Syllabus

Master of Science in Health Informatics

HIN 715 – Health Data Analysis, Visualization, and Storytelling – Fall B 2023

Credits - 3

Description

Large data sets are not useful in their native state. Informaticists have to begin by defining the question that will be answered by the data and then organizing, analyzing, and visualizing the dataset. Analytics provide meaningful patterns in the data, and data visualization communicates the information clearly through graphical means. This course is designed to familiarize students with core concepts in communicating information through effective data visualization. This course introduces students to data visualization elements and best practices in data visualization using Tableau. Students will gain hands-on experience building explanatory and exploratory visualizations using healthcare data.

Materials

Required:

Benevento, D., Rowell, K., Steeger, J., Cutrell, A., Morales, M. (2017). Best boring book ever of tableau for healthcare (3rd ed.). HealthDataViz. ISBN: 978-0692938508

Knaflic, C.N. (2015) Storytelling with data. John Wiley & Sons, Inc. ISBN 978-1119002253; eText 978-1119002062  (Available as etext via UNE Library)

Required Software Tools:

  • Microsoft Excel —This is the most commonly used and commonly available software for data visualization in the world and can be expected to be available in almost every healthcare setting.
  • Tableau Desktop —Tableau is a leading data visualization and business intelligence tool that supports the cycle of visual analysis to rapidly manipulate and see data. Tableau offers free licenses to students, academics,  and smaller non-profits. As part of this course, you will be getting a free license for the duration of the course. License and download instructions provided in Blackboard.
  • MySQL/MySQL Workbench — MySQL™ is a free, open-source relational database management system (RDBMS) that utilizes both the user-friendly interface, MySQL Workbench, and a multiuser database server. Further, MySQL allows users to utilize Structured Query Language (SQL) to manipulate the data in the structured databases. 

Recommended:

  • Alexander, M., et al. (2018). Excel 2019 Bible. John Wiley & Sons, Inc. (Available as etext via UNE Library)
  • American Psychological Association. (2019). Publication manual of the American Psychological Association (7th ed.). American Psychological Association. ISBN: 978-1433832154. E-text: 978-1433832185

Helpful Resources:

Online help for Tableau  – You will find lots of helpful tutorials here that you may wish to consult as you go through the course. You may wish to bookmark this site.

Tableau Community – Highly recommend setting up a free account to ask questions, read solutions to your hair-pulling Tableau issues, to get introduced to a robust data community, and to view a multitude of Tableau views. Worth a bookmark and a frequent visit!

Learning Objectives and Outcomes

Program Outcome addressed by this course:

  • Apply core concepts of database design to facilitate managing data produced and captured in the healthcare setting

Course Outcomes:

Upon completion of the course, students will be able to:

  • Create a variety of types of graphics in order to visualize data appropriately
  • Normalize data in order to make use of it in visualizations
  • Evaluate a research question in order to select appropriate method(s) of visualization

Assignments

Please note that all times in the syllabus and in Blackboard refer to Eastern Time. The discussion board for each week will open at the start of the week for submissions.

Discussion Board Posts:

These assignments will assess your ability to clearly and accurately apply concepts from your readings and from your own experiences. Each week you are expected to submit an initial post and comment on at least 2 other students’ posts. You need to follow APA guidelines for citing any sources you may reference in either your initial post or your response to others. Refer to the Discussion Rubric and discussion question for submission guidelines.

Initial post: You should submit your initial post by 11:59 p.m. Sunday. Your initial post should be approximately 500 words.

Response to others: You should comment on at least 2 other students’ posts by 11:59 p.m. Wednesday. Your comments to others should be thorough, thoughtful, and they should offer some new content. Do not merely respond with “I agree” or “I disagree.” Engage directly with the ideas of your classmates and briefly mention which part of the post you are responding to.

Weekly Assignments: 

In weeks 1-4 and 6 there are a number of short assignments. You will use either Tableau or Excel to create these assignments. Please see Blackboard for specifics and rubrics.

