Master’s in Applied Biomedical Data Sciences Curriculum

An Expansive Learning Environment

photo of man in class

The Master’s in Applied Biomedical Data Sciences program provides students an opportunity to develop a multidisciplinary skillset that allows them to lead and partner in biomedical research initiatives as data science experts.

The program equips students with tools and knowledge via an expansive curriculum that covers the following areas:

  • Biology, Computing, and Mathematics
  • Biostatistics, Computational Biology, Bioinformatics, Omics Data Analysis
  • Data Wrangling, Workflow and Dashboard Development
  • Communication Skills and Ethics

The one-year, in-person practicum at St. Jude provides students a mentored opportunity to apply and further develop their expertise via a practical experience mentored by a faculty committee that culminates in a written thesis, development of a software or data resource, and an oral defense.

Courses and Schedule

The core curriculum of the Applied Biomedical Data Sciences program consists of 13 courses taken in an accelerated format spread over two semesters that are completed in ten months. The required coursework is listed below.

Year two of the program is designed to explore the practical application of biomedical data science in mentored practicum that best fit the students individual interests and goals.

Year 1
Fall Semester Ethics & Professionalism in Biomedical Data Science
Essential Computing Skills for Biomedical Data Science
Essential Biology for Biomedical Data Science
Data Wrangling and Data Bases
Essential Mathematics for Biomedical Data Science
Biostatistics I for Biomedical Data Sciences
Spring Semester Biostatistics II for Biomedical Data Sciences
Scientific Rigor in Data Analysis
Omic Data Analysis I
Machine Learning
Scientific Communication
Scientific Application Development
Electives (listed below)
Year 2
Summer Semester Practicum
Fall Semester Practicum
Spring Semester Practicum

Electives: Omic Data Analysis II, Neuroimaging Statistics, Clinical Trials, High Performance Computing, Structural Bioinformatics