Biostatistics and Medical Data Science
The new Biostatistics and Medical Data Science track will offer an additional application period from February 16 to 22, 2025, for applicants to the Fall 2025 class.
Medical and health-related research has significantly advanced with the rise of data science. This field addresses a wide range of biological and medical challenges, focusing on developing novel methodologies to propel biomedical discoveries. Data science leverages cutting-edge tools and techniques to uncover hidden patterns and trends, providing valuable insights that inform medical and healthcare decisions. Biostatistics is a cornerstone of biomedical data science. Advanced algorithms and theories in data science depend on biostatistical methods to identify and transform data patterns into actionable evidence. Our trainees will be well-versed in biostatistics to collect, preprocess, analyze, evaluate, and draw meaningful conclusions from data. Biostatistics is also essential in translating data into statistically valid and clinically relevant conclusions, bridging the gap between big data and its clinical applications in biomedical research. This integration plays a critical role in advancing precision medicine and healthcare research.
The PhD Track in Biostatistics and Medical Data Science provides comprehensive training for students seeking to excel in the analysis and interpretation of complex biological and medical data. The program begins with foundational coursework in statistical methods, data science, and their applications to biomedical research. Students develop expertise in areas such as computational biology, real-world data analysis, and clinical trial design. As they progress, students have the opportunity to specialize in advanced statistical modeling, big data analytics, or the integration of multi-omics data for precision medicine. This track is designed to equip students with the skills necessary to contribute to cutting-edge research in healthcare and biomedical sciences, addressing key challenges in medical data analysis and interpretation.
Note: A Master’s degree in biostatistics/statistics is preferred but not required.
Research areas include:
- Biostatistical method development for medical data science and computational biology
- Real-world data analytics in healthcare (e.g., Claims and EHR)
- Computational biology for omics data analysis (e.g., genomics, metabolomics, imaging)
- Machine learning/artificial intelligence to inform clinical decision-making based on biomedical data with complex structures
- Modern statistical design and modeling for clinical trials and epidemiological studies
The primary objective of this track is to cultivate the next generation of biostatisticians and medical data scientists and equip them with modern biostatistical, machine learning, and AI skills, as well as problem-solving experience. Graduates will be prepared for roles in academia, pharmaceutical companies, biotechnology firms, federal agencies, and more.
Learn about potential faculty mentors here.
Curriculum
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Overall Total Credits = 37-40 credits
- Core Methods Courses = 16 credits
- Dissertation Research = 12 credits
- Elective Courses = 9-12 credits
PhD Required Courses:
Core Methods = 16 credits
- CIPP 907 – Research Ethics (1 credit)
- PREV 801 - Longitudinal Data Analysis (3 credits, Spring Semester)**
- (UMCP) STAT 770 - Categorical Data Analysis (3 credits)**
- (UMCP) STAT 702 - Survival Analysis (3 credits)**
- (UMCP) STAT 740 - Advanced Linear Model I (3 credits)**
- (UMCP) STAT 700 - Advanced Statistical Inference I (3 credits)**
Dissertation Research = 12 credits
Elective Courses = 9-12 credits
- GPLS 716 - Genomics and Bioinformatics (3 credits)
- GPLS 718 - Programming for Bioinformatics (3 credits)
- GPLS 728 - Genomic Data Science (3 credits)
- PREV 720 - Statistical Methods in Epidemiology (3 credits)
- PREV 747/748 - Research Practicum (3 Credits)
- PREV 803 - Clinical Trials and Experimental Epidemiology (Spring Semester, 3 credits, requires PREV 600)
- (UMCP) STAT 741 - Advanced Linear Model II (3 credits)
- (UMCP) STAT 600 - Advanced Probability I (3 credits)
- (UMCP) STAT 601 - Advanced Probability II (3 credits)
- (UMCP) STAT 701 - Advanced Statistical Inference II (3 credits)
- (UMCP) STAT 707 - Bayesian Analysis (3 credits)
- (UMCP) STAT 750 - Multivariate Analysis (3 credits)
- (UMCP) STAT 818 - Missing Data Analysis (3 credits)
Courses with (UMCP) are courses provided by College Park..
Courses with ** are those for the statistical qualifying exams (1-1.5 years). Qualifying exams will include one 5-hour closed-book in-person exam for theory and one take-home methods exam.
How to Apply
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Please follow these instructions carefully.
- Go to https://umaryland.elluciancrmrecruit.com/Apply/Account/Login
- Select the Create Account link
- Complete the Create Account information.
- In the Program of Interest header, select Fall 2025 as your Term of Interest and Molecular Medicine, PhD as your Academic Program.
- From the My Account Dashboard, select Create a New Application
- On the next screen, select Graduate School Application
- Next, select Fall 2025 – Molecular Medicine, PhD and Continue the Current Application
- In the Academic Plans section of the application, answer the “Please indicate your research interest (select one from the drop-down menu):” dropdown with Biostatistics/Medical Data.
- Pay the $75 application fee. Please note that we do not offer any application fee waivers.
- Once you have completed the application, check the Supplemental Items & Documents section to see what else is required and to submit your Recommendation Requests.
Please feel free to contact the director of this track with any questions:
Professor, Department of Epidemiology and Public Health
For questions regarding the application process, please contact Melissa Kroeger at mkroeger@som.umaryland.edu.