Key Assessments: 

There are two key assessments for this course in which you will be asked to develop your own visualizations. The weekly assignments support and provide the knowledge and skills needed to complete these assessments. In these assessments, you will apply the core concepts in communicating information through effective data visualization. 

Key Assessment 1, Explanatory Visualization Presentation – In week 5, you will use what you have learned in the first 4 weeks to create an explanatory visualization presentation to compare physician performance on a number of quality measures. Then, you will use a screencasting tool such as Screencast-o-Matic to present your visualizations. Please see Blackboard for specifics and rubrics.

Key Assessment 2, Exploratory Research, Visualizations, and Presentation – In this two-part assessment,  you will integrate what you have learned into an interactive dashboard of your own creation.

Part 1: In week 7,  you will research publicly available datasets for a health topic of your choice, then write a use case and description of your methods for visualizing your data, along with 3-5 references.

Part 2: In week 8, you will communicate your research visualizations in either a poster presentation or an interactive Tableau Dashboard. You will also record a 5-10 minute screencast presenting your visualization. 

Please see Blackboard for specifics and rubrics.

Grading Policy

Your grade in this course will be determined by the following criteria:

Grade Breakdown

Assessment ItemPossible PointsPercent of Total Grade
Discussion Forums (6)18 pts - (3 pts each)18%
Weekly Assignments (weeks 1-4, 6)42 points (point values vary by week)42%
Key Assessment 1: Explanatory Visualization Presentation (week 5)18 points18%
Key Assessment 2, Part 1: Exploratory Interactive Dashboard (week 7)10 points10%
Key Assessment 2, Part 2: Exploratory Visualizations and Presentation (week 8)12 points12%
Total100 pts100%

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

Course learning modules are divided into weeks. Each week starts on Wednesday at 12:00 am Eastern Time (ET) and closes on Wednesday at 11:59 pm ET, with the exception of Week 8, which ends on Sunday. All assignments must be submitted by 11:59 pm ET on the due date.

Course 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 and Due Dates

Week 1

What is Data Visualization?

Introductory Discussion
Discussion – Initial post by Sunday, responses by Wednesday

Three Short Assignments due Wednesday

Week 2

Understanding Use Cases and Creating Simple Charts

Discussion – Initial post by Sunday, responses by Wednesday

Three Short Assignments due Wednesday 

Week 3

Understanding Descriptive Statistics and Building Intermediate Charts (Part 1)

Discussion -Initial post by Sunday, responses by Wednesday

Four Short Assignments due Wednesday

Week 4

Understanding Descriptive Statistics and Building Intermediate Charts (Part 2)

Discussion – Initial post by Sunday, responses by Wednesday

Four Short Assignments due Wednesday

Week 5

Explanatory Visualizations

Discussion – Initial post by Sunday, responses by Wednesday

Key Assessment 1: Explanatory Visualization Presentation due Wednesday

Week 6

Principles of Good Visualizations

Discussion – Initial post by Sunday, responses by Wednesday

Create a Dashboard Assignment due Wednesday

Week 7

Exploratory Research and Visualizing Findings

Discussion – Initial post by Sunday, responses by Wednesday

Key Assessment 2, Part 1: Exploratory Research and Visualizations due Wednesday

Week 8

Communicating Results

Discussion – Initial post by Friday, responses by Sunday

Key Assessment 2, Part 2: Exploratory Visualizations and Presentation due Sunday

 

Student Resources

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ITS Contact: Toll Free Help Desk 24 hours/7 days per week at 1-877-518-4673

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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.

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UNE Online Student Handbook

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Academic dishonesty includes, but is not limited to the following:

  1. Cheating, copying, or the offering or receiving of unauthorized assistance or information.
  2. Fabrication or falsification of data, results, or sources for papers or reports.
  3. Action which destroys or alters the work of another student.
  4. Multiple submissions of the same paper or report for assignments in more than one course without permission of each instructor.
  5. 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.