Incorporating a Post-Graduate Course in Data Science Into the Nursing Post-Graduate Curriculum: Benefits and Challenges

Purpose: Data science is a rapidly growing interdisciplinary field that reflects the integration of mathematical, statistical, computer and computational sciences. Advances in the digitization and accessibility of big data generated from discoveries in the human genome and biological sciences necessitate analytic methods that enable the collection, preparation, management, visualization, storage, and analysis of large data sets. Furthermore, large volumes of unstructured narrative data are proliferating through expansion in social media and social networks, opening new possibilities for insight into the environmental contexts which play a role in promoting the health and well-being of individuals and society. Traditional nursing curricular offerings offer little exposure to data science concepts challenging nurse educators to consider how to prepare themselves and introduce data science to the next generation of nurse leaders to navigate the changing landscape created by trends in big data. The purpose of this presentation is to discuss the faculty and student experience and outcomes of developing and pilot-testing a post-graduate course in Data Science for Nurses.

Methods:

This project used a case study descriptive qualitative approach. A faculty team consisting of a nurse scientist and behavioral scientist met weekly for six-months to experiment with the use of R and Python computer languages. The interdisciplinary faculty team created a hybrid, post-graduate course in data science for nurses. Individual review of basic statistical concepts was built into the first few weeks of the course. Instructionally, the course consisted of four modules supported by an online learning management system. Data science exercises, asynchronous case studies requiring the use of beginning concepts and tools in data science, a clinical analytics textbook, and free online data science and statistical websites were used to deliver and support course content. A semester-long project designed to answer a clinical question using data science was presented in poster format as a culminating exercise. A single debriefing-focus group was conducted at the completion of the course. Student perceptions were recorded verbatim, clustered into themes using a qualitative interpretive approach and was reviewed for veracity by all participants.

Results:

Four (66%) of the students who began the course completed. The narrative themes included: “power of data-driven decisions,” “value of supporting claims/position with data “increased awareness of big-data,” “use of effect size,” “collaboration to solve problems and gain new skills,” “difficulty with time needed to explore R and Python,” “appreciation for under-used tools like Excel,” “fostering independent thinking,” and “taking nursing to the next level”. Pedagogically, the course materials and format fostered student collaboration while exploring the application and use of data science tools. Exposure to R and Python were supported through the use of the Data Camp website; however, one semester was not adequate for an introductory exposure to the languages commonly used in data science.

Conclusion:

Exposing post-graduate students to data science concepts may foster the use of analytic methods and data-driven decision making among post-graduate students. A single semester limited time for practice with R and Python software. Interdisciplinary approaches to incorporating data science into nursing curricular offerings are needed.