Syllabus

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 at a Glance

Learning Modules	Topics	Assignments and Due Dates
Week 1 10/23 – 10/30	What is Data Visualization?	Introductory Discussion Discussion – Initial post by Sunday 10/27, responses by Wednesday 10/30 Four Short Assignments – Wednesday 10/30
Week 2 10/30 – 11/6	Understanding Briefs and Creating Simple Charts	Discussion – Initial post by Sunday 11/3, responses by Wednesday 11/6 Three Short Assignments – Wednesday 11/6
Week 3 11/6 – 11/13	How do we Know the Data is Good?	Discussion -Initial post by Sunday 11/10, responses by Wednesday 11/13 Three Short Assignments -Wednesday 11/13
Week 4 11/13 – 11/20	Making the Data Usable	Discussion – Initial post by Sunday 11/17, responses by Wednesday 11/20 Five Short Assignments – Wednesday 11/20
Week 5 11/20 – 11/27	Explanatory Visualizations	Discussion – Initial post by Sunday 11/24, responses by Wednesday 11/27 Key Assessment 1: Explanatory Visualization Presentation – Wednesday 11/27
Week 6 11/27 – 12/4	Principles of Good Visualizations	Discussion – Initial post by Sunday 12/1, responses by Wednesday 12/4 Create a Dashboard Assignment – Wednesday 12/4
Week 7 12/4 – 12/11	Exploratory Dashboards	Discussion – Initial post by Sunday 12/8, responses by Wednesday 12/11 Key Assessment 2: Exploratory Interactive Dashboard – Wednesday 12/11
Week 8 12/11 – 12/15	Network Mapping	Discussion – Initial post by Friday 12/13, responses by Sunday 12/15 Network Mapping Assignment -Sunday12/15

Weekly Course Schedule

WEEK 1 — What is Data Visualization?

Learning Outcomes:

• Explore what makes a good visualization
• Create simple visualizations with Excel and Tableau
• Critique a visualization

Course Materials:

Kirk, A. (2016). Data visualisation: A handbook for data driven design. Thousand Oaks, CA: Sage Publications, Inc.

• Chapter 1 – Defining Data Visualisation
• Chapter 2 – Visualisation Workflow

Forum One Communications. (2014, February 11). Worth a Thousand Words: How to Display Health Data. Retrieved from https://www.chcf.org/publication/worth-a-thousand-words-how-to-display-health-data/

Video:

Harvard Business Review. (2016, May 19). Designing Persuasive Charts [Video File]. Retrieved from https://hbr.org/video/4901986396001/designing-persuasive-charts

Tableau Tutorials:

• Tableau Interface – 4-minute overview. 
• Getting Started With Tableau – 25-minute tutorial. 

Tableau Help Page (to be used for reference, as needed):

• Online Help for Tableau

Assignments:

Introductory Discussion

Discussion Question, Data Visualization Critique: In this discussion you will critique a visualization. From the document link provided in Blackboard, please select one of the visualizations; (all the visualizations were created based on recent data on malaria). To make sure that each of you picks a different visualization, put your name on the google doc next to the visualization you have chosen. Make sure you put your name on the document before you start working on your post. Please don’t erase anyone’s name who has already chosen a visualization. Your critique should include answers to the following:

• • Who is the visualization for (who is the intended audience)?
  • What is the visualization trying to communicate (i.e. what is the goal)?
  • What aspects of the visualization allowed it to reach the goal?
  • Where did the visualization fall short?
  • If the visualization is interactive, did the interactivity support the goal of the visualization?

Week 1 Assignments: You will set up several tools that you will use throughout the course and submit a screenshot. See Blackboard for details.

WEEK 2 — Understanding Briefs and Creating Simple Charts

Learning Outcomes:

• Explore basic chart types
• Build three basic chart types — bars, lines, text tables — in both Excel and Tableau

Course Materials:

Kirk, A. (2016). Data visualisation: A handbook for data driven design. Thousand Oaks, CA: Sage Publications, Inc.

• Chapter 3: Formulating Your Brief (In the US a “brief” is called a “use case”)
• Chapter 5: Establishing your Editorial Thinking
• Chapter 6: Pages 151-160 (intro), 161-162 (bar charts) and page 191 (line charts). You will read the full chapter later.

Videos:

RichReport. (2014, March 23). The Zen of Visual Analysis [Video file]. Retrieved from https://www.youtube.com/watch?v=QfmVoFcZJM0

Nussbaumer, C. (2016, September 21). Do you see it? The power of CONTRAST [Video file]. Retrieved from https://www.youtube.com/watch?v=60KiAXbkrl0

Nussbaumer, C. (2016, June 2). The Cat in the Hat knows a lot about Data Visualization [Video file]. Retrieved from https://www.youtube.com/watch?v=yFUvaxF6S84

Assignments:

Discussion Question, Rethinking Visualizations: How have this week’s readings & viewings changed how you see data visualization and the process of creating visualizations? Think of a past visualization that you’ve created and write how you would approach that differently now, whether that be in the process of assembling your brief/use case (or writing one because you didn’t have one), the design process, and/or the implementation.If you haven’t done visualizations, then think of text that you’ve written that could have been visualized. How would you approach the task of creating the visualization(s)?

