Biostatistics Concentration (BIS)
The M.S. with a concentration in Biostatistics is a two-year program that provides training in clinical trials, epidemiologic methodology, implementation science, data science, statistical genetics, and mathematical models for infectious diseases. Students have a choice of three pathways: the Biostatistics Standard Pathway, the Biostatistics Implementation and Prevention Science Methods Pathway, and the Biostatistics Data Science Pathway. In contrast to the more general M.P.H. degree, the M.S. degree emphasizes the mastery of biostatistical skills from the beginning of the plan of study. While graduates of this program may apply to the Ph.D. degree program, the M.S. degree is itself quite marketable as a terminal degree. Part-time enrollment is permitted.
Degree Requirements
The Biostatistics concentration requires the completion of fourteen required and elective courses for the Standard Pathway and the Implementation and Prevention Sciences Pathway. Fifteen required and elective courses must be completed for the Data Science Pathway. These requirements exclude the Seminar, BIS 525/BIS 526; the Summer Internship, BIS 695; PUBH 100; and PUBH 101. Note: Half-term courses cannot count as an elective unless an additional half-term course is taken, and the Biostatistics faculty have approved both courses as an elective. Additionally, all first-year students must participate in an online Public Health Primer course the summer before their first term.
The Graduate School requires an overall grade average of High Pass, including grades of Honors in at least two full-term graduate courses for students enrolled in a two-year program. In order to maintain the minimum average of High Pass, each grade of Pass on the student’s transcript must be balanced by one grade of Honors. Each grade of Fail must be balanced by two grades of Honors. If a student retakes a course in which the student has received a failing grade, only the newer grade will be considered in calculating this average. The initial grade of Fail, however, will remain on the student’s transcript. A grade awarded at the conclusion of a full-year course in which no grade is awarded at the end of the first term would be counted twice in calculating this average.
Curriculum
Required Courses for All Pathways
(or substitutions approved by the student’s adviser and the DGS)
| BIS 525 | Seminar in Biostatistics and Journal Club 1 | 0 |
| BIS 526 | Seminar in Biostatistics and Journal Club 1 | 0 |
| BIS 623 | Advanced Regression Models | 1 |
| or S&DS 6120 | Linear Models | |
| BIS 628 | Longitudinal and Multilevel Data Analysis | 1 |
| BIS 630 | Applied Survival Analysis | 1 |
| or BIS 643 | Theory of Survival Analysis | |
| BIS 678 | Statistical Practice I | 1 |
| BIS 695 | Summer Internship in Biostatistics 1 | 0 |
| PUBH 100 | Professional Skills Series 1 | 0 |
| PUBH 101 | Professional Skills Series 1 | 0 |
| PUBH 508 | Foundations of Epidemiology and Public Health | 1 |
| S&DS 5410 | Probability Theory 2 | 1 |
| or S&DS 5510 | Stochastic Processes | |
| or S&DS 6000 | Advanced Probability | |
| S&DS 5420 | Theory of Statistics 2 | 1 |
| or S&DS 6100 | Statistical Inference | |
| 1 | Course does not count as a credit. |
| 2 | Course offered in the Graduate School of Arts and Sciences. |
Additional Required Courses: Standard Pathway
| BIS 679 | Advanced Statistical Programming in SAS and R | 1 |
| BIS 681 | Statistical Practice II 1 | 1 |
| or BIS 649 | Master’s Thesis Research | |
| or BIS 650 | Master’s Thesis Research | |
Electives Five courses are required. A minimum of two must be from the biostatistics list. The additional three electives can be taken from either list of approved electives below.
