Get ready to leverage data to improve healthcare, public health or the sciences with our M.S. Biomedical Data Science program. You will learn valuable skills like data engineering, machine learning and statistical inference and apply them to precision health or population health informatics.
Why Choose Our M.S. Biomedical Data Science Program?
Students are eligible for scholarships up to 50% of tuition.
Core Competencies in Our Biomedical Data Science Master’s
Our curriculum is designed to develop leaders who will improve healthcare, science, and public health through data science. You’ll learn:
You will also choose one of two concentration tracks:
14 courses, 42 graduate credits
Students will gain a common background in bioinformatics through thirteen core courses. The degree culminates with a comprehensive real-life, industry-type capstone, oriented toward the student’s domain of interest.
Courses
Courses are offered on a Fall, Spring and Summer Semester schedule. The Fall and Spring Semester includes three concurrent courses, Each class is held once per week, for three hours. The Summer Semester has one course. It will take two years to complete the program.
MSDS 510 Computer Programming Foundations for Data Science
3 credit hours, Spring, Summer. Pre-requisite(s): None.
Introduction to computer programming for data science using Python, R, and SAS.
There are no pre-requisites for this course. Students are expected to have a working familiarity with the discipline of data science and analytics and general knowledge about the impacts of Big Data in businesses and corporations. All students should have a working knowledge of all aspects of Microsoft Office; and it goes without saying that they should be familiar with Internet access and usage.
MSDS 515 Data Conscientiousness
3 credit hours, Spring & Summer. Pre-requisite(s): None. There are no pre-requisites for this course.
Using Excel, JavaScript, Python, SAS, SQL, and R to develop Data Conscientiousness: ability to immediately recognize the issues involved in data organization that will need to be addressed to tackle a specific problem. Developing skills in all of the preprocessing, scrubbing, cleaning tools (“search and rescue” operations), data imputation and handling of missing values, checking for adherence to data standards, and all of the rest of the time-consuming and dirty work of data projects. Linking structured and unstructured data sources and recognizing how to reshape data to get it into a computer-friendly format (i.e., rows and columns) required by analytical and statistical methods. A gentle introduction to statistics to enable understanding of the statistical difference between observations and variables, along with knowledge of the different scales of measurement so as not to end up with nonsensical analytical results.
There are no pre-requisites for this course. Students are expected to have a working familiarity with the discipline of data science and analytics and general knowledge about the impacts of Big Data in businesses and corporations. All students should have a working knowledge of all aspects of Microsoft Office; and it goes without saying that they should be familiar with Internet access and usage.
MSBD 520 Introduction to Biostatistics
3 credit hours, Fall, Spring. Pre-requisite(s): Elementary Statistics.
Principles of biostatistics and the analysis of clinical and epidemiological data. Descriptions and derivations of statistical methods as well as demonstrations of these methods using SAS. Topics include basic analysis methods, elementary concepts, statistical models and applications of probability, commonly used sampling distributions, parametric and nonparametric one and two sample tests, confidence intervals, applications of analysis of two-way contingency table data, simple linear regression, and simple analysis of variance.
MSDS 525 Data Management Foundations for Data Science
3 credit hours, Spring & Summer. Pre-requisite(s): MSDS 510, 515.
The concepts and structures used to store, analyze, manage, and present (visualize) information and navigation using Python, SQL, SAS, and QGIS. Topics will include information analysis and organizational methods, and metadata concepts and applications. Students will be assisted to identify disparate data sources needed to perform analysis for a given real-world problem. Typically, data from a single source will not be adequate to perform the required analysis. Students will pull data from the disparate data sources and import it into SAS and use several SAS procedures to detect invalid data; format, validate, clean the data; and impute the data if it is missing. This will prepare the data for statistical analysis and decision modeling in SAS.
MSBD 530 Statistical Methods for Biomedical Data Science
3 credit hours, Spring & Summer. Pre-requisite(s): MSDS 510, (MSDS 520 or MSBD 520).