Week 2 Assignments: In this week’s assignments you will create visualizations for cardiac quality measures. Note that you will create the visualizations in both Tableau AND Excel. See Blackboard for assignment specifics and helpful resources. 

WEEK 3 — How do we Know the Data is Good?

Learning Outcomes:

• Build scatter plots, strip plots, and box plots in Tableau
• Use data visualizations to answer specific questions about the data

Course Materials:

Kirk, A. (2016). Data visualisation: A handbook for data driven design. Thousand Oaks, CA: Sage Publications, Inc.

• Chapter 4 – Working With Data
• Chapter 6, pp. 163-195 – This week look at the different types of graphics on pages 163 – 195. You will read the entire chapter next week.

Wickham, H. (n.d.) Tidy data. Journal of Statistical Software,1 – 13. Retrieved from http://vita.had.co.nz/papers/tidy-data.pdf

Rost, L. C. (2016, December 16). How we cleaned up and ranked our listeners’ favorite albums of 2016 [Blog post]. Retrieved from http://blog.apps.npr.org/2016/12/16/all-songs-considered-poll.html

Review Healthcare Hotspotting website: (https://hotspotting.camdenhealth.org/getting-started-with-data/)

Why Data Visualizations. (n.d.). Retrieved from https://www.dashingd3js.com/why-data-visualizations

Videos:

Micallef, L. (2012, November 7). Explaining Bayesian problems using visualizations [Video file]. Retrieved from https://www.youtube.com/watch?v=D8VZqxcu0I0

PBS. (2011, August 3). Frontline: Doctor hotspot (full report) PBS [Video file]. Retrieved from https://www.youtube.com/watch?time_continue=2&v=0DiwTjeF5AU

Assignments:

Discussion Question, Cleaning Up Data: Healthcare data is not uniform across systems. For example, dates might use a mm/dd/yy format (numbers) or perhaps month name, date, year (words). Systems may also differ in measure, such as measuring a baby’s weight in ounces or grams. Inconsistent data needs to be cleaned up before it can be used to create data visualizations. Cleaning up that data takes time and is therefore costly. Read the article on how NPR ran into a problem with inconsistent data and what they did to clean it up: How We Cleaned Up And Ranked Our Listeners’ Favorite Albums of 2016 (Rost, 2016) linked in course.

Think about another case where inconsistent data caused problems. Your example can be from your work life, your personal life, or something you read about in the media. If you can’t find an example, please imagine one. Describe the scenario. What was the process for ensuring that the data was consistent and cleaning up any inconsistent data? (or what should it have been if it wasn’t done) Were there lasting ramifications caused by the inconsistent data?

Week 3 Assignments: In this week’s assignments you will use the HbA1c dataset and Tableau to create scatter plots, strip plots, and box plots to allow you to visualize different aspects of the data. See Blackboard for details. 

WEEK 4 — Making the Data Usable

Learning Outcomes:

• Address variations in databases to normalize data
• Identify appropriate variables so that tables may be joined
• Select appropriate visualizations to show levels of granularity

Course Materials:

Kirk (2016) textbook, Chapter 6 – Data Representation

Abela, A. (2009).Chart suggestions-A thought-starter [Graphic]. Retrieved from http://extremepresentation.typepad.com/files/choosing-a-good-chart-09.pdf

Few, S. (2005). Effectively communicating numbers: Selecting the best means and manner of display Retrieved from https://www.perceptualedge.com/articles/Whitepapers/Communicating_Numbers.pdf

Presentation:

Cherdarchuk, J. (2014, September 26). Salvaging the Pie. Retrieved from https://www.darkhorseanalytics.com/blog/salvaging-the-pie

Assignments:

Discussion Question, Levels of specificity: Data can be viewed at different levels of specificity. Let’s use an electronic calendar as an example. You can look at your calendar by day, by week, by month, and even by year. The calendar view you select, is influenced by the information you need.

Now consider a patient with heart failure who needs to lose weight and who also needs to track their weight on a daily basis because extreme changes in weight may indicate a change in disease status. What is the value of looking at this person’s weight change by day, week, month, year?

What is another example of health data that we might want to view at different levels of specificity? Why? If you don’t have experience looking at clinical data in this way, write about something that you might track: finances, weight loss, strength training, sport training, etc.

Week 4 Assignments: You will use the HbA1c dataset and Tableau to create pie charts, a stacked bar chart, and a treemap. You will also be given a database with inconsistent data that you will have to clean up. In addition, you will join two datasets using Tableau. See Blackboard for details. 

