This course is designed to provide students with a hands-on introduction to the concepts, methods, and Python tools used for healthcare analytics. The course will cover the foundations of healthcare data, machine learning, and Python programming, as well as the application of these techniques to real-world healthcare data. Students will learn how to obtain, clean, and refine data from electronic health records (EHRs), build predictive models, and use analytics to improve healthcare performance.
Upon completion of this course, students will be able to:
Your weekly discussion posts throughout this course – both initial and response posts – should be substantive, thoughtful, respond to the instructions, and integrate and refer to the course material.
You will follow along with the textbook on a number of Python data analysis projects and turn in the outputs and questions related to the process.
Your written assignment in Week 2 will ask you to do research and analyze a real-world example of machine learning in a healthcare environment, then reflect on how its use aligns with the concepts learned in the course.
Your final assignment is a longitudinal project based on predictive analysis using Python. You will have two weeks to work on and complete the assignment.
Your grade in this course will be determined by the following criteria:
Grade Item | Points |
---|---|
Discussions (8 - 1 x 1 point, 7 x 4 points) | 29 |
Python Assignments (5 - 1 x 4 points, 4 x 8 points) | 36 |
Week 2 Written Assignment | 10 |
Final Assignment: Longitudinal Project | 25 |
Total | 100 |
Grade | Points Grade | Point Average (GPA) |
A | 94 – 100% | 4.00 |
A- | 90 – 93% | 3.75 |
B+ | 87 – 89% | 3.50 |
B | 84 – 86% | 3.00 |
B- | 80 – 83% | 2.75 |
C+ | 77 – 79% | 2.50 |
C | 74 – 76% | 2.00 |
C- | 70 – 73% | 1.75 |
D | 64 – 69% | 1.00 |
F | 00 – 63% | 0.00 |
Week 1: Mar 13 – Mar 20
Week 2: Mar 20 – Mar 27
Week 3: Mar 27 – Apr 3
Week 4: Apr 3 – Apr 10
Week 5: Apr 10 – Apr 17
Week 6: Apr 17 – Apr 24
Week 7: Apr 24 – May 1
Week 8: May 1 – May 5
Learning Modules | Topics | Assignments Due |
Week 1 |
Basics of Healthcare Analytics |
Introductions Discussion Weekly Discussion Week 1 Assignment: Tools of the Trade |
Week 2 |
Machine Learning in Healthcare |
Weekly Discussion Week 2 Assignment: Real World Applications |
Week 3 |
Intro to Python and Associated Libraries |
Weekly Discussion Week 3 Assignment: Exploring Methods and Variables |
Week 4 |
Importing and Analyzing Datasets |
Weekly Discussion Week 4 Assignment: Analyzing Datasets |
Week 5 |
Predictive Modeling for Healthcare Applications |
Weekly Discussion Week 5 Assignment: Predictive Modeling Part 1 |
Week 6 |
Preparing Data for Machine Learning Processes |
Weekly Discussion Week 6 Assignment: Predictive Modeling Part 2 |
Week 7 |
Cleaning Data and Daset Attributes |
Week 7 Assignment – Final Assignment: Longitudinal Project, Part 1 |
Week 8 |
Analyzing Data Visualization Outputs |
Weekly Discussion Week 8 Assignment – Final Assignment: Longitudinal Project, Part 2 |
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