Program Overview
The St. Jude Graduate School of Biomedical Sciences (SJGS) offers a Master of Science in Applied Biomedical Data Sciences (MS-ADS) to prepare students to be effective collaborative biomedical data scientists in roles as staff computational biologists, bioinformaticians, or biostatisticians. The program consists of ten months of accelerated coursework in ethics, communications, biostatistics, and bioinformatics and a formal twelvemonth academic practicum mentored by an advisory committee of three SJGS faculty members that is led by a data science faculty member and includes a collaborative clinician or laboratory biologist.
Student Learning Objectives
SLO 1: Understand and appropriately apply advanced statistical and computational analysis tools to facilitate scientifically rigorous interpretation of biomedical research data.
SLO 2: In collaboration with biomedical and data science colleagues, develop and execute plans to complete the data scientific components of a biomedical research project including formulation of scientific questions, study design, data acquisition, data management, data analysis, and dissemination of research findings.
SLO 3: Communicate clearly, accurately, and professionally to disseminate scientific concepts and research results in visual, oral, computational, and written form to diverse audiences.
Thesis - Practicum
Students are required to complete a practicum for eight credit hours. The student must form a supervisory committee of at least three St. Jude Graduate School faculty members to direct the practicum experience. The committee will be led by one or two primary mentors. At least one primary mentor must be a data scientist who is first or senior author of a software or data resource published in a peer-reviewed journal. The committee must also include a biologist or clinician to advise on the biomedical collaborative value of the project. The student will prepare and orally defend a project proposal to be reviewed and approved by the committee. The student will then complete the proposed project by producing a written thesis, developing a software or data resource of value for future research, and giving an oral defense of their work. A mid-point “check-in” meeting of the supervisory committee to evaluate progress is recommended, but not required. The thesis committee will use a rubric to evaluate the thesis, software or data resource, and the oral defense in terms of the student learning objectives listed above. In the practicum, the student must complete a biomedical data science research project by applying statistical and computational data analysis methods to existing biomedical data to answer a scientifically compelling biomedical research question. In completing this task, the student will develop software and/or data resources of value to future researchers. Potential software resources include workflows, pipelines, packages, or dashboards that are well-documented to provide future researchers a roadmap or utility to readily perform similar data analyses for similar research problems. Potential data resources may include a clean and harmonized data resource that can be made available to other researchers to benefit their research projects.
MS - Applied Biomedical Data Sciences Core Curriculum
Year 1 - Fall (16 Credits)
| ADS 8001 | Ethics and Professionalism in Biomedical Data Sciences | 1 |
| ADS 8101 | Essential Computing Skills for Biomedical Data Sciences | 3 |
| ADS 8111 | Essential Biology for Biomedical Data Sciences | 3 |
| ADS 8121 | Essential Mathematics for Biomedical Data Sciences | 3 |
| ADS 8131 | Data Bases and Data Wrangling | 3 |
| ADS 8141 | Biostatistics for Biomedical Data Scientist I | 3 |
Year 1 - Summer (2 Credits)
| ADS 8194 | Practicum in Applied Biomedical Data Science | 3 |
Year 2 - Fall and Spring - (6 Credits)
| ADS 8194 | Practicum in Applied Biomedical Data Science | 3 |
| ADS 8194 | Practicum in Applied Biomedical Data Science | 3 |
Electives
Year 1 - Spring (16 Credits)
| ADS 8142 | Biostatistics for Biomedical Data Sciences II | 3 |
| ADS 8152 | Scientific Rigor in Biomedical Data Sciences | 2 |
| ADS 8162 | Omics Data Analysis I | 3 |
| ADS 8172 | Machine Learning | 3 |
| ADS 8182 | Effective Communication for Biomedical Data Scientists | 1 |
| ADS 8192 | Developing Scientific Software Applications | 3 |
| *Elective (see below) | |