With the rise of data science and machine learning, and the emergence of digital health information, a wave of companies and researchers have sought to answer new and interesting questions throughout the healthcare domain. Yet, as health data presents inherently fragmented, noisy, high-dimensional, and heterogeneous attributes, it has become clear that computational research will require a specific set of techniques surrounding the preparation, modeling, and interpretation of its data.

This course will provide students an overview to many of the concepts, techniques, and theories associated with analytics in the healthcare domain. It will highlight some of the major challenges that arise from the complex nature of health data and present a set of statistical and machine learning techniques specifically geared towards answering a wide range of health-centric research questions. The course will have a strong focus on data drawn electronic medical records, providing students hands on experience with a large real-world dataset.

Course Syllabus

Instructor:

Keith Feldman


Lectures/Location:

M/W/F 10:30–11:20am
DeBartolo Hall 125


Office Hours: 308 Cushing

Wednesday 1 PM to 3 PM
Thursday 10 AM to 12 PM


Announcements
  • Please Complete your CIF's.
  • Assignment 4 Grades are up.
  • Assignment 3 Grades are up.
  • Assignment 3 released.
  • Assignment 2 Grades are up.
  • Assignment 2 released.
  • Assignment 1 Grades are up.
  • Assignment 1 released.
  • Welcome! Class begins, Aug. 22