Health Data Analytics MOOC

Health data on a computer screen

Health Data Analytics

  • Registration is now closed. 
  • All coursework must be completed by December 7, 2013.
  • Each unit consists of a narrated PowerPoint presentation and recommended readings
  • The course is self-paced and is designed to be completed in eight weeks and represents the equivalent of 2 credits, however, because this course is free no credit will be granted.
  • Course provides an introduction to big data, databases, and data analytics in healthcare.
  • Recommended Textbook: The textbook is not required, but it is recommended.
    • Williams, Graham (2011). Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery. Springer. ISBN-10: 1441998896; ISBN-13: 978-1441998897
  • A certificate of completion is awarded to students who successfully complete the course.

Course Description

This course is designed to provide participants with a detailed description of data analytics in health care. Big data in health care has sparked a lot of interest in applying large-scale data analytical techniques for acquiring new information about existing data.  In order to become proficient in data analytics one must first understand the foundational component of how the data is stored and acquired. Therefore, the first part of the course will focus on database design and executing structured query language (SQL) scripts in MySQL workbench for acquiring data. Once the data is in hand, the next step is to analyze that data. Commonly, statistical techniques are utilized for testing hypothesis and determining if there are statistically significant observations. However, with the expansion of big data there is an opportunity to move away from hypothesis testing and acquire new knowledge using data mining modeling. In this course, statistical analyses and data mining techniques will be discussed along with methods for deploying these techniques using the open-access analytical software, R. After taking this course, you will have a better understanding of the nature of big data and the methods used for acquiring, analyzing, and ultimately discovering new information from data. Download course syllabus.

Upon completion of the course students will be able to:

  1. Describe the opportunities and challenges with big data
  2. Design a database and execute SQL scripts for querying the data
  3. Apply basic statistical and data mining procedures to health care data.
  4. Utilize the program R for statistical and data mining purposes.

AHIMA Approved CEU ProgramThis program has been approved for 12 continuing education unit(s) for use in fulfilling the continuing education requirements of the American Health Information Management Association (AHIMA).

About the course author

David Marc is an adjunct faculty at the College of St. Scholastica in the Department of Health Informatics and Information Management where he teaches courses on databases in healthcare, data analytics, and research design. Mr. Marc has a master’s degree in biological sciences and as extensive experience working with large healthcare datasets.  Previously, a biotech company employed Mr. Marc where he conducted research on the development of predictive models for psychiatric diseases utilizing medical history and laboratory data. Currently, Mr. Marc is pursuing a PhD in health informatics at the University of Minnesota where he is working on the development of a patient information system for ICU clinicians.

Assessments

The course includes four assessments in the form of multiple choice quizzes. The quizzes are meant to test your knowledge on SQL, statistics, data mining, and the use of R. All quizzes are administered online and should be completed without any aid from other persons.

Technical support and academic honesty

Limited technical support is available through videos and other resource links provided. Participants are asked to comply with our policies relating to academic honestly, intellectual property, and other principles of our institution. Please see Terms of Use for details.