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 101Note: 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 525Seminar in Biostatistics and Journal Club 10
BIS 526Seminar in Biostatistics and Journal Club 10
BIS 623Advanced Regression Models1
or S&DS 6120 Linear Models
BIS 628Longitudinal and Multilevel Data Analysis1
BIS 630Applied Survival Analysis1
or BIS 643 Theory of Survival Analysis
BIS 678Statistical Practice I1
BIS 695Summer Internship in Biostatistics 10
PUBH 100Professional Skills Series 10
PUBH 101Professional Skills Series 10
PUBH 508Foundations of Epidemiology and Public Health1
S&DS 5410Probability Theory 21
or S&DS 5510 Stochastic Processes
or S&DS 6000 Advanced Probability
S&DS 5420Theory of Statistics 21
or S&DS 6100 Statistical Inference

Additional Required Courses: Standard Pathway

BIS 679Advanced Statistical Programming in SAS and R1
BIS 681Statistical Practice II 11
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 536Measurement Error and Missing Data1
BIS 537Statistical Methods for Causal Inference1
BIS 540Fundamentals of Clinical Trials1
BIS 550/CB&B 7500Topics in Biomedical Informatics and Data Science1
BIS 555Machine Learning with Biomedical Data1
BIS 560Introduction to Clinical and Translational Informatics1
BIS 567Bayesian Statistics1
BIS 568Applied Artificial Intelligence in Healthcare1
BIS 629Advanced Methods for Implementation and Prevention Science1
BIS 631Advanced Topics in Causal Inference Methods1
BIS 633Population and Public Health Informatics1
BIS 634Computational Methods for Informatics1
BIS 638Clinical Database Management Systems and Ontologies1
BIS 640User-Centered Design of Digital Health Tools1
BIS 643Theory of Survival Analysis 21
BIS 645Statistical Methods in Human Genetics1
BIS 646Nonparametric Statistical Methods and Their Applications1
BIS 691Theory of Generalized Linear Models1
Additional electives must be approved by the Standard Pathway director.
A minimum of three electives must be taken from either the Biostatistics electives list or the list below:
BENG 5450Biomedical Image Processing and Analysis1
CDE 566Causal Inference Methods in Public Health Research1
CDE 634Advanced Applied Analytic Methods in Epidemiology and Public Health1
CPSC 5371Database Design and Implementation 11
CPSC 5460Data and Information Visualization 11
CPSC 5520/CB&B 6663/AMTH 5520Deep Learning Theory and Applications 11
CPSC 5700Artificial Intelligence 11
CPSC 5710Trustworthy Deep Learning1
CPSC 5770Natural Language Processing 11
CPSC 5820Current Topics in Applied Machine Learning 11
CPSC 5830Deep Learning on Graph-Structured Data 11
CPSC 6400Topics in Numerical Computation 11
CPSC 6700Topics in Natural Language Processing 11
CPSC/CB&B/MB&B/MCDB 7520Biomedical Data Science: Mining and Modeling 11
CPSC 7760Topics in Industrial AI Applications1
ECON 5554Econometrics V 11
EMD 553Transmission Dynamic Models for Understanding Infectious Diseases1
HPM 583Methods in Health Services Research1
INP 7599Statistics and Data Analysis in Neuroscience 11
MGT 803Decision Making with Data 22
PSYC 5580Computational Methods in Human Neuroscience1
S&DS 5170Applied Machine Learning and Causal Inference 11
S&DS 5510Stochastic Processes 1,41
S&DS 5620Computational Tools for Data Science 11
S&DS 5630/ENV 758Multivariate Statistical Methods for the Social Sciences 11
S&DS 5650Introductory Machine Learning 11
S&DS 5660Deep Learning for Scientists and Engineers1
S&DS 5690Numerical Linear Algebra: Deterministic and Randomized Algorithms 11
S&DS 5800Neural Data Analysis 11
S&DS 6000Advanced Probability 1,41
S&DS 6100Statistical Inference 1,51
S&DS 6110Selected Topics in Statistical Decision Theory 11
S&DS 6120Linear Models 1,31
S&DS 6180Asymptotic Statistics 11
S&DS 6310Optimization and Computation 11
S&DS 6320Advanced Optimization Techniques 11
S&DS 6610Data Analysis 11
S&DS 6620Statistical Computing1
S&DS 6630Computational Mathematics Situational Awareness and Survival Skills 11
S&DS 6640Information Theory 11
S&DS 6650Intermediate Machine Learning 11
S&DS 6740/ENV 781Applied Spatial Statistics 11
S&DS 6850Theory of Reinforcement Learning 11
Additional electives must be approved by the Standard Pathway director.

