Health Care Analytics Course Descriptions

All course descriptions for the Health Care Analytics Graduate Certificate Course are listed below. Please refer to the course catalog for more detailed information.

HCA Courses:

HCA 5110: Applied Healthcare Statistics (3 Credits)

This introductory statistics course is designed for people entering a healthcare profession that requires a solid grasp of statistical methods as the foundation of their work. This will also form the foundation for more advanced topics to come in later courses. The course will emphasize the principles of statistical reasoning, underlying assumptions, hypothesis testing, and careful interpretation of results. It differs from traditional statistics courses as topics will be covered in the context of their direct application to healthcare. Topics include data presentation and summarization, descriptive statistics, regression analysis, fundamental probability theory, and random variables, introductory decision analysis, estimation, confidence intervals, hypothesis testing, and ANOVA.

HCA 5210: Healthcare Information Systems (3 Credits)

This course is to provide students with a broad understanding of the strategic application of information systems technology and leveraging information systems to analyze clinical data and support decision-making in healthcare organizations. Specifically, we will present the fundamental principles, relevant healthcare knowledge and terminology, and building blocks of healthcare information systems, including the characteristics of data, information, and knowledge, the common algorithms for health applications, and technological components in real-world clinical processes. It also introduces the technical framework for handling the collection, storage, and optimal use of biomedical data. Our emphasis is on conceptual frameworks as well as a deeper level of real-world applications, covering the planning, implementation, and evaluation of information systems, and how they relate to practical healthcare decision-making and management. It also provides a basic understanding of data standards and requirements, and the critical concepts and practices in mapping and interpreting health information.

HCA 5300: Healthcare Analytics (3 Credits)

This core course is designed to build solid foundational skills and knowledge in healthcare data analytics for healthcare analysts and technology professionals. With the increasing adoption of electronic health record systems, the ability to understand, analyze, and solve problems from data has become increasingly important in the healthcare area. Big data analytics is becoming central to the healthcare industry, both regarding delivering effective outcomes and controlling escalating costs. Students will explore the value proposition for clinical intelligence and the role of analytics in supporting a data-driven healthcare system. Students will apply knowledge and skills from healthcare data mining, data science, machine learning, AI, and data management to address practical healthcare business and clinical intelligence challenges. In addition, the topics covered in this course will provide a foundation for a future advanced deep learning for healthcare course.

HCA 5600: Advanced Machine Learning for Healthcare (3 Credits)

This advanced machine learning course will involve a deep dive into recent advances in AI in healthcare, focusing in particular on deep learning approaches for healthcare problems. Students are expected to learn deep learning models such as deep neural networks, convolutional neural networks, recurrent neural networks, autoencoder, attention models, graph neural networks, and deep generative models. Students will also get a chance to learn different healthcare applications using deep learning methods such as clinical predictive models, computational phenotyping, patient risk stratification, treatment recommendation, clinical natural language processing, and medical imaging analysis. This course will focus on hands-on experiences for data scientists and machine learning practitioners to implement various practical healthcare models on diverse medical data.