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 fifteen required and elective courses for the Standard Pathway and the Implementation and Prevention Sciences Pathway. Sixteen 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; EPH 100; and EPH 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.
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 612 | 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 |
| EPH 100 | Professional Skills Series 1 | 0 |
| EPH 101 | Professional Skills Series 1 | 0 |
| EPH 509 | Fundamentals of Epidemiology | 1 |
| EPH 608 | Frontiers of Public Health 2 | 1 |
| S&DS 541 | Probability Theory 3 | 1 |
| or S&DS 551 | Stochastic Processes | |
| or S&DS 600 | Advanced Probability | |
| S&DS 542 | Theory of Statistics 3 | 1 |
| or S&DS 610 | Statistical Inference | |
| 1 | These courses do not count toward the fifteen required courses. |
| 2 | Students entering the program with an M.P.H. or relevant graduate degree may be exempt. |
| 3 | These courses are 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 |
| Two of the following suggested electives: | ||
| BIS 534 | Stochastic Models and Inference for the Biomedical and Social Sciences | 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 | Topics in Biomedical Informatics and Data Science | 1 |
| BIS 555 | Machine Learning with Biomedical Data | 1 |
| BIS 560 | Introduction to Health Informatics | 1 |
| BIS 567 | Bayesian Statistics | 1 |
| BIS 568 | Applied Machine Learning in Healthcare | 1 |
| BIS 620 | Data Science Software Systems | 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 1 | 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 |
| BIS 692 | Statistical Methods in Computational Biology | 1 |
| CDE 566 | Causal Inference Methods in Public Health Research | 1 |
| CDE 634 | Advanced Applied Analytic Methods in Epidemiology and Public Health | 1 |
| EMD 553 | Transmission Dynamic Models for Understanding Infectious Diseases | 1 |
| ENAS 912 | Biomedical Image Processing and Analysis | 1 |
| HPM 573 | Advanced Topics in Modeling Health Care Decisions | 1 |
| HPM 583 | Methods in Health Services Research | 1 |
| Additional electives must be approved by the Standard Pathway director. | ||
| 1 | Cannot fulfill elective credit if substituted for BIS 630. |
| Three electives in Statistics and Data Science. Suggested electives are: | ||
| CPSC 540 | Database Design and Implementation 1 | 1 |
| CPSC 546 | Data and Information Visualization 1 | 1 |
| CPSC 552 | Deep Learning Theory and Applications 1 | 1 |
| CPSC 570 | Artificial Intelligence 1 | 1 |
| CPSC 577 | Natural Language Processing 1 | 1 |
| CPSC 582 | Current Topics in Applied Machine Learning 1 | 1 |
| CPSC 583 | Deep Learning on Graph-Structured Data 1 | 1 |
| CPSC 640 | Topics in Numerical Computation 1 | 1 |
| CPSC 670 | Topics in Natural Language Processing 1 | 1 |
| CPSC 677 | Advanced Natural Language Processing 1 | 1 |
| CPSC 680 | Trustworthy Deep Learning 1 | 1 |
| CPSC 752 | Biomedical Data Science: Mining and Modeling 1 | 1 |
| INP 558 | Computational Methods in Human Neuroscience 1 | 1 |
| INP 599 | Statistics and Data Analysis in Neuroscience 1 | 1 |
| MGT 510 | Data Analysis and Causal Inference 3 | 2 |
| MGT 556 | Big Data & Customer Analytics 3 | 2 |
| MGT 803 | Decision Making with Data 3 | 2 |
| S&DS 517 | Applied Machine Learning and Causal Inference 1 | 1 |
| S&DS 530 | Data Exploration and Analysis 1 | 1 |
| S&DS 551 | Stochastic Processes 1 | 1 |
| S&DS 562 | Computational Tools for Data Science 1 | 1 |
| S&DS 563 | Multivariate Statistical Methods for the Social Sciences 1 | 1 |
| S&DS 565 | Introductory Machine Learning 1 | 1 |
| S&DS 569 | Numerical Linear Algebra: Deterministic and Randomized Algorithms 1 | 1 |
| S&DS 600 | Advanced Probability 1 | 1 |
| S&DS 610 | Statistical Inference 1 | 1 |
| S&DS 611 | Selected Topics in Statistical Decision Theory 1 | 1 |
| S&DS 612 | Linear Models 1,2 | 1 |
| S&DS 625 | Statistical Case Studies 1 | 1 |
| S&DS 631 | Optimization and Computation 1 | 1 |
| S&DS 632 | Advanced Optimization Techniques 1 | 1 |
| S&DS 661 | Data Analysis 1 | 1 |
| S&DS 662 | Statistical Computing 2 | 1 |
| S&DS 663 | Computational Mathematics Situational Awareness and Survival Skills 1 | 1 |
| S&DS 664 | Information Theory 1 | 1 |
| S&DS 665 | Intermediate Machine Learning 1 | 1 |
| S&DS 674 | Applied Spatial Statistics 1 | 1 |
| S&DS 685 | Theory of Reinforcement Learning 1 | 1 |
| Additional electives must be approved by the Standard Pathway director. | ||
| 1 | These courses are offered in the Graduate School of Arts and Sciences. |
| 2 | Cannot fulfill elective credit if substituted for BIS 623. |
| 3 | These courses are offered in the School of Management |
Students wishing to complete a thesis may enroll in BIS 649 and BIS 650, Master’s Thesis Research. This would be an additional requirement and cannot replace any of the required courses noted above. All students who complete a thesis will be required to present their research during a public seminar to the Biostatistics faculty and students in order to graduate.