Additional Required Courses: Implementation and Prevention Science Methods Pathway

BIS 629Advanced Methods for Implementation and Prevention Science1
BIS 679Advanced Statistical Programming in SAS and R1
BIS 681Statistical Practice II 11
or BIS 649 Master’s Thesis Research
or BIS 650 Master’s Thesis Research
EMD 533Implementation Science1
At least one of the following:
BIS 536Measurement Error and Missing Data1
BIS 537Statistical Methods for Causal Inference1
BIS 631Advanced Topics in Causal Inference Methods1
At least two of the following:
CDE 516Principles of Epidemiology II1
CDE 534Applied Analytic Methods in Epidemiology1
EMD 538Quantitative Methods for Infectious Disease Epidemiology1
HPM 570Cost-Effectiveness Analysis and Decision-Making 11
HPM 575Evaluation of Global Health Policies and Programs1
HPM 586Microeconomics for Health Policy and Health Management1
HPM 587Advanced Health Economics1
MGT 611Policy Modeling2
SBS 541Community Health Program Evaluation 11
SBS 574Developing a Health Promotion and Disease Prevention Intervention1
SBS 580Qualitative Research Methods in Public Health 11
S&DS 5650Introductory Machine Learning 21
Alternative electives must be approved by the Implementation Science Pathway director.

Additional Required Courses: Data Science Pathway

BIS 678Statistical Practice I1
BIS 681Statistical Practice II 11
or BIS 649 Master’s Thesis Research
or BIS 650 Master’s Thesis Research
Two of the following biostatistics, computer science, and statistical methods courses
BENG 5440Fundamentals of Medical Imaging1
BIS 536Measurement Error and Missing Data1
BIS 537Statistical Methods for Causal Inference1
BIS 540Fundamentals of Clinical Trials1
BIS 550/CB&B 7500Topics in Biomedical Informatics and Data Science1
BIS 555Machine Learning with Biomedical Data1
BIS 567Bayesian Statistics1
BIS 629Advanced Methods for Implementation and Prevention Science1
BIS 634Computational Methods for Informatics1
BIS 645Statistical Methods in Human Genetics1
BIS 646Nonparametric Statistical Methods and Their Applications1
CB&B 5620Modeling Biological Systems II 11
CB&B 7520Biomedical Data Science: Mining and Modeling 11
CPSC 5150Law and Large Language Models1
CPSC 5190Full Stack Web Programming 11
CPSC 5260Building Distributed Systems 11
CPSC 5390Software Engineering 11
CPSC 5650Theory of Distributed Systems 11
CPSC 5770Natural Language Processing 11
CPSC 5880AI Foundation Models 11
CPSC 6400Topics in Numerical Computation 11
CPSC 6420Modern Challenges in Statistics: A Computational Perspective 11
EMD 553Transmission Dynamic Models for Understanding Infectious Diseases1
MCDB 5000Biochemistry1
S&DS 5410Probability Theory 1,21
S&DS 5510Stochastic Processes 1,3,41
S&DS 5660Deep Learning for Scientists and Engineers1
S&DS 6110Selected Topics in Statistical Decision Theory 11
S&DS 6450Statistical Methods in Computational Biology1
S&DS 6610Data Analysis 11
S&DS 6640Information Theory 11
Additional electives must be approved by the Data Science Pathway director.
One of the following Machine Learning courses:
BIS 555Machine Learning with Biomedical Data 31
BIS 568Applied Artificial Intelligence in Healthcare1
BIS 634Computational Methods for Informatics 31
BIS 691Theory of Generalized Linear Models1
CB&B 5555/AMTH 5530/CPSC 5530Unsupervised Learning for Big Data 11
CB&B 6663/AMTH 5520/CPSC 5520Deep Learning Theory and Applications 11
CPSC 5690Randomized Algorithms 11
CPSC 5710Trustworthy Deep Learning1
CPSC 5830Deep Learning on Graph-Structured Data 11
CPSC 6440Geometric and Topological Methods in Machine Learning 11
CPSC 6700Topics in Natural Language Processing 11
S&DS 5170Applied Machine Learning and Causal Inference 11
S&DS 5620Computational Tools for Data Science 11
S&DS 5650Introductory Machine Learning 11
S&DS 5690Numerical Linear Algebra: Deterministic and Randomized Algorithms 11
S&DS 6310Optimization and Computation 11
S&DS 6320Advanced Optimization Techniques 11
S&DS 6650Intermediate Machine Learning 11
S&DS 6740/ENV 781Applied Spatial Statistics 11
S&DS 6840Statistical Inference on Graphs 11
S&DS 6850Theory of Reinforcement Learning 11
S&DS 6860High-Dimensional Phenomena in Statistics and Learning 11
Additional electives must be approved by the Data Science Pathway director.
One of the following Database courses:
BIS 550/CB&B 7500Topics in Biomedical Informatics and Data Science 31
BIS 638Clinical Database Management Systems and Ontologies1
BIS 679Advanced Statistical Programming in SAS and R1
CPSC 5370Database Systems 11
MGT 656Management of Software Development 54
MGT 660Advanced Management of Software Development 54
Additional electives must be approved by the Data Science Pathway director.

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