| Biostatistics Electives | ||
| BIS 536 | Measurement Error and Missing Data | 1 |
| BIS 537 | Statistical Methods for Causal Inference | 1 |
| BIS 540 | Fundamentals of Clinical Trials | 1 |
| BIS 550/CB&B 7500 | Topics in Biomedical Informatics and Data Science | 1 |
| BIS 555 | Machine Learning with Biomedical Data | 1 |
| BIS 560 | Introduction to Clinical and Translational Informatics | 1 |
| BIS 567 | Bayesian Statistics | 1 |
| BIS 568 | Applied Artificial Intelligence in Healthcare | 1 |
| BIS 629 | Advanced Methods for Implementation and Prevention Science | 1 |
| BIS 631 | Advanced Topics in Causal Inference Methods | 1 |
| BIS 633 | Population and Public Health Informatics | 1 |
| BIS 634 | Computational Methods for Informatics | 1 |
| BIS 638 | Clinical Database Management Systems and Ontologies | 1 |
| BIS 640 | User-Centered Design of Digital Health Tools | 1 |
| BIS 643 | Theory of Survival Analysis 2 | 1 |
| BIS 645 | Statistical Methods in Human Genetics | 1 |
| BIS 646 | Nonparametric Statistical Methods and Their Applications | 1 |
| BIS 691 | Theory of Generalized Linear Models | 1 |
| Additional electives must be approved by the Standard Pathway director. | ||
| 1 | M.S. Biostatistics (Standard Pathway) students are required to complete a two-semester capstone experience in the second year. This requirement can be fulfilled by:
All students who complete a thesis are required to present their research during a public seminar to the Biostatistics faculty and students in order to graduate. |
| 2 | Cannot fulfill elective if substituted for BIS 630. |
| A minimum of three electives must be taken from either the Biostatistics electives list or the list below: | ||
| BENG 5450 | Biomedical Image Processing and Analysis | 1 |
| CDE 566 | Causal Inference Methods in Public Health Research | 1 |
| CDE 634 | Advanced Applied Analytic Methods in Epidemiology and Public Health | 1 |
| CPSC 5371 | Database Design and Implementation 1 | 1 |
| CPSC 5460 | Data and Information Visualization 1 | 1 |
| CPSC 5520/CB&B 6663/AMTH 5520 | Deep Learning Theory and Applications 1 | 1 |
| CPSC 5700 | Artificial Intelligence 1 | 1 |
| CPSC 5710 | Trustworthy Deep Learning | 1 |
| CPSC 5770 | Natural Language Processing 1 | 1 |
| CPSC 5820 | Current Topics in Applied Machine Learning 1 | 1 |
| CPSC 5830 | Deep Learning on Graph-Structured Data 1 | 1 |
| CPSC 6400 | Topics in Numerical Computation 1 | 1 |
| CPSC 6700 | Topics in Natural Language Processing 1 | 1 |
| CPSC/CB&B/MB&B/MCDB 7520 | Biomedical Data Science: Mining and Modeling 1 | 1 |
| CPSC 7760 | Topics in Industrial AI Applications | 1 |
| ECON 5554 | Econometrics V 1 | 1 |
| EMD 553 | Transmission Dynamic Models for Understanding Infectious Diseases | 1 |
| HPM 583 | Methods in Health Services Research | 1 |
| INP 7599 | Statistics and Data Analysis in Neuroscience 1 | 1 |
| MGT 803 | Decision Making with Data 2 | 2 |
| PSYC 5580 | Computational Methods in Human Neuroscience | 1 |
| S&DS 5170 | Applied Machine Learning and Causal Inference 1 | 1 |
| S&DS 5510 | Stochastic Processes 1,4 | 1 |
| S&DS 5620 | Computational Tools for Data Science 1 | 1 |
| S&DS 5630/ENV 758 | Multivariate Statistical Methods for the Social Sciences 1 | 1 |
| S&DS 5650 | Introductory Machine Learning 1 | 1 |
| S&DS 5660 | Deep Learning for Scientists and Engineers | 1 |
| S&DS 5690 | Numerical Linear Algebra: Deterministic and Randomized Algorithms 1 | 1 |
| S&DS 5800 | Neural Data Analysis 1 | 1 |
| S&DS 6000 | Advanced Probability 1,4 | 1 |
| S&DS 6100 | Statistical Inference 1,5 | 1 |
| S&DS 6110 | Selected Topics in Statistical Decision Theory 1 | 1 |
| S&DS 6120 | Linear Models 1,3 | 1 |
| S&DS 6180 | Asymptotic Statistics 1 | 1 |
| S&DS 6310 | Optimization and Computation 1 | 1 |
| S&DS 6320 | Advanced Optimization Techniques 1 | 1 |
| S&DS 6610 | Data Analysis 1 | 1 |
| S&DS 6620 | Statistical Computing | 1 |
| S&DS 6630 | Computational Mathematics Situational Awareness and Survival Skills 1 | 1 |
| S&DS 6640 | Information Theory 1 | 1 |
