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HIF 537: Research Methods and Data Visualization

Course Description

This course introduces research methods and data visualization techniques for analyzing healthcare data. Students learn practical skills in research design, ethics, data preparation, and storytelling, using generative AI tools to support coding, analysis, and visualization. No prior programming experience is required; all coding is performed via vibe coding using generative AI. This is not a software development course. Rather, it is an AI-supported exploration of healthcare data analytics and visualization.

Prerequisites

  • None

Student Learning Outcomes (SLOs)

Upon successful completion of the course, the student will be able to:

  1. Apply a variety of research methodologies to specific healthcare problems.
  2. Analyze the various types of healthcare data and the data quality challenges of each data type.
  3. Design appropriate data visualization techniques to communicate effectively regarding what was learned from data analysis.
  4. Apply generative AI to create software tools for meaningful analysis of healthcare data.
  5. Generate synthetic data and use generative AI to create Python programs.
  6. Create a narrative story with data.
  7. Reframe the high dimensionality of healthcare problems and simplify the problem to create more manageable solutions.
  8. Leverage data clustering techniques to find meaning in datasets.
  9. Define the interpretability of machine learning (ML) systems and create a simple ML system to evaluate healthcare data.

Course Activities and Grading

AssignmentsWeight

Discussions (385 Pts, Weeks 1-8)

38.5%

Labs (390 Pts, Weeks 2-8)

39%

Lab proficiency Test (25 points, schedule with professor Week 1-3) 

2.5%

Final: Live PPT presentation on 10 important or interesting things learned in the class (100 points) 

10%

Final: Demonstrate Vibe Coding proficiency through the execution of a programming prompt given during the test. (100 points)  

10%

Total

100%

Required Textbook

  • This course uses Open Educational Resources (OER). OER are openly licensed, educational resources that can be used for teaching, learning and research. OER may consist of a variety of resources such as textbooks, videos and software that are no cost for students.

Course Schedule

Week

PLOs

SLOs

Readings and Exercises

Assignments

1

HDA 1,5

1,4,5

Topic: Research Methodologies and Data Visualization for Healthcare Data Analytics

 

  • Introductions
  • Review the lecture material including course videos
  • Participate in discussion
  • Complete Week 1 Assignments including Lab(s)

2

HDA 1,2,4,6,8

2,3,4,5,6

Topics: Research Study Design, Data Generation, Initial Data Exploration, and Intro to Generative AI

  • Review the lecture material including course videos
  • Participate in discussion
  • Complete Week 2 Assignments including Lab(s)

3

HDA 1,2,4,5,6,8

1,2,3,4,5,6,7,8

Topics: Creating and visualizing time series data. Tracking Change: Understanding, Generating, and Visualizing Data Over Time, Descriptive Analytical Research Methods


 

  • Review the lecture material including course videos
  • Complete Week 3 Assignments including Lab(s)

4

HDA 1,2,4,5,8

1,2,3,5,7,8

Topic: Taming Complexity: Heatmaps, Understanding, Reducing, and Visualizing High-Dimensional Data, Distance Metrics, and Working with Unstructured Data


 

  • Review the lecture material including course videos
  • Participate in discussion
  • Complete Week 4 Assignments including Lab(s)

5

HDA 1,2,3,4,5,68

1,2,3,4,5,6,8

Topics: Uncovering Natural Data Groupings - Data Clustering. Patient Phenotyping

 

  • Review the lecture material including course videos
  • Participate in discussion
  • Complete Week 5 Assignments including Lab(s)

6

HDA 1,2,4,5,6,8

1,2,3,4,5,6,7

Topics: Predictive Analytics and Fun with Graphs: Word Clouds and Bubble Charts

  • Review the lecture material including course videos
  • Complete Week 6 Assignments including Lab(s)

7

HDA 1,2,4,5,6,8

1,2,3,4,5,7

Topic: Regression, Prescriptive Analytics Research Methodology, Predictive Analytics Research Methodology, and Animated Graphs

  • Review the lecture material including course videos
  • Participate in discussion
  • Complete Week 7 Assignments including Lab(s)

8

HDA 1,2,4,5,6,8

1,2,3,4,5,9

Topics: Introduction to Machine Learning, AI Interpretability, Swarm Plots

  • Review the lecture material including course videos
  • Participate in discussion
  • Complete Week 8 Assignments including Lab(s)
  • Submit Final Part I: Give a live presentation on two topics: 1. Describe EDA process 2. Describe the 10 most important or interesting things you learned in this class
  • Submit Final Part II: Demonstrate vibe coding proficiency through the execution of a programming prompt for a problem given by the professor during the test session

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.