HS 853 001 FA 2016

HS 853 001 FA 2016

An advanced course on probability reasoning, statistical inference, data science, and predictive analytics.

Prerequisites

HS 852, or equivalent, instructor may review syllabi of previously taken courses (past 5 years) and/or require a test to assess the equivalence of the student background, as necessary.

Class Schedule

See UMSN Courses and UMich Office of the Registrar.
Mondays, Wednesdays: Time/Place: SNB 1250, 8:30-10:30 AM

Course Description

HS 853 will cover a number of modern analytical methods for advanced healthcare research. Specific focus will be on reviewing and using innovative modeling, computational, analytic and visualization techniques to address concrete driving biomedical and healthcare applications. The course will cover the 5 dimensions of Big-Data (volume, complexity, time/scale, source and management). HS853 is a 4 credit hour course (3 lectures + 1 lab/discussion).

Objectives

Students will learn how to:
  • Research, employ and report on recent advanced health sciences analytical methods
  • Read, comprehend and present recent reports of innovative scientific methods
  • applicable to a broad range of health problems
  • Experiment with real Big-Data

Examples of Topics Covered

  • Scientific Visualization
  • PCOR/CER methods Heterogeneity of Treatment Effects
  • Big-Data / Big-Science
  • Missing data
  • Genotype-Environment-Phenotype associations
  • Medical imaging
  • Data Networks
  • Adaptive Clinical Trials
  • Databases/registries
  • Meta-analyses
  • Causality/Causal Inference, SEM
  • Classification methods
  • Time-series analysis
  • Scientific Validation
  • Geographic Information Systems (GIS)
  • Rasch measurement model/analysis
  • MCMC sampling for Bayesian inference
  • Network Analysis

Teaching and Learning Methods

This course meets four times on campus and will use blended instructional techniques to deliver learning materials, provide instructional resources and assess student progress. Synchronous web-streaming of lectures/labs and asynchronous virtual office hour forums will be supported. Assignments will be announced on the web and will be electronically collected, graded and recorded. A variety of teaching methods will be used including lecture, Journal Club, discussion, small group work, and guest presentation.

Textbooks

SMHS EBook and additional resources will be made available through the SOCR Wiki and may include chapters, websites for review, references, reports posted online, ebooks and learning modules.

Software and Computational Tools

We will only use open-source software, libraries and tools including the web-based SOCR tools (which require Java and HTML5/JavaScript enabled web-browsers) and the Statistical computing Software "R" (which you need to download and install the graphical user interface (GUI), RStudio).

Assignments and Evaluation Methods

  • 40% Homework Projects
  • 30% Midterm Exam
  • 30% Final Paper

Standard letter-grading distribution will be used:

  • A: 90%+
  • B: 80-90%
  • C: 70-80%
  • D: 60-70%
  • ...
  • Plus and minus grads will also be used (e.g., "B-": 80-83%; "B+": 87-90%)

Grading Policy

The lowest graded Homework assignment will be dropped. All Homework assignments must be completed by the corresponding deadline, however. No late assignments will be accepted. For students with genuine documented reasons for missing the midterm arrangements will be made. If after receiving the graded exams or HW/projects back you believe a grading error has occurred please see (INSTRUCTOR) or your TA, within one week. Late regrade requests may not be accommodated. Reading assignments will be given. You will be responsible for the information covered in these assignments. Lecture and discussion attendance will be recorded from time to time.

Office Hours

  • Instructor: Ivo Dinov
  • Office: SNB 4126
  • Time: Thursdays: 9:30 AM

Course Schedule

Course Summary:

Date Details
CC Attribution Non-Commercial Share Alike This course content is offered under a CC Attribution Non-Commercial Share Alike license. Content in this course can be considered under this license unless otherwise noted.