CNE 151

CNE 151: Data Science Methods for Clinicians

January 22 - April 28, 2019

Course Description

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

Course Prerequisite

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

Course Content

Module Topic Lecturer Schedule
  Pre-quiz available/ Review Website/ Introductions Karen Monsen 1/22 - 1/27
1 Introduction to Big Data Science in Health Care Connie Delaney 
Sripriya Rajamani
1/28 – 2/3
2 Team Science Approach Connie Delaney 
Sripriya Rajamani
2/4 – 2/10
3 Data: Types, Quality and Preprocessing Lisiane Pruinelli 2/11 – 2/17
4 Patient Generated Health Data, Omic Sciences and Big Data Robin Austin 2/18 – 2/24
5 Predictive Modeling and Decision Trees Bonnie Westra 
Chih-Lin Chi
2/25 – 3/10
6 Cluster and Discriminant Pattern Analysis Bonnie Westra 3/11 - 3/17
  Spring break   3/18 – 3/24
7 Artificial Intelligence Martin Michalowski 3/25 – 3/31
8 Mapping and Geographic Information Systems Madeleine Kerr 4/1 - 4/7
9 Visualization: The Power of Pictures Karen Monsen 4/8 - 4/21
  Final Evaluation/ Post quiz is available 4/22   4/22 – 4/28

Target Audience

Healthcare professionals interested in the quality improvement and research of healthcare 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.


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.

Learning Activities

The learning involves students accessing content online each week at their convenience.  In addition, an instructor will generally be available each week on Friday at 7:30 CST to answer student questions or review content to reinforce learning.

Course syllabus