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 |
| EMD 533 | Implementation Science | 1 |
| 1 | A master’s thesis is strongly recommended in place of BIS 681 and one elective. All students who complete a thesis will be 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 |
| 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 |
| SBS 676 | Questionnaire Development | 1 |
| S&DS 565 | Introductory Machine Learning 2 | 1 |
| Alternative electives must be approved by the Implementation Science Pathway director. | ||
| 1 | These courses are highly recommended. |
| 2 | This course is offered in the Graduate School of Arts and Sciences. |
Additional Required Courses: Data Science Pathway
| BIS 620 | Data Science Software Systems | 1 |
| BIS 687 | Data Science Capstone | 1 |
| Two of the following Biostatistics, Computer Science or Statistical Methods courses | ||
| 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 | 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 752 | Biomedical Data Science: Mining and Modeling 1 | 1 |
| CPSC 519 | Full Stack Web Programming 1 | 1 |
| CPSC 526 | Building Distributed Systems 1 | 1 |
| CPSC 539 | Software Engineering 1 | 1 |
| CPSC 565 | Theory of Distributed Systems 1 | 1 |
| CPSC 577 | Natural Language Processing 1 | 1 |
| CPSC 640 | Topics in Numerical Computation 1 | 1 |
| EMD 553 | Transmission Dynamic Models for Understanding Infectious Diseases | 1 |
| S&DS 541 | Probability Theory 4 | 1 |
| S&DS 551 | Stochastic Processes 5 | 1 |
| S&DS 611 | Selected Topics in Statistical Decision Theory 1 | 1 |
| S&DS 625 | Statistical Case Studies | 1 |
| S&DS 661 | Data Analysis 1 | 1 |
| S&DS 664 | Information Theory | 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 2 | 1 |
| BIS 568 | Applied Machine Learning in Healthcare | 1 |
| BIS 634 | Computational Methods for Informatics 2 | 1 |
| BIS 691 | Theory of Generalized Linear Models | 1 |
| CB&B 555 | Unsupervised Learning for Big Data 1 | 1 |
| CB&B 567 | Topics in Deep Learning: Methods and Biomedical Applications 1 | 1 |
| CB&B 663 | Deep Learning Theory and Applications 1 | 1 |
| CB&B 745 | Advanced Topics in Machine Learning and Data Mining 1 | 1 |
| CPSC 569 | Randomized Algorithms 1 | 1 |
| CPSC 583 | Deep Learning on Graph-Structured Data | 1 |
| CPSC 644 | Geometric and Topological Methods in Machine Learning | 1 |
| CPSC 670 | Topics in Natural Language Processing 1 | 1 |
| S&DS 517 | Applied Machine Learning and Causal Inference 1 | 1 |
| S&DS 538 | Probability and Statistics | 1 |
| S&DS 562 | Computational Tools for Data Science 1 | 1 |
| S&DS 565 | Introductory Machine Learning 1 | 1 |
| S&DS 569 | Numerical Linear Algebra: Deterministic and Randomized Algorithms | 1 |
| S&DS 631 | Optimization and Computation 1 | 1 |
| S&DS 632 | Advanced Optimization Techniques 1 | 1 |
| S&DS 665 | Intermediate Machine Learning 1 | 1 |
| S&DS 674 | Applied Spatial Statistics | 1 |
| S&DS 684 | Statistical Inference on Graphs | 1 |
| S&DS 685 | Theory of Reinforcement Learning | 1 |
| S&DS 686 | High-Dimensional Phenomena in Statistics and Learning | 1 |
| Additional electives must be approved by the Data Science Pathway director. | ||
| One of the following Database courses: | ||
| BIS 550 | Topics in Biomedical Informatics and Data Science 2 | 1 |
| BIS 638 | Clinical Database Management Systems and Ontologies | 1 |
| BIS 679 | Advanced Statistical Programming in SAS and R | 1 |
| CPSC 537 | Introduction to Database Systems 1 | 1 |
| MGT 660 | Advanced Management of Software Development 3 | 4 |
| Additional electives must be approved by the Data Science Pathway director. | ||
| 1 | These courses are offered in the Graduate School of Arts and Sciences |
| 2 | These courses can only be counted to fulfill the requirement of one category; they cannot be counted twice |
| 3 | These courses are offered at the School of Management |
| 4 | Cannot fulfill elective if taken as a requirement |
| 5 | Cannot fulfill elective if taken as a substitute for S&DS 541 |
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.
Students wishing to complete a thesis may enroll in BIS 649 and BIS 650, Master’s Thesis Research. This would be an additional requirement and cannot replace any of the required courses noted above. All students who complete a thesis will be required to present their research during a public seminar organized by the Biostatistics department.
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.