visualization and Building Applications. 2 Credit Hours.
This class offers a hands-on approach to data visualization for biomedical data science. The class uses R, Python and Javascript and the software tools that interoperate with them. Some cross-cutting best practices. The course focuses on real world applications. Prerequisites: Introductory-level courses in probability and statistics; comfort with a programming language will be helpful to completing the homework assignments.
TSCI 6203. Practicum in Biomedical Data Science. 1 Credit Hour.
This elective course provides an opportunity for participation in unique biomedical data science and translational research activities that are highly individualized for each student on the basis of prior experience and research interests.