Principles of biostatistics focusing on statistical modeling approaches to the analysis of continuous, categorical, and survival data. Regression modeling includes the links between regression and analysis of variance (parameterization), multiple regression, indicator variables, use of contrasts, multiple comparison procedures and regression diagnostics. The course will generalize these modeling concepts to different types of outcome data including categorical outcomes (i.e., logistic and log-linear modeling) and survival outcomes (i.e., proportional hazards analysis). Students are taught to conduct the relevant analysis using SAS and R.
MSBD 540 Introduction to Artificial Intelligence in Health Care
3 credit hours, Fall, Summer. Pre-requisite(s): MSDS 510, (MSDS 520 or MSBD 520).
Deep dive into recent advances in AI in healthcare, focusing on deep learning approaches for healthcare problems. Foundations of neural networks. Cutting-edge deep learning models in the context of a variety of healthcare data including image, text, multimodal and time-series data. Advanced topics on open challenges of integrating AI in a societal application such as healthcare, including interpretability, robustness, privacy and fairness.
MSBD 550 Applied Machine Learning for Biomedical Data Science
3 credit hours, Fall, Spring. Pre-requisite(s): (MSDS 530 or MSBD 530), (MSDS 535 or MSBD 540).
Introduction to machine learning with biomedical applications. Survey of machine learning techniques, including traditional statistical methods, resampling techniques, model selection and regularization, tree-based methods, principal components analysis, cluster analysis, artificial neural networks, and deep learning. Students implement machine learning models with open-source software for data science. They explore data and learn from data, finding underlying patterns useful for data reduction, feature analysis, prediction, and classification.
MSDS 570 Visualization and Unstructured Data Analysis
3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 520 or MSBD 520.
This course provides a comprehensive exploration of data visualization as a critical tool for interpreting complex data, facilitating insights, and supporting data-driven decision-making. Students will learn data visualization tools and technologies (including Matplotlib, Seaborn, Plotly) essential to analyze massive disparate amounts of information and make data-driven decisions. They will develop the skills necessary to transform raw data into compelling visuals that communicate insights effectively and responsibly.
MSDS 575 Ethics in Data Science
3 credit hours, Spring & Summer. Pre-requisite(s): MSDS 530 or MSBD 530.
Analysis of ethical issues, algorithmic challenges, and policy decisions (and social implications of these decisions) that arise when addressing real-world problems through the lens of data science, and the choices we make at the different stages of the data analysis pipeline, from data collection and storage to understand feedback loops in analysis.
MSDS 580 Research Methods
3 credit hours, Spring, Summer. Pre-requisite(s): MSDS 565, 575.
The research process investigating information needs, creation, organization, flow, retrieval, and use. Stages include: research definition, question, objectives, data collection and management, data analysis and data interpretation. Techniques include: observation, interviews, questionnaires, and transaction-log analysis.
MSDS 590 Capstone
3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 580.
Comprehensive real-life industry-type capstone, oriented toward the student’s domain of interest. Projects will include: formulation of a question to be answered by the data; collection, cleaning and processing of data; choosing and applying a suitable model and/or analytic method to the problem; and communicating the results to a non-technical audience.
Students choose one of two Concentration Tracks, each comprising 3 Courses (9 credit hours):
MSBD 535 Precision Medicine Informatics
3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 525, (MSDS 530 or MSBD 530).
This course will focus on the inherent translational informatics challenges, concerns, and opportunities afforded by precision medicine to provide a more accurate, personalized characterization of patient populations based on various characteristics including molecular (e.g., genomic, proteomic), clinical (e.g., comorbidities), environmental exposures, lifestyle, patient preferences and other information. Informatics is a necessary component to tackle precision medicine. This includes managing Big data, advanced concepts of a huge variety of genomic sequencing datasets emerging in the post-genomic era from several sequencing platforms, creating learning systems for knowledge generation, providing access for individual involvement, and ultimately supporting the optimal delivery of precision treatments derived from translational research.
