Requirements - Biostatistics Standard Pathway

The MS Biostatistics Standard Pathway degree requires a total of 15-course units from the curriculum below (BIS 525/526 and EPH 100/101 are not for credit). Course substitutions must be approved by the student’s advisor and the DGS. Electives not listed below must be approved by the BIS Standard Pathway Faculty Liaison.

Full-time students must carry a minimum of 4 course units each semester. Course schedules with more than 5 courses for credit will not be approved. If students have fewer than 4 required courses to take in their last term, it is acceptable to register for just the courses needed to fulfill the degree requirements.

All courses count as 1 credit unless otherwise noted.

MS Required Courses (10 course units)

*Students entering the program with an MPH or relevant graduate degree may be exempt from this requirement.

ELECTIVES: 5 courses are REQUIRED. A minimum of 2 must be from the Biostatistics list. The additional 3 electives can be taken from either list of approved electives below (Biostatistics or Additional electives)

MS Electives in Biostatistics (2 course units)

• BIS 536 Measurement Error and Missing Data
  
• BIS 537 Statistical Methods for Causal inference
• BIS 540 Fundamentals of Clinical Trials
• BIS 550/CB&B 750 Topics in Biomed Informatics and Data Science
• BIS 555 Machine Learning and Biomedical Data
• BIS 560 Introduction to Health Informatics
• BIS 567 Bayesian Statistics
• BIS 568 Applied Machine Learning in Healthcare
• BIS 620 Data Science Software Systems
• BIS 629 Advanced Methods for Implementation and Prevention Science
• BIS 631 Advanced Topics in Causal Inference
• BIS 633 Population and Public Health Informatics
• BIS 634 Computational Methods for Informatics
• BIS 638 Clinical Database Management Systems and Ontologies
• BIS 640 User-Centered Design of Digital Health Tools
• BIS 643 Theory of Survival Analysis (Cannot fulfill elective if substituted for BIS 630)
• BIS 645 Statistical Methods in Human Genetics
• BIS 646 Nonparametric Statistical Methods & their Applications
• BIS 662 Computational Statistics
• BIS 691 Theory of Generalized Linear Models
• BIS 692 Statistical Methods in Computational Biology

• CDE 566 Causal Inference Methods in Public Health Research
• CDE 634 Advanced Applied Analytic Methods in Epidemiology and Public Health
• CPSC 540 Advanced Databases
• CPSC 546 Data and Information Visualization
• CPSC 552/CB&B 663 Deep Learning Theory and Applications
• CPSC 570 Artificial Intelligence
• CPSC 577 Natural Language Processing
• CPSC 582 Current Topics in Applied Machine Learning
• CPSC 583 Deep Learning on Graph-Structured Data
• CPSC 640 Topics in Numerical Computation
• CPSC 670 Topics in Natural Language Processing
• CPSC 680 Trustworthy Deep Learning
• CPSC 752/ CB&B 752/ MB&B 752, 753,753/MCDB 752 Biomedical Data Science: Mining and Modeling
• ECON 554 Econometrics V
• EMD 553 Transmission Dynamic Modeling of Infectious Diseases
• ENAS 912 Biomedical Image Processing Analysis
• HPM 573 Advanced Topics in Modeling Health Care Decisions
• HPM 583 Methods in Health Services Research
• INP 558/PSYC 558 Computational Methods in Human Neuroscience
• INP 599 Statistics and Data Analysis in Neuroscience
• MGT 803 Decision Making with Data
• S&DS 517 Applied Machine Learning and Causal Inference
• S&DS 551 Stochastic Processes
• S&DS 562 Computational Tools for Data Science
• S&DS 563/ ENV 758 Multivariate Statistical Methods for the Social Sciences
• S&DS 565 Introduction to Machine Learning
• S&DS 569 Numerical Linear Algebra: Deterministic and Randomized Algorithms
• S&DS 580 Neural Data Analysis to the Additional Electives list
• S&DS 600 Advanced Probability
• S&DS 610 Statistical Inference
• S&DS 611 Selected Topics in Statistical Decision Theory
• S&DS 612 Linear Models
• S&DS 618 Asymptotic Statistics
• S&DS 631 Optimization and Computation
• S&DS 632 Advanced Optimization Techniques
• S&DS 661 Data Analysis
• S&DS 662 Statistical Computing
• S&DS 663 Computational Mathematics Situational Awareness and Survival Skills
• S&DS 664 Information Theory
• S&DS 665 Intermediate Machine Learning
• S&DS 674/ ENV 781 Applied Spatial Statistics
• S&DS 685 Theory of Reinforcement Learning

Other Courses

BIS 649/BIS 650 Master’s Thesis: If chosen, BIS 650 replaces BIS 678 in the spring of the 2nd year. Students doing a thesis must present their research in a public seminar to graduate.

Note: CPSC 537 (Introduction to Database Systems) is not approved as an elective because the material is too introductory and covers database theory rather than application. Students should instead take BIS 638 (Clinical Database Management Systems and Ontologies)

1. 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.
2. Design efficient computer programs for study management, statistical analysis, as well as presentation using R, SAS and other programming languages.
3. Demonstrate oral and written communication and presentation skills to effectively communicate and disseminate results to professional audiences.