R. Patterson
Healthcare organizations increasingly rely on data-driven decision-making, yet many nursing professionals lack formal training in analytical methods. This paper describes the design, delivery, and evaluation of a data analytics course tailored for nurses enrolled in a Master of Science in Nursing (MSN) program at a midwestern U.S. university. The course was structured around five topic modules spanning fourteen weeks: foundations of nursing analytics, data management and visualization, statistical methods for nursing research, predictive modeling and clinical decision support, and ethics and governance. Four software platforms, that is, Microsoft Excel, Tableau, IBM SPSS Statistics, and KNIME Analytics Platform, were integrated into lab exercises to provide learners with practical experience. We surveyed 32 students who were working nurses at a regional medical center in the Chicago metropolitan area, collecting demographic information and perceptions of the topic's relevance, the tool's usefulness, the learning activity's effectiveness, and overall satisfaction. Results indicate that respondents valued data visualization and ethical governance most highly, while predictive modeling and natural language processing received lower relevance scores. Excel and Tableau were perceived as the most useful and easiest tools to use. Hands-on laboratory sessions were rated the most effective instructional activity. We discuss implications for curriculum designers seeking to build analytics literacy among nursing professionals and propose directions for future course iterations.
Pearl Academic Publishing. All rights reserved.
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