MSDS 545 Introduction to Computational Software Engineering
3 credit hours, Fall, Summer. Pre-requisite(s): MSDS 525.
Introduction to systems development for computational science. Design, develop, and deploy a set of software components to produce a scalable, reliable, and reproducible experimental system for scientific investigation; Use a variety of approaches to software development team organization, and select techniques that are appropriate in different circumstances.
MSDS 565 Predictive Modeling and Analytics
3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 550 or MSBD 550.
Tools and techniques for building statistical or machine learning models to make predictions based on data. NLP and Text Analytics, Time Series, Experimentation and Optimization.
MSBD 536 Population Health Informatics
3 credit hours, Fall, Spring. Pre-requisite(s): MSDS 525.
Uses a problem- or inquiry-based learning approach where students will take the lead in designing and implementing data- and technology-driven projects that generate data analytics-based solutions for complex public health issues and develop useful data-driven decision strategies. Students will also reproduce and replicate several case studies that illustrate the power of technologies like GIS, GPS, drones, spatial narratives, and web visualization in population health. They will examine the impact of technology on population health informatics and vice versa.
MSBD 545 GIS for Health Informatics
3 credit hours, Fall, Summer. Pre-requisite(s): MSDS 525.
Exposes students to foundational GIS concepts, tools and methods relevant to the health sector. Special attention is given to acquiring, organizing, integrating, analyzing, and visualizing location-based health data with the aid of closed- and open-source GIS software. Students will develop practical competencies in applying GIS to achieve several goals and purposes including understanding and solving spatio-temporal population health problems in ways that are socially and ethically appropriate.
MSBD 565 GIS Algorithms and Programming in Health
3 credit hours, Fall, Spring. Pre-requisite(s): MSBD 545 or MSDS 550 or MSBD 550.
Focuses on customizing or extending the tools, techniques, and capabilities of GIS to meet health application needs. Students will critique the underlying assumptions and implementation approaches of existing algorithms and advance them as needed, or code new computer programs and scripts to assist in mapping spatial distribution of disease outbreaks, modeling spread of infectious diseases, and other health-related applications. They will be exposed to HTML, CSS, JavaScript, and Python coding languages.
Our students have pursued meaningful research topic such as:
Your research projects will be powered by a high-performance, supercomputer network and access to Meharry’s robust, diverse dataset.
Biomedical data scientists also enjoy rewarding salaries*.
Average salary 2024
$123,544
*According to Glassdoor, June 2024.
If you meet one of the above requirements, you may begin the admissions process.
Applicants can apply to begin our programs in the Fall Semester, starting in August. While we accept students on a rolling basis, the following dates serve as application deadlines the semester. Please contact the Office of Enrollment Management about applying after these deadlines.
May 30, 2026: Priority deadline for application. All materials outlined in Step 2 of the applicant process are due by June 13, 2026.
Step 1
Complete an application.
That application will include the following:
Step 2
Step 3
On-campus (or virtual) interview with the School’s Admissions Committee.
International applicants must hold a degree comparable to a regionally accredited US baccalaureate or master’s degree. Applicants submitting transcripts from international colleges and universities are required to have them verified for US degree program equivalency before being considered for admission. Verification from the following organizations is acceptable:
The decision of the verifying organization must be transmitted directly to Meharry Medical College in electronic form.
Adequate proficiency of spoken and written English is essential to success in graduate study, and medical residency training at Meharry Medical College.
Please review Meharry’s F-1 English Language Policy and the College’s English proficiency requirements policy.
Are you interested in the M.S. Data Science or M.S. Biomedical Data Science program but need to improve your programming or statistics skillset? Contact us at sacsenrollment@mmc.edu or complete the request information form to learn about courses you can take to prepare you for either program.
Te’Onnika Hollowell
Student Recruitment and Admissions Specialist
teonnika.hollowell@mmc.edu
