Requirements - Biostatistics Standard Pathway

The MS Biostatistics Standard Pathway degree requires a total of 14-course units from the curriculum below (Public Health Primer, BIS 525/526 and PUBH 100/101 are not for credit). Course substitutions must be approved by the student 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 (9 course units)

• Public Health Primer – 0 units
  
• BIS 525 Seminar in Biostatistics and Journal Club - 0 units
  
• BIS 526 Seminar in Biostatistics and Journal Club - 0 units
  
• BIS 623 Advanced Regression Models [or S&DS 6120 Linear Models] *BIS 623 is a 1st year class only; BIS 623 is a prerequisite for BIS 630
  
• BIS 628 Longitudinal and Multilevel Data Analysis - 1unit
  
• BIS 630 Applied Survival Analysis [or BIS 643 Theory of Survival Analysis] *BIS 630 is a 1st year class only; BIS 623 is a prerequisite for BIS 630
  
• BIS 678 Statistical Practice I- Capstone Experience *2nd year class only
  
• BIS 679 Advanced Statistical Programming in SAS and R
  
• BIS 681 Statistical Practice II- Capstone Experience *2nd year class only
  
• PUBH 508 Foundations of Epidemiology and Public Health
  
• S&DS 5410 Probability Theory [or S&DS 6000 Advanced Probability or S&DS 5510 Stochastic Process]
  
• S&DS 5420 Theory of Statistics [or S&DS 6100 Statistical Inference]
  
• BIS 695 Summer Internship in Biostatistical Research - 0 units
  
• PUBH 100 (Fall); PUBH 101 (Spring) Professional Skills Series - 0 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 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 691 Theory of Generalized Linear Models
• BIS 692 Statistical Methods in Computational Biology

• BENG 5450 Biomedical Image Processing Analysis
  
• CDE 566 Causal Inference Methods in Public Health Research
  
• CDE 634 Advanced Applied Analytic Methods in Epidemiology and Public Health
  
• CPSC 5371 Database Design and Implementation
  
• CPSC 5460 Data and Information Visualization
  
• CPSC 5520/CB&B 6663 Deep Learning Theory and Applications
  
• CPSC 5700 Artificial Intelligence
  
• CPSC 5710 Trustworthy Deep Learning
  
• CPSC 5770 Natural Language Processing
  
• CPSC 5820 Current Topics in Applied Machine Learning
  
• CPSC 5830 Deep Learning on Graph-Structured Data
  
• CPSC 6400 Topics in Numerical Computation
  
• CPSC 6700 Topics in Natural Language Processing
  
• CPSC 7520/ CB&B 7520/ MB&B 7520, 7503,7530/MCDB 7520 Biomedical Data Science: Mining and Modeling
  
• CPSC 7760 Topics in Industrial AI Applications
  
• ECON 5540 Econometrics V
  
• EMD 553 Transmission Dynamic Modeling of Infectious Diseases
  
• HPM 583 Methods in Health Services Research
  
• INP 7599 Statistics and Data Analysis in Neuroscience
  
• MGT 803 Decision Making with Data
  
• PSYC 5580 Computational Methods in Human Neuroscience
  
• S&DS 5170 Applied Machine Learning and Causal Inference
  
• S&DS 5510 Stochastic Processes
  
• S&DS 5620 Computational Tools for Data Science
  
• S&DS 5630 Multivariate Statistical Methods for the Social Sciences
  
• S&DS 5650 Introduction to Machine Learning
  
• S&DS 5650 Introduction to Machine Learning
  
• S&DS 5660 Deep Learning for Scientists and Engineers
  
• S&DS 5690 Numerical Linear Algebra: Deterministic and Randomized Algorithms
  
• S&DS 5800 Neural Data Analysis to the Additional Electives list
  
• S&DS 6000 Advanced Probability
  
• S&DS 6100 Statistical Inference
  
• S&DS 6110 Selected Topics in Statistical Decision Theory
  
• S&DS 6120 Linear Models
  
• S&DS 6180 Asymptotic Statistics
  
• S&DS 6310 Optimization and Computation
  
• S&DS 6320 Advanced Optimization Techniques
  
• S&DS 6610 Data Analysis
  
• S&DS 6620 Statistical Computing
  
• S&DS 6630 Computational Mathematics Situational Awareness and Survival Skills
  
• S&DS 6640 Information Theory
  
• S&DS 6650 Intermediate Machine Learning
  
• S&DS 6740/ ENV 781 Applied Spatial Statistics
  
• S&DS 6850 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 5370 (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.