Master's Certificate in Healthcare Analytics Online

Gives students an overview of the history, concepts, purpose, and building blocks of healthcare analytics. Topics will include data acquisition and management, data analysis and visualization, and storytelling with data. Using case studies and other applied assignments, students will learn how to use statistical techniques to plan, deliver, and improve healthcare services, interpret data to make evidence-based decisions, and visualize large amounts of data to generate reports that tell a story. Given the rapidly advancing nature of the field, the course will touch on the latest technologies for healthcare data analytics. This course will also prepare students to begin work on the healthcare analytics capstone project, to be completed by the end of the program.

Teaches theory and best practices of modern healthcare analytics with attention to classification and prediction models. We cover preparation, visualization, and exploration of data and provide hands-on experience with the main methods of data mining and machine learning. In working with RStudio and R packages, students will become experienced in the creation of accurate models, as well as professional communication of their analytic results.

Some of the most important recent innovations in healthcare analytics are rooted in the fields of data mining and machine learning. In this course we will study a variety of major techniques currently applied to healthcare data, always in the context of real problems, and the search for actionable results. We will regularly move between theory and practice, working through the challenges of coding and managing data sources in a flexible yet efficient manner.

Designed to provide students with an understanding of the challenges of collecting and maintaining electronic health data. The course focuses on issues specific to health data and the systems implemented to collect and store it. This includes an overview of various types of hospital systems; methods used to interface between systems; and operations issues typical of hospital systems. The course also includes a study of the evolving health care regulatory environment and common classifications for health care data. By the end of the course, students will be able to evaluate and identity health care data set problems and implement EHR-based solutions to the source data.

Among the most promising developments in data analytics is the growth in Data/Information Visualization. Across numerous disciplines, tools and techniques are emerging that help people interactively analyze and understand the flood of data now available. There is rapid growth in the availability of tools and also in the number of domains in which these techniques are deployed, including healthcare.

Visualization can tap into the ways in which health professionals rapidly and intuitively process information, taking advantage of IT tools to move beyond tabular displays and simple graphs. The theory and techniques of data visualization not only make information accessible, but also can open paths to exploration of cause and effect.

This course provides an overview of the field of data visualization, presenting current theory and best practices to students. We will rely heavily on hands-on learning, developing skills in Tableau and R. Although we naturally will use particular software, the lessons are aimed at principles so that students can continue to refine their skills as new tools emerge. Students will learn to evaluate and assess existing visualizations as well as develop their own information-rich interactive displays.