Big Ten Academic Alliance Nursing Informatics Collaborative Webinar Series

Big Ten Academic Alliance Nursing Informatics Collaborative Webinar Series

Free Online Course in Nursing Informatics

The Big Ten Academic Alliance Nursing Informatics Collaborative also offers a free course for faculty who are new to nursing informatics and who are interested in a broad overview of the discipline.

The course integrates various nursing informatics standards into the curriculum and provides examples of methods, tools, and learning resources to teach them. Subjects include:

  • knowledge complexity
  • informatics literacy
  • nursing knowledge work
  • data standards and standardized languages
  • clinical decision support
  • future trends.

The course is a collaborative effort between the University of Minnesota, the Gordon and Betty Moore Foundation, and the American Association of Colleges of Nursing.

Take the Course

Webinar No. 4:
Mining Health Data for Prediction and Pattern Identification

Date and Time:
Monday, March 12, noon-1 p.m. (EST)

Matthew A. Davis, MPH, PhD
University of Michigan School of Nursing


This free lunchtime webinar is the final in a series of four.

There is considerable interest among researchers in the application of new methodologies to mine data for prediction and pattern identification. However, there is often a communication disconnect between health content experts and those who possess the technical knowledge of data mining methods.  The purpose of this seminar is to provide a conceptual overview of popular data mining techniques. In doing so, example applications of the techniques covered to health research will be presented.

Nurses attending may receive a certificate awarding 1 Continuing Education (CE) credit. The Big Ten Academic Alliance’s Nursing Informatics Collaborative is supporting the cost of these webinars and the CE.

More Information:

Register Now


Following the seminar attendees will be able to:

  1. Distinguish between large and big data
  2. Define and differentiate between supervised and unsupervised learning algorithms
  3. Describe how several data mining methods work, including tree-based classification, support vector machines, clustering algorithms, and dimensionality reduction
  4. Provide examples of the application of data mining methods in order to answer health-related research questions.

Webinar Instructions

This webinar series is delivered using WebEx online meeting service.

  • A link to the WebEx and corresponding call-in number will be sent to the email address you provide when registering.
  • Please review the WebEx Participant Guide in advance of the webinar.
  • You will also receive an email reminder with the meeting link 10 minutes prior to the webinar.
  • Please log in 5-10 minutes early to test your audio connections.

Continuing Education (CE) Credit

Nurses who attend the entire webinar and complete the online webinar evaluation may request and receive a certificate awarding 1 CE. Partial CE is not provided. Request for CE with a completed evaluation must be submitted within 60 days of the event.

The University of Maryland School of Nursing is accredited as a provider of continuing nursing education by the American Nurses Credentialing Center’s Commission on Accreditation.


Planning committee members and faculty involved in this continuing education activity did not report any conflict of interest or financial relationship with a commercial interest that would bias the content of this presentation. This educational activity has not received any form of commercial support.

Explore Past Webinars