CNE 151

CNE 151: Data Science Methods for Clinicians

January 16 - May 12, 2018

Course Description

This is an overview course with a focus on understanding new data analytic methods and application in health care. It does not require expertise in mathematics or statistics. The course will cover methods for reuse of big data in the context of health care for quality improvement and research, from an interprofessional team perspective

Course Prerequisite

Health care professional with at least a bachelor’s degree or equivalent.

Course Content






Introduction to Big Data Science in Health Care

Connie White Delaney

1/16/18 - 1/12/18


Team Science Approach

Connie White Delaney

1/22/18 - 1/28/18


Data: Types, Quality and Preprocessing

Lisiane Pruinelli

2/5/18 - 2/11/18


Patient Generated Health Data, Omic Sciences and Big Data

Robin Austin

2/19/18 - 2/25/18


Cluster and Discriminate Pattern Analysis

Bonnie Westra

2/26/18 - 3/4/18


Decision Trees

Bonnie Westra

3/5/18 - 3/11/18


Visualization: The Power of Pictures

Karen Monsen

3/19/18 - 3/25/18


Artificial Intelligence

Martin Michalowski

4/2/18 - 4/8/18


Mapping and Geographic Information Systems

Madeleine Kerr

4/16/18 - 4/22/18


Predictive Modeling for Quality Optimization

Chih-Lin Chi

4/30/18 - 5/6/18

Target Audience

Health care professionals interested in the quality improvement and research of health care through the reuse of big data.


Jointly Accredited Provider LogoIn support of improving patient care, the University of Minnesota, Interprofessional Continuing Education is jointly-accredited by the Accreditation Council for Medical Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team.

This course has been awarded 60.0 ANCC contact hours.

It is the responsibility of each participant to determine if the program meets the criteria for licensure and/or recertification for their discipline. Please check with your accrediting organization for complete requirements.


Registration is closed. Complete this form to be notified about future offerings.

Cancellation Policy: A course registration may be canceled by emailing the School of Nursing Office of Continuing Professional Development at No refunds will be granted once the Moodle course page has been accessed.

Course Information

Learning Objectives & Syllabus

Learning Objectives & Syllabus

Course Objectives
Upon completion of the course, participants should be able to:

  • Describe methods for use of large clinical datasets.
  • Compare applicability of methods for diverse clinical problems.
  • Analyze application of methods in the context of clinical settings.