Using advanced data analytics can improve patient outcomes, lower costs, improve quality and enhance the overall health delivery system performance. This course will provide an in-depth and real-world comprehension of advanced healthcare data analytics topics and the intersecting fields of data mining. The course consists of hands on projects through the understanding of data visualization, implementing scientific decision making and using predictive data analytics. This includes the use of data to make decisions on business goals and objectives as various types of healthcare organizations and emerging financial models depend on healthcare data analytics. Students will utilize tools and techniques to illustrate and present new knowledge regarding the operations, financial, quality, business intelligence, care and policy in healthcare settings that help to fuel data-driven cultures. (3 credits)
Student Learning Outcomes (SLOs)
Students who successfully complete this course will be able to:
- Describe the concepts of Advanced Data Analytics.
- Examine the importance and value of data.
- Produce models utilizing techniques of data mining and predictive analytics to produce desired outcomes.
- Design visualizations to explain key outcomes.
- Critique model outcomes for quality.
- Learn tools for performing data analytics.
- Build K-Nearest Neighbor Predictive Models.
- Build Decision Tree Predictive Models.
- Build Random Forests.
- Use Linear Regression for continual data modeling.
- Analyzed the use of Prescriptive Modeling.
Course Activities and Grading
Discussions (100 Pts, Weeks 1-8)
Projects (100 Pts, Weeks 1-8)
Available through Charter Oak's online bookstore
- Kumar, Vikar (2018). Healthcare Analytics Made Simple: Techniques In Healthcare Computing Using Machine Learning and Python. 1st ed. Packt Publishing. ISBN-13: 978-1787286702
Readings and Exercises
Topics: Machine Learning Foundations - Introduction to Descriptive, Diagnostic, Predictive Analytics and Prescriptive Analytics
Topic: Introduction to Python
Topic: Python Machine Learning
Topic: Python for predictive analysis using classification
Topics: Python and Decisions Trees
Topic: Random Forests
Topics: Linear Regression Using Python
Topic: Working toward the goal of Prescriptive Analytics
COSC Accessibility Statement
Charter Oak State College encourages students with disabilities, including non-visible disabilities such as chronic diseases, learning disabilities, head injury, attention deficit/hyperactive disorder, or psychiatric disabilities, to discuss appropriate accommodations with the Office of Accessibility Services at OAS@charteroak.edu.
COSC Policies, Course Policies, Academic Support Services and Resources
Students are responsible for knowing all Charter Oak State College (COSC) institutional policies, course-specific policies, procedures, and available academic support services and resources. Please see COSC Policies for COSC institutional policies, and see also specific policies related to this course. See COSC Resources for information regarding available academic support services and resources.