Online Master’s in Healthcare Analytics

This course will introduce students to Bayesian statistics using Python. Students will learn probabilistic models and apply Bayes’ theorem to derive the logical consequences to models and data. This course will cover Bayesian data analysis and probabilistic programming.

Credits: 3

This course will introduce students to the purpose and need for wrangling data. Data wrangling is the process in which data is shaped and formed for usefulness to solve business problems. Students will learn to merge, clean, shape and store data to use for analytics. Students will use tools such as Excel, Python and R to prepare data for analysis.

Credits: 3

This course will provide the basic framework for conducting various data and text mining methodologies, including logistic regression analyses, classical discriminant analyses, association rule, decision tree, support vector machine, neural networks, variable reduction, cluster analysis, text analytics, and web mining. In addition, this course teaches the essential and practical skills in visualization, including computer graphics, visual data representation, physical and human vision models, numerical representation of knowledge and concepts, pattern analysis, and computational methods. Recommended Prior Course: MIS500

Credits: 3

This course will teach students to form hypotheses and generate descriptive statistics that can provide key insights into existing data. Students will learn how to write SQL queries to aggregate, calculate, and combine SQL data from sources outside of a current dataset. Students will learn the efficient application of SQL techniques in everyday business scenarios and looking at data with the critical eye of analytics professional.

Credits: 3

This course covers the fundamental predictive analytics and data mining approaches applied in business. It introduces basic concepts and techniques to discover patterns in data, identify variables with the most predictive power, and develop predictive models.

Credits: 3

This course will provide students with an opportunity to learn how to visualize data and tell a data story using visualization tools. Students will learn to understand and determine the needs of the stakeholder by using Use Cases to provide data for decision-making. Students will also learn to create visualizations and dashboards to display an effective data story to an audience, create dashboards for monitoring metrics, and communicating complex ideas into effective results.

Credits: 3

This course will provide students with the opportunity to analyze different types of datasets used in healthcare analytics. Data such as hospital ratings, population health, hospital readmissions, medicare spending, healthcare staffing, hospital emergency department performance and patient satisfaction will be analyzed for insights. Socio-economic factors will also be explored as predictors for healthcare treatment.

Credits: 3

This course will educate students on concepts that can lead an organization to sustained changes and to improve clinical, operational, and financial outcomes. Students will learn by using real-world healthcare problems and use cases focusing on connecting and integrating people, process, and technology to deliver insight-driven decisions. Students will also explore innovative technologies available to manage data and apply analytics with some best practices to transform an organization. This course will also explore the future of healthcare and what to expect with the rise of digital transformation, machine learning, and artificial intelligence.

Credits: 3