| S&DS 6650 | Intermediate Machine Learning 1 | 1 |
| S&DS 6740/ENV 781 | Applied Spatial Statistics 1 | 1 |
| S&DS 6850 | Theory of Reinforcement Learning 1 | 1 |
| Additional electives must be approved by the Standard Pathway director. | ||
| 1 | Course offered in the Graduate School of Arts and Sciences. |
| 2 | Course offered in the School of Management |
| 3 | Cannot fulfill elective credit if substituted for BIS 623. |
| 4 | Cannot fulfill elective credit if substituted for S&DS 5410 |
| 5 | Cannot fulfill elective credit if substituted for S&DS 5420 |
Additional Required Courses: Implementation and Prevention Science Methods Pathway
| BIS 629 | Advanced Methods for Implementation and Prevention Science | 1 |
| BIS 679 | Advanced Statistical Programming in SAS and R | 1 |
| BIS 681 | Statistical Practice II 1 | 1 |
| or BIS 649 | Master’s Thesis Research | |
| or BIS 650 | Master’s Thesis Research | |
| EMD 533 | Implementation Science | 1 |
| 1 | M.S. Biostatistics (Implementation Science Pathway) students are required to complete a two-semester capstone experience in the second year. This requirement can be fulfilled by:
Students in this pathway are strongly encouraged to complete a thesis. All students who complete a thesis are required to present their research during a public seminar to the Biostatistics faculty and students in order to graduate. |
| At least one of the following: | ||
| BIS 536 | Measurement Error and Missing Data | 1 |
| BIS 537 | Statistical Methods for Causal Inference | 1 |
| BIS 631 | Advanced Topics in Causal Inference Methods | 1 |
| At least two of the following: | ||
| CDE 516 | Principles of Epidemiology II | 1 |
| CDE 534 | Applied Analytic Methods in Epidemiology | 1 |
| EMD 538 | Quantitative Methods for Infectious Disease Epidemiology | 1 |
| HPM 570 | Cost-Effectiveness Analysis and Decision-Making 1 | 1 |
| HPM 575 | Evaluation of Global Health Policies and Programs | 1 |
| HPM 586 | Microeconomics for Health Policy and Health Management | 1 |
| HPM 587 | Advanced Health Economics | 1 |
| MGT 611 | Policy Modeling | 2 |
| SBS 541 | Community Health Program Evaluation 1 | 1 |
| SBS 574 | Developing a Health Promotion and Disease Prevention Intervention | 1 |
| SBS 580 | Qualitative Research Methods in Public Health 1 | 1 |
| S&DS 5650 | Introductory Machine Learning 2 | 1 |
| Alternative electives must be approved by the Implementation Science Pathway director. | ||
| 1 | Course is highly recommended |
| 2 | Course offered in the Graduate School of Arts and Sciences. |
Additional Required Courses: Data Science Pathway
| BIS 678 | Statistical Practice I | 1 |
| BIS 681 | Statistical Practice II 1 | 1 |
| or BIS 649 | Master’s Thesis Research | |
| or BIS 650 | Master’s Thesis Research | |
| 1 | M.S. Biostatistics (Data Science Pathway) students are required to complete a two-semester capstone experience in the second year. This requirement can be fulfilled by:
All students who complete a thesis are required to present their research during a public seminar to the Biostatistics faculty and students in order to graduate. |
| Two of the following biostatistics, computer science, and statistical methods courses | ||
| BENG 5440 | Fundamentals of Medical Imaging | 1 |
| BIS 536 | Measurement Error and Missing Data | 1 |
| BIS 537 | Statistical Methods for Causal Inference | 1 |
| BIS 540 | Fundamentals of Clinical Trials | 1 |
| BIS 550/CB&B 7500 | Topics in Biomedical Informatics and Data Science | 1 |
| BIS 555 | Machine Learning with Biomedical Data | 1 |
| BIS 567 | Bayesian Statistics | 1 |
| BIS 629 | Advanced Methods for Implementation and Prevention Science | 1 |
| BIS 634 | Computational Methods for Informatics | 1 |
| BIS 645 | Statistical Methods in Human Genetics | 1 |
| BIS 646 | Nonparametric Statistical Methods and Their Applications | 1 |
| CB&B 5620 | Modeling Biological Systems II 1 | 1 |
| CB&B 7520 | Biomedical Data Science: Mining and Modeling 1 | 1 |
| CPSC 5150 | Law and Large Language Models | 1 |
| CPSC 5190 | Full Stack Web Programming 1 | 1 |
