Curriculum

The CAS-BDS draws upon a range of courses and opportunities located throughout the University of Rochester, exposing students to a range of perspectives, nomenclatures, analytic approaches, and scientific cultures. Students enroll in the one year program starting with a Summer Session and complete the CAS-BDS in one calendar year with a mentored team science project. 

The Curriculum is fixed, which provides students with the fundamentals of biomedical data science, combined with the mentored project, non-credit offerings in programming and analytic languages, and training in Team Science.

Mentored Project

Faculty offer pre-approved two-semester Big Data projects into which CAS-BDS students enroll at intake:

Sample Organizing Projects

Clinical

Work with a clinical researcher to analyze data from the Medical Center’s laboratory information system linked with electronic medical records to identify association of 25-hydroxyvitamin D levels with clinical presentation of Type 2 diabetes.

Health Services

Work with hospital quality improvement staff to analyze hospital electronic medical record data to identify clinical, demographic, and care determinants of hospital re-admission within 90 days for patients discharged with Chronic Obstructive Pulmonary Disease (COPD).

Population Health

Work with a health economist to analyze regional and national Medicare claims data to compare costs and complications of hospital-based and home-based dialysis in the elderly.

Students in each project group (n=5 to 8) will meet for a Project Orientation at the beginning of the program and will participate in a weekly one hour project workshop with their mentor to sequentially develop the project and to apply their classroom learnings at each stage to the project’s implementation. Each project will include attention to: Specific Aim formulation, Background, Analytic plan development, Database creation and data analysis, and Reporting. Each Mentored Project culminates with a Project Presentation at the end of the one-year program.

A formal competition will solicit project team request proposals from faculty, reviewed and selected for program inclusion by the BDS Certificate Oversight Committee.

Coursework

The coursework required provides students with fundamentals of disease biology, health care systems, and big data management and analytics.

The CAS-BDS is a summer plus two-semester sequence of courses, plus completion of the organizing Practicum Project and seminar in Team Science. Students with prior training in medical sciences or clinical care can opt out, after committee review, of the Human Biology in Health Research requirement, offered in the summer.

Courses

Summer I

Fall I

Spring I

Summer Session I

Coursework begins with a Summer Session that includes foundation courses in Quantitative Methods, Data Structures, Human Biology, Team Science, and in programming languages.  Students are required to take all courses, and to enroll in at least one non-credit programming seminar (each of which includes 4.5 hours of classroom instruction). Students with a medical or clinical background can apply for a waiver of Human Biology. The Team Science seminar is a weekly one hour session with all program participants to meet with program leadership to discuss Team Science principles, review related papers, and to discuss project options.

Purpose: Assure basic capacity in data science, biostatistics, and data management; orientation to organizing project.

Non-matriculated students need to secure the permission of instructors in order to register for courses. Class sizes may be limited. For more information about the courses listed below, please contact the instructor or Pattie Kolomic, Graduate Programs Administrator, 275-7882, pattie_kolomic@urmc.rochester.edu

PM 401 Quantitative Methods in Public Health Res.  (CRN: 21437) 3 Credits

Instructor: Dongmei Li, Ph.D., Kathleen Holt, Ph.D.

The purpose of this course is to familiarize students with many of the standard statistical techniques utilized in the health sciences. By the end of the course, students should be able to understand, interpret, and communicate about statistical topics including but not limited to: descriptive statistics; displaying data in tables and figures; types of data and distributions; sampling distributions and hypothesis testing; comparing means; correlation and regression; and contingency tables and sensitivity/specificity. M/W, 4:15 pm-7:15pm, June 29-August 3, SRB 1.404

PM 402 Human Biology & Health Research, (CRN: 21458) 3 credits*

Instructor: Timothy Dye, Ph.D.

This course aims to introduce graduate students in health research disciplines to human biology, with a particular focus on systems, disease, treatment, and etiology. The course is oriented for students with little or no undergraduate training in human biology or a clinical field, and focuses upon broad concepts surround health and disease. Examples from published health research are used in the course to underscore the importance of human biology in addressing research questions in health services research, biomedical informatics, epidemiology, and public health. M/W, 1:10pm- 4:10pm June 29–Aug. 3, SRB 1.402

*Students with prior clinical, biology, or medical experience/ training may opt out.

PM 403 Research Team Science Seminar, (CRN: 21460) 1 credit

Instructors: Timothy Dye, Ph.D., Harriet Kitzman, Ph.D.

This course introduces graduate students to the concepts, practice, and challenges of Team Science and collaborative research environments. Students will be exposed both to team science (TS) initiatives and the science of team science (SciTS) as presented through practical examples from local research teams and researchers, focusing upon the practical implications of a team science approach to biomedical research requiring large-scale data analysis. M/W, 12 noon -1:00pm, June 29-August 3, SRB 1.406

CSC 162 The Art of Data Structures (CRN: 20980) Lab: (CRN: 21215)

Instructor: Ted Pawlicki, Ph.D.

Computers are universal tools to store and process information. The storage part is organized as data structures; the processing part is captured as algorithms. Together, these form the heart of every computer application (in science, government, business, and the arts), on every kind of information (pictures, numbers, sound, and text). We will study the most fundamental data structures and algorithms as a means of using computers more effectively, and as preparation for more advanced study in CS and related fields. Prerequisite: CSC 161 or equivalent. Lab required. T/R, 12 noon to 3:00PM, Meliora 224, Lab, T/R, 4:00-7:00PM, Goergen 102 

Fall, Session I

Purpose: Provide foundations in the U.S. health system, and big data analytics; apply learnings to organizing project.

• MIF400 Introduction to Medical Informatics (3 cr)
• CSC 440 Data Mining (4 cr)
• PM421 US Health Care System (3 cr)
• PMXXX Data Science Practicum (1 cr)

Spring, Session I

Purpose: Provide overview of analytic techniques in biomedical data science; apply learnings to organizing project.  

• PM422 Quality of Care and Risk Adjustment (3 cr)
• BST 467 Applied Statistics in the Biomedical Sciences (3 cr)
• PMXXX Data Science Practicum (1 cr)

Practicum Presentation

Students are required to present their practicum project in a poster/paper session organized at the end of the spring semester.