The Master of Science (MS) in Biostatistics is an applied graduate program intended for students seeking training in applied biostatistics.  The program emphasizes applications and understanding of statistical concepts rather than theoretical and mathematical principles.  The program is meant to be a terminal degree providing students with the necessary background for applying good biostatistical practices in real-world settings.  Students will gain practical skills that can be applied immediately to a variety of data settings, which includes, but not limited to, the biological life sciences public health, medical studies, and health services outcome research.  

The degree consists of 33 credits in a 10-month period (3 semesters) covering introductory probability and mathematical statistics, regression modeling, statistical computing, design and analysis of clinical trials, survival analysis, machine learning and fundamentals of epidemiology and public health. Enrichment will be provided by a statistical practicum and a seminar course varying with special topics. 

Pre-requisites include:  

  1. Introductory calculus 
  2. Introductory computing
  3. Introductory statistics/biostatistics

Admission Requirements

·       Application - Applicants must submit their application online through SOPHAS, the centralized application service of the Association of Schools and Programs of Public Health (ASPPH). All application materials, including transcripts, test scores, statement of purpose/personal statement, resume/CV, and letters of recommendations, must be submitted directly through SOPHAS.

·       Transcripts – Applicants must submit official transcripts from all previously attended colleges and universities. All foreign transcripts must be official and submitted in the original language. If the original language is not English, an official translation must be submitted along with the transcript. We do not accept evaluations from foreign credentialing service organizations. All non-U.S. transcripts must be evaluated by the World Education Service (WES) using ICAP course-by-course evaluation service.

·       Standardized Test Scores - The Graduate Record Exam (GRE) is not required for the MS in Biostatistics degree program. 

·       English Proficiency Exam - International students whose native language is not English and/or did not graduate from an English-teaching institution are required to submit TOEFL or IELTS scores.

·       Resume/Curriculum Vitae – Applicants must include a detailed resume including employment, public health experiences, community service, research, and academic or professional honors.

·       Statement of Purpose/Personal Statement – Applicants are required to submit a statement of purpose that details their academic interest in the program. The statement should discuss any experiences in public health including field experience, research, training, education or other related qualifications. Applicants should discuss how earning the degree will contribute to their future professional and career goals, as well as to the future of public health. Applicants should also address any academic deficiencies, if applicable.

·       Letters of Recommendation– Applicants must provide three letters of recommendation from individuals who are best able to assess their ability to be successful in a graduate degree program. Ideally, recommenders are recent professors, researchers or employers in a related field. Letters must be signed and on letterhead. Applicants will be asked to include the contact information of their recommenders on the SOPHAS application and recommenders will be sent an online form to complete via email.

For more information about our application process, please click here. To obtain detailed curricula on all our program offerings, please visit our website.

For further information, please contact:

Andria L. Williams, MBA
Director of Admissions
Department of Public Health Sciences

University of Miami Miller School of Medicine
1120 N.W. 14 Street, Room 905 (R-669)
Miami, Florida 33136
Tel: 305-243-0291

Curriculum Requirements

BST 605Statistical Principles of Clinical Trials3
BST 610Introduction to Statistical Collaboration3
BST 625Survey of Statistical Computing3
BST 650Topics in Biostatistical Research 12
BST 692Data Science and Machine Learning for Health Research3
EPH 600Introduction to the Science Practice of Public Health3
EPH 621Fundamentals of Epidemiology3
EPH 703Advanced Statistical Methods I4
EPH 705Advanced Statistical Methods II3
EPH 751Survival Analysis in Clinical Trials3
MTH 624Introduction to Probability Theory3
Total Credit Hours33

Plan of Study (10-month)

Plan of Study Grid
First Year
FallCredit Hours
BST 625 Survey of Statistical Computing 3
BST 650 Topics in Biostatistical Research 1
EPH 600 Introduction to the Science Practice of Public Health 3
EPH 703 Advanced Statistical Methods I 4
MTH 624 Introduction to Probability Theory 3
 Credit Hours14
BST 605 Statistical Principles of Clinical Trials 3
BST 650 Topics in Biostatistical Research 1
EPH 621 Fundamentals of Epidemiology 3
EPH 705 Advanced Statistical Methods II 3
EPH 751 Survival Analysis in Clinical Trials 3
 Credit Hours13
BST 610 Introduction to Statistical Collaboration 3
BST 692 Data Science and Machine Learning for Health Research 3
 Credit Hours6
 Total Credit Hours33


The mission of the Graduate Programs in Public Health is to develop leaders who can translate knowledge into policy and practice to promote health and prevent disease in human populations.


Upon completion of the Master of Science (MS) in Biostatistics degree, all graduates will be able to:

  • Describe the core disciplines of public health and how they apply to improving population health.
  • Apply statistical and epidemiological methods to the measurement and study of population health and the prevention of infectious and chronic disease. 
  • Describe concepts in probability theory, random variation and commonly used statistical distributions and mathematical statistics. 
  • Develop sample size and statistical power calculations for different study designs including those from clinical trials and observational studies. 
  • Perform a variety of basic and advanced statistical analyses methods (estimation and inference) including explanatory data analysis, graphical visual displays, analysis of variance (ANOVA), univariate and multivariable regression models, general linear modeling, multivariate analysis, survival analysis, design of experiments and clinical trials, machine learning techniques, various new techniques from statistical methods arising from applications, analyze cross-sectional and longitudinal data from observational and clinical trials. 
  • Apply quantitative and reasoning skills, as well as content-area knowledge, to analyze data from epidemiological, clinical, observational, laboratory, and experimental studies.
  • Interpret results from explanatory and descriptive data analysis and advanced statistical analyses to draw relevant conclusions from data.
  • Develop a high level of competency in statistical programming both with SAS and R for both managing and analyzing data from different sources. 
  • Communicate effectively by producing summary reports, statistical analysis sections of applied papers, graphical summaries and tabular summaries of the data. 
  • Interact with different public health, health care and medical and biomedical professionals to address statistical aspects of their research studies as a part of statistical consultation. 
  • Recognize potential ethical issues and implement the concepts of ethical conduct of research.

Student Learning Outcomes

  • Students will develop and demonstrate effective written and oral communication skills in the presentation of public health information.
  • Students will demonstrate mastery of applied statistical data analysis techniques.
  • Students will develop and demonstrate the ability to make scholarly contributions to the biomedical sciences through effective statistical collaborating efforts.
  • Students will master at least one statistical analysis software for data management and statistical analysis techniques.