| CPSC 5260 | Building Distributed Systems 1 | 1 |
| CPSC 5390 | Software Engineering 1 | 1 |
| CPSC 5650 | Theory of Distributed Systems 1 | 1 |
| CPSC 5770 | Natural Language Processing 1 | 1 |
| CPSC 5880 | AI Foundation Models 1 | 1 |
| CPSC 6400 | Topics in Numerical Computation 1 | 1 |
| CPSC 6420 | Modern Challenges in Statistics: A Computational Perspective 1 | 1 |
| EMD 553 | Transmission Dynamic Models for Understanding Infectious Diseases | 1 |
| MCDB 5000 | Biochemistry | 1 |
| S&DS 5410 | Probability Theory 1,2 | 1 |
| S&DS 5510 | Stochastic Processes 1,3,4 | 1 |
| S&DS 5660 | Deep Learning for Scientists and Engineers | 1 |
| S&DS 6110 | Selected Topics in Statistical Decision Theory 1 | 1 |
| S&DS 6450 | Statistical Methods in Computational Biology | 1 |
| S&DS 6610 | Data Analysis 1 | 1 |
| S&DS 6640 | Information Theory 1 | 1 |
| Additional electives must be approved by the Data Science Pathway director. | ||
| One of the following Machine Learning courses: | ||
| BIS 555 | Machine Learning with Biomedical Data 3 | 1 |
| BIS 568 | Applied Artificial Intelligence in Healthcare | 1 |
| BIS 634 | Computational Methods for Informatics 3 | 1 |
| BIS 691 | Theory of Generalized Linear Models | 1 |
| CB&B 5555/AMTH 5530/CPSC 5530 | Unsupervised Learning for Big Data 1 | 1 |
| CB&B 6663/AMTH 5520/CPSC 5520 | Deep Learning Theory and Applications 1 | 1 |
| CPSC 5690 | Randomized Algorithms 1 | 1 |
| CPSC 5710 | Trustworthy Deep Learning | 1 |
| CPSC 5830 | Deep Learning on Graph-Structured Data 1 | 1 |
| CPSC 6440 | Geometric and Topological Methods in Machine Learning 1 | 1 |
| CPSC 6700 | Topics in Natural Language Processing 1 | 1 |
| S&DS 5170 | Applied Machine Learning and Causal Inference 1 | 1 |
| S&DS 5620 | Computational Tools for Data Science 1 | 1 |
| S&DS 5650 | Introductory Machine Learning 1 | 1 |
| S&DS 5690 | Numerical Linear Algebra: Deterministic and Randomized Algorithms 1 | 1 |
| S&DS 6310 | Optimization and Computation 1 | 1 |
| S&DS 6320 | Advanced Optimization Techniques 1 | 1 |
| S&DS 6650 | Intermediate Machine Learning 1 | 1 |
| S&DS 6740/ENV 781 | Applied Spatial Statistics 1 | 1 |
| S&DS 6840 | Statistical Inference on Graphs 1 | 1 |
| S&DS 6850 | Theory of Reinforcement Learning 1 | 1 |
| S&DS 6860 | High-Dimensional Phenomena in Statistics and Learning 1 | 1 |
| Additional electives must be approved by the Data Science Pathway director. | ||
| One of the following Database courses: | ||
| BIS 550/CB&B 7500 | Topics in Biomedical Informatics and Data Science 3 | 1 |
| BIS 638 | Clinical Database Management Systems and Ontologies | 1 |
| BIS 679 | Advanced Statistical Programming in SAS and R | 1 |
| CPSC 5370 | Database Systems 1 | 1 |
| MGT 656 | Management of Software Development 5 | 4 |
| MGT 660 | Advanced Management of Software Development 5 | 4 |
| Additional electives must be approved by the Data Science Pathway director. | ||
| 1 | Course offered in the Graduate School of Arts and Sciences |
| 2 | Cannot fulfill elective if taken as a requirement |
| 3 | This course can only be counted to fulfill the requirement of one category; it cannot be counted twice |
| 4 | Cannot fulfill elective if taken as a substitute for S&DS 5410 |
| 5 | Course offered at the School of Management |
Two additional electives are required from the biostatistics, machine learning, or database list. Other courses from Public Health or other departments must be approved by the Data Science Pathway director.
Competencies
Upon receiving an M.S. in the Biostatistics concentration of Public Health, the student will be able to:
- Select from a variety of analytical tools to test statistical hypotheses, interpret results of statistical analyses, and use these results to make relevant inferences from data
- Design efficient computer programs for study management, statistical analysis, as well as presentation using R, SAS, and other programming languages
- Demonstrate oral and written communication and presentation skills to effectively communicate and disseminate results to professional audiences