HIF 635: Advanced Data Analytics

Course Description

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:

  1. Describe the concepts of Advanced Data Analytics.
  2. Examine the importance and value of data.
  3. Produce models utilizing techniques of data mining and predictive analytics to produce desired outcomes.
  4. Design visualizations to explain key outcomes.
  5. Critique model outcomes for quality.
  6. Learn tools for performing data analytics.
  7. Build K-Nearest Neighbor Predictive Models.
  8. Build Decision Tree Predictive Models.
  9. Build Random Forests.
  10. Use Linear Regression for continual data modeling.
  11. Analyzed the use of Prescriptive Modeling.

 

Course Activities and Grading

AssignmentsWeight

Discussions (100 Pts, Weeks 1-8)

20%

Projects (100 Pts, Weeks 1-8)

80%

Total

100%

Required Textbooks

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

Course Schedule

Week

SLOs

Readings and Exercises

Assignments

1

1,2

Topics: Machine Learning Foundations - Introduction to Descriptive, Diagnostic, Predictive Analytics and Prescriptive Analytics

  • Review Getting Started information
  • Review course syllabus
  • Read assigned chapters
  • Review the lecture material
  • Introduce yourself in discussion forum
  • Participate in discussion - The Value of Data Discussion Board
  • Submit Assignment: Determining the value of your data

2

3,4,6

Topic: Introduction to Python

  • Read assigned chapters
  • Review the lecture material
  • Participate in discussion – Patient Health Identification
  • Submit Assignment: Write a python application

3

3,4,6

Topic: Python Machine Learning


 

  • Read assigned chapter
  • Review the lecture material
  • Participate in discussion – Patient Health Information
  • Submit Assignment: Python ML

4

3,4,6,7

Topic: Python for predictive analysis using classification


 

  • Read assigned chapter
  • Review the lecture material
  • Participate in discussion – Too Much Data
  • Submit Assignment: Writing KNN algorithm using python and analyzing data

5

3,4,5,6,8

Topics: Python and Decisions Trees

  • Read assigned chapter
  • Review the lecture material
  • Participate in discussion – Healthcare Improvement
  • Submit Assignment: Using Decision Trees

6

3,4,5,6,9

Topic: Random Forests

  • Read assigned chapter
  • Review the lecture material
  • Participate in discussion – Changes in Quality
  • Submit Assignment: Building Random Forests

7

3,4,6,10

Topics: Linear Regression Using Python


 

  • Read assigned chapters
  • Review the lecture material
  • Participate in discussion – Data Visualization
  • Submit Assignment: Linear Regression with Python

8

3,4,6,11

Topic: Working toward the goal of Prescriptive Analytics

  • Read assigned chapters
  • Review the lecture material
  • Participate in discussion – Predicting the Future
  • Submit Assignment - Cutting Through the Hype

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.