Data Science Pathway

The Biostatistics data science pathway combines rigorous statistical training with the development of advanced computational skills to solve the public health challenges of tomorrow. Required courses cover epidemiology, regression models, databases, machine learning and more. Students will become familiar with data science programming tools (e.g. R, Python, SQL and NoSQL databases). Data science pathway graduates can find careers analyzing large volumes of health data in government (e.g. public health agencies), hospitals, industry (e.g. pharmaceutical companies) or research.

Students pursuing this pathway will graduate with the key skills of any Biostatistician. Unlike the traditional pathway, data science pathway students will have more experience using computational techniques to store, manipulate and analyze large volumes and varieties of data. This pathway trains biostatisticians; as such, it emphasizes the development and application of rigorous statistical theory to extensive health data sets, as opposed to application of the latest computational techniques that are prioritized in the health informatics masters. The focus on health applications differentiates this pathway from the MS in Data Science and Statistics.

Students must choose this pathway at the start of their two-year program.

Requirements - Data Science Pathway

2022-23 Matriculation

The M.S. degree requires a total of 16 course units. The M.S in Biostatistics requires the student to complete or acquire an exemption from the following courses. Full time students must carry a minimum of 4 course units each semester. Course substitutions (other than those listed) must be approved by the academic advisor, the Data Science Pathway Director and the DGS

MS Required Courses (10 course units)

BIS 525 Seminar in Biostatistics and Journal Club - 0 units
BIS 526 Seminar in Biostatistics and Journal Club - 0 units
BIS 620 Data Science Software Systems - 1 unit
BIS 623 Advanced Regression Models [or S&DS 612 Linear Models] - 1 unit
BIS 628 Longitudinal and Multilevel Data Analysis - 1 unit
BIS 630 Applied Survival Analysis [or BIS 643 Theory of Survival Analysis] - 1 unit
BIS 678 Statistical Practice – Capstone Experience - 1 Unit
BIS 687 Data Science Statistical Practice- Capstone Experience - 1 unit
EPH 509 Fundamentals of Epidemiology - 1 unit
EPH 608 Frontiers of Public Health * - 1 unit
EPH 600 Research Ethics and Responsibilities - 0 units
S&DS 541 Probability Theory [or S&DS 600 Advanced Probability or S&DS 551 Stochastic Process] - see note - 1 unit
S&DS 542 Theory of Statistics [or S&DS 610 Statistical Inference] - 1 unit
BIS 695 Summer Internship in Biostatistical Research - 0 units
EPH 100/101 Professional Skills Series - 0 units

MS Electives in Biostatistics (minimum 2 course units)

BIS 536 Measurement Error and Missing Data - 1 unit
BIS 550/CB&B 750 Topics in Biomed Informatics and Data Science - 1 unit
BIS 555 Machine Learning and Biomedical Data** - 1 unit
BIS 557 Computational Statistics** - 1 unit
BIS 634 Computational Methods for Informatics **- 1 unit
BIS 645 Statistical Methods in Human Genetics - 1 unit
BIS 646 Nonparametric Statistical Methods and their Applications** - 1 unit
S&DS 611 Selected Topics in Statistical Decision Theory - 1 unit
S&DS 661 Data Analysis - 1 unit
CPSC 539 Software Engineering - 1 unit
CPSC 577 Natural Language Processing - 1 unit
CB&B 752 Biomedical Data Science: Mining and Modeling - 1 unit

Electives in Machine Learning (1 course unit)

Take one or more of the following (if not taken from list above):

BIS 555 Machine Learning and Biomedical Data - 1 unit
BIS 557 Computational Statistics** - 1 unit
BIS 568 Applied Machine Learning in Healthcare - 1 unit
BIS 634 Computational Methods for Informatics** - 1 unit
BIS 646 Nonparametric Statistical Methods and their Applications** - 1 unit
S&DS 517 Applied Machine Learning and Casual Inference - 1 unit
S&DS 565 Introductory Machine Learning - 1 unit
S&DS 631 Optimization and Computation – 1 unit
S&DS 632 Advanced Optimization Techniques - 1 unit
S&DS 665 Intermediate Machine Learning - 1 unit
CB&B 555 Unsupervised Learning for Big Data - 1 unit
CB&B 567 Topics in Deep Learning: Methods & Biomedical Applications - 1 unit
CB&B 663 Deep Learning Theory and Applications - 1 unit
CB&B 745 Advanced Topics in Machine Learning - 1 unit
CPSC 552 Deep Learning Theory and Applications - 1 unit

Electives in Databases (1 course unit)

Take one or more of the following:

BIS 550/CB&B 750 Topics in Biomed Informatics and Data Science** - 1 unit
BIS 638 Clinical Database Management Systems and Ontologies - 1 unit
BIS 679 Advanced Statistical Programming in SAS & R - 1 unit
CPSC 537 Introduction to Database Systems - 1 unit
MGT 660 Advanced Management of Software Development - 1 unit

Electives (2 course units)

Take two additional course units from either the machine learning list, the databases list, or in BIS, CB&B or S&DS. Other courses from YSPH, CPSC, or another department may be acceptable if given permission from the Data Science Pathway Director.

Other Courses

BIS 649/BIS 650 Master’s Thesis Research - 2 units
Students choosing this option must present their research in a public seminar to graduate

Students should take 4 courses for credit each semester (BIS 525/526, EPH 600, EPH 100/101 are not for credit). Course schedules with more than 5 courses for credit will not be approved. Courses listed without a notation in the “term taken” column can be taken in either year of the program if prerequisites are met and with advisor approval.

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

** These courses can only be counted to fulfill the requirement of one category (they cannot be counted twice in fulfillment of requirements)

Rev. 09.08.22