Syllabus
Master of Science in Health Informatics
HIN 776 – Python for Health Data Analysts – Fall B 2023
Credits - 3
Description
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.
Materials
Learning Objectives and Outcomes
Upon completion of this course, students will be able to:
- Examine the basics of healthcare analytics and its importance in today’s healthcare sector.
- Compare and contrast the connections between machine learning and healthcare processes.
- Measure and assess healthcare quality and provider performance.
- Obtain, clean, and refine data from electronic health record (EHR) surveys.
- Apply Python programs and machine learning to build predictive models using real-world healthcare data.
- Appraise the features and attributes needed to build successful healthcare models.
- Apply python codes to a longitudinal project to analyze and visualize healthcare data utilizing pandas, numpy, and matplotlib.
Assignments
Discussions
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.
Python Assignments
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.
Written Assignment
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.
Final Longitudinal Project
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.
Grading Policy
Your grade in this course will be determined by the following criteria:
Grade Breakdown
| 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 Scale
| 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 |
Schedule
Week 1: Oct 25 – Nov 1
Week 2: Nov 1 – Nov 8
Week 3: Nov 8 – Nov 15
Week 4: Nov 15 – Nov 22
Week 5: Nov 22 – Nov 29
Week 6: Nov 29 – Dec 6
Week 7: Dec 6 – Dec 13
Week 8: Dec 13 – Dec 17
| 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 |
Student Resources
Online Student Support
Your Student Support Specialist is a resource for you. Please don't hesitate to contact them for assistance, including, but not limited to course planning, current problems or issues in a course, technology concerns, or personal emergencies.
Questions? Visit the Student Support Health Informatics page
APA Style Guide
UNE Libraries:
- Library Access for all students: Your library login ID and password are the same as the ones you use to log into Blackboard/Brightspace.
- MSIN Databases: https://library.une.edu/library-materials/find/research-by-subject/health-informatics/
- Library Questions: Ask a librarian or phone library staff at (207) 602-2361 or (207) 221-4330.
UNE Student Academic Success Center
The Student Academic Success Center (SASC) offers a range of services to support your academic achievement, including tutoring, writing support, test prep and studying strategies, learning style consultations, and many online resources. To make an appointment for tutoring, writing support, or a learning specialist consultation, go to une.tutortrac.com. To access our online resources, including links, guides, and video tutorials, please visit:
Accommodations
Any student who would like to request, or ask any questions regarding, academic adjustments or accommodations must contact the Student Access Center at (207) 221-4438 or pcstudentaccess@une.edu. Student Access Center staff will evaluate the student's documentation and determine eligibility of accommodation(s) through the Student Access Center registration procedure.
Online Peer Support
Togetherall is a 24/7 communication and emotional support platform monitored by trained clinicians. It’s a safe place online to get things off your chest, have conversations, express yourself creatively, and learn how to manage your mental health. If sharing isn’t your thing, Togetherall has other tools and courses to help you look after yourself with plenty of resources to explore. Whether you’re struggling to cope, feeling low, or just need a place to talk, Togetherall can help you explore your feelings in a safe supportive environment. You can join Togetherall using your UNE email address.
Information Technology Services (ITS)
Students should notify their Student Support Specialist and instructor in the event of a problem relating to a course. This notification should occur promptly and proactively to support timely resolution.
ITS Contact: Toll-Free Help Desk 24 hours/7 days per week at 1-877-518-4673.
Career Ready Program
The College of Professional Studies supports its online students and alumni in their career journey!
The Career Ready Program provides tools and resources to help students explore and hone in on their career goals, search for jobs, create and improve professional documents, build professional network, learn interview skills, grow as a professional, and more. Come back often, at any time, as you move through your journey from career readiness as a student to career growth, satisfaction, and success as alumni.
Policies
Technology Requirements
Please review the technical requirements for UNE Online Graduate Programs: Technical Requirements
Turnitin Originality Check and Plagiarism Detection Tool
The College of Professional Studies uses Turnitin to help deter plagiarism and to foster the proper attribution of sources. Turnitin provides comparative reports for submitted assignments that reflect similarities in other written works. This can include, but is not limited to, previously submitted assignments, internet articles, research journals, and academic databases.
Make sure to cite your sources appropriately as well as use your own words in synthesizing information from published literature. Webinars and workshops, included early in your coursework, will help guide best practices in APA citation and academic writing.
You can learn more about Turnitin in the guide on how to navigate your Similarity Report.
Information Technology Services (ITS)
ITS Contact: Toll Free Help Desk 24 hours/7 days per week at 1-877-518-4673
Course Evaluation Policy
Course surveys are one of the most important tools that University of New England uses for evaluating the quality of your education, and for providing meaningful feedback to instructors on their teaching. In order to assure that the feedback is both comprehensive and precise, we need to receive it from each student for each course. Evaluation access is distributed via UNE email at the beginning of the last week of the course.
Late Policy
Assignments: Late assignments will be accepted up to 3 days late; however, there is a 10% grade reduction (from the total points) for the late submission. After three days the assignment will not be accepted.
Discussion posts: If the initial post is submitted late, but still within the discussion board week, there will be a 10% grade reduction from the total discussion grade (e.g., a 3 point discussion will be reduced by 0.3 points). Any posts submitted after the end of the Discussion Board week will not be graded.
Please make every effort ahead of time to contact your instructor and your student support specialist if you are not able to meet an assignment deadline. Arrangements for extenuating circumstances may be considered by faculty.
Attendance Policy
8 week: Students taking online graduate courses through the College of Professional Studies will be administratively dropped for non-participation if a graded assignment/discussion post is not submitted before Sunday at 11:59 pm ET of the first week of the term. Reinstatement is at the purview of the Dean's Office.
16 week: Students taking online graduate courses through the College of Professional Studies will be administratively dropped for non-participation if a graded assignment/discussion post is not submitted before Friday at 11:59 pm ET of the second week of the term. Reinstatement is at the purview of the Dean's Office.
Student Handbook Online - Policies and Procedures
The policies contained within this document apply to all students in the College of Professional Studies. It is each student's responsibility to know the contents of this handbook.
UNE Course Withdrawal
Please contact your student support specialist if you are considering dropping or withdrawing from a course. The last day to drop for 100% tuition refund is the 2nd day of the course. Financial Aid charges may still apply. Students using Financial Aid should contact the Financial Aid Office prior to withdrawing from a course.
Academic Integrity
The University of New England values academic integrity in all aspects of the educational experience. Academic dishonesty in any form undermines this standard and devalues the original contributions of others. It is the responsibility of all members of the University community to actively uphold the integrity of the academy; failure to act, for any reason, is not acceptable. For information about plagiarism and academic misconduct, please visit UNE Plagiarism Policies.
Academic dishonesty includes, but is not limited to the following:
- Cheating, copying, or the offering or receiving of unauthorized assistance or information.
- Fabrication or falsification of data, results, or sources for papers or reports.
- Action which destroys or alters the work of another student.
- Multiple submissions of the same paper or report for assignments in more than one course without permission of each instructor.
- Plagiarism, the appropriation of records, research, materials, ideas, or the language of other persons or writers and the submission of them as one's own.
Charges of academic dishonesty will be reviewed by the Program Director. Penalties for students found responsible for violations may depend upon the seriousness and circumstances of the violation, the degree of premeditation involved, and/or the student’s previous record of violations. Appeal of a decision may be made to the Dean whose decision will be final. Student appeals will take place through the grievance process outlined in the student handbook.