WEEK 5 — Explanatory Visualizations

Learning Outcomes:

• Build an explanatory visualization to present needed information
• Create a presentation to showcase the visualization

Course Materials:

Kirk (2016), Chapter 6 – Data Representation (you may want to look at this chapter again) and Chapter 8 – Annotation

Videos:

Rosling, H. (2006, February). The best stats you’ve ever seen [Video file]. Retrieved from https://www.ted.com/talks/hans_rosling_shows_the_best_stats_you_ve_ever_seen

Reininger, H. (2012, April 17). Hans Rosling’s shortest TED talk [Video file]. Retrieved from https://www.youtube.com/watch?v=UNs-ziziPyo

Assignments:

Discussion Question, Peer Support for Key Assessment 1: The week 5 discussion is ungraded. It is a peer support discussion. Use it to reach out to your classmates for assistance with the week 5 assignment. You should also plan to check it throughout the week to see if you can help others with challenges they may have.

Key Assessment 1 Explanatory Visualization Presentation: This assignment is the first of two key assessments in the course. The student will create an explanatory visualization to show physician and practice level performance compared to target values for 5 quality measures over time. Please see Blackboard for specifics.

WEEK 6 — Principles of Good Visualization

Learning Outcomes:

• Discuss elements that work well in visualizations
• Construct a simple dashboard

Course Materials:

Kirk, Chapter 7 – Interactivity, Chapter 9 – Colour, and Chapter 10 – Composition

See Blackboard for additional course materials for completing this week’s assignments.

Assignments:

Visualization Upload: Upload the document or powerpoint that contains the visualization that you created last week to Google Drive folder linked in the course. You will need to look at one another’s visualizations to do in this week’s discussion post.

Discussion Question, Effective Visualization Elements: What works in a visualization is often a matter of personal preference. Take a look at the visualizations that your classmates created last week. What elements of the visualizations were most effective for you? What elements did you find confusing? What elements were not as effective for you?

The purpose of this discussion is not to discuss individual visualizations, but rather to consider the visualizations as a whole and talk about elements of the visualizations: choices in data representation, color, composition, annotation, etc. Remember, you can find the visualizations in the Google Folder (where you should have put your own visualization at the beginning of this week) linked in the course.

Create a Dashboard Assignment: During the next two weeks, you will create exploratory visualizations. Exploratory visualizations are often referred to as interactive visualizations because the user can interact with the visualization to see the data from a variety of different views. The assignment for this week is to create an interactive dashboard using the HbA1c dataset that you have used in prior weeks. (Next week you will create an interactive dashboard from a dataset that is less familiar to you.) See Blackboard for explicit instructions.

WEEK 7 — Exploratory Dashboards

Learning Outcomes:

• Create an interactive dashboard

Course Materials:

Review parts of the Kirk (2016) textbook to help you with your dashboard design, including pp. 79-80, 160, 258-261, 223-246 (chapter 7).

See Blackboard for additional course materials on building dashboards.

Assignments:

Discussion Question, Peer Support for Key Assessment 2: This is an ungraded peer support post. Please post any challenges or problems you encounter this week. Your peers may be able to give you ideas to solve the problems you run into. Check this discussion throughout the week to see if you help your classmates with challenges they may have.

Key Assessment 2 Exploratory Interactive Dashboard: This week you will create your second key assessment project: an interactive dashboard with a dataset that you have used in the past. You will turn in the brief, the Tableau workbook, and a demonstration of the dashboard’s functionality through Screencast-o-matic or another tool. Your dashboard will include three visualizations. One visualization is your choice — it should be a showcase for your creativity and demonstrate how well you have come to learn Tableau. The remaining two visualizations will be used by clinicians and financial staff, the format of the visualizations is up to you. Your dashboard will provide insight into questions posed in the prompt in aggregate and at the individual hospital level of analysis.

WEEK 8 — Network Mapping

Learning Outcomes:

• Create a network map

Course Materials:

TED. (2010, June). Nicholas Christakis: How social networks predict epidemics [Video file]. Retrieved from https://www.ted.com/talks/nicholas_christakis_how_social_networks_predict_epidemics?language=en

Assignments:

Discussion Question, Predictions with Social Networks: Discuss your thoughts on Nicholas Christakis’s Ted talk. He mentions a number of things, such as flu epidemics, that we can use social networking to predict. If you could have access to all the necessary data, what would you like to use social networking to predict? Why?

Network Mapping: You will use Gephi to create a network map that shows the relationships between HIV users and their contacts/partners. See Blackboard for specific instructions.

Student Resources

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• https://www.apastyle.org

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• 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 Tutorial: Find an article
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• UNE Student Academic Success Center

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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.

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Online students are required to submit a graded assignment/discussion prior to Sunday evening at 11:59 pm ET of the first week of the term. If a student does not submit a posting to the graded assignment/discussion prior to Sunday evening at 11:59 pm ET, the student will be automatically dropped from the course for non-participation. Review the full attendance policy.

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.

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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.
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