Dataviz Syllabus

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Dataviz

Introduction to DataViz Syllabus

COMM 2300: Introduction to Data Visualization

Summer Session: 2016

Course Meeting Location: This class is entirely asynchronous

Duration: 6 Weeks

Course Instructor: Yvonne Wade Sanchez

Office Hours: M-F, 11:00am – 1:00pm

Audience

This is a sophomore-level service course targeting students from a cross section of disciplines (e.g., engineering, computer sciences, biology, art, technical communication, etc.).

Problem Statement

The relative newness of the phenomenon of data visualization training in numerous disciplines has created a need for technical communicators to obtain knowledge and training in this area. Therefore, this course is designed to help technical communicators become familiar with the theories, technologies, and tools associated with data visualization. This course is not intended to provide software or tools training. The tools used to produce data visualizations are wide and varied. Instead, this course focuses on preparing technical communicators to ask the right questions and to participate productively and meaningfully as member of a multi-discipline data visualization team.

Course Overview

Data visualization is currently a popular format for disseminating technical information (Wijk, 2005). For example, the CDC is used infographics to educate the population about the 2014 Ebola outbreak (http://www.cdc.gov/vhf/ebola/resources/infographics.html). The popular use of infographics and data visualization deliverables is setting the expectation for current and future information consumers. In addition, educational institutes across the nation are including data visualization courses in their programs. Interestingly, these programs appear in a wide variety of educational departments (i.e., art, computer science, mathematics, mass communication, technical communication, psychology, etc.). The appearance of data visualization courses in a variety of university departments reflects the diversity of skills necessary to production effective data visualization deliverables (Kinross, 1985). Technical communicators, if they are not already doing so, may find themselves managing or participating on multi-discipline teams tasked with the production of these types of deliverables. Multi-discipline teams have the potential to falter because of the obstacles imposed by different disciplinary and organizational perspectives (Wilson & Herndl, 2007). To participate fully as knowledge workers (New London Group, 2000), technical communicators must become functionally literate (Simmons & Zoeteway, 2012) in the theories, technologies, and tools associated with the production of data visuals.

Learning Objectives

Upon complete of this course, students will be able to:

  • LO1: List similarities and differences related to how different academic disciplines teach data visualization. We’ll accomplish this by performing a syllabus review of different college courses. Identifying similarities and differences will help prepare technical communicators to identify the skills necessary to design, develop, deploy data visualizations when part of a multi-discipline team.
  • LO2: Describe how these similarities and differences might affect (both positive and negative) a multi-discipline team working to produce data visualization deliverables. Reflecting on these similarities and differences will help the technical communicators prepare to work on multi-discipline teams.
  • LO3: Describe what skills a technical communicator contributes to a data visualization team. As a technical communicator, it’s important to be able to articulate how you add value to a team. Don’t let others (especially those who are not familiar with your skill set) box you into a skills corner or define your worth.
  • LO4: List at least three industries/fields involved in the production of data visualization. We’ll accomplish this by reviewing practitioner bios, articles, text, etc.
  • LO6: List at least three data visualization resources for continuing education. Resources are provided at the end of this syllabus, but the hope is that participants will embrace this learning community and use this opportunity to share valuable resources with each other.
  • LO7: Share knowledge or experience regarding data visualization with others. Sharing your knowledge is important because teaching/sharing is a good way to reinforce what you have learned. In addition, it provides you with a means to increase your professional visibility.

Required Course Text

Course Components

Learning Assignments

If you are new to asynchronous, online learning, try following the recommended weekly schedule provided below.  At a minimum, give it a try for the first week, and then make adjustments as necessary to accommodate your learning style and situation.

IMPORTANT: The due dates listed on Wednesday, Thursday, and Friday in the following schedule are not recommendations, they are required due dates, and must be met.

Weekly Schedule

At the beginning of each week, the facilitator will post an overview of the week’s activities in a Slack post in the appropriate Slack channel.

This course is being delivered asynchronously, but there are specific due date that must be met in order for the class to progress together. The following is the recommended schedule with the required due dates highlighted:

  • Monday: Review the agenda and start your literature review. Each week, a list of articles or book chapters will be identified in the list of required reading.

Important: Each student must choose one article or book chapter to read and summarize. Claim your article or chapter by posting the title in the classroom discussion forum (i.e., appropriate Slack channel). All articles or chapters must be read and summarized by at least one participant. Therefore, if your first choice is claimed by another student in the discussion forum, claim your second choice.  Remember that you are participating in a learning community, so if you do not provide a useful summary of the article you selected, everyone in the learning community is impacted.

  • Tuesday: Complete the reading and begin your technology review. Each week, we will review a different technology associated with data visualization.
  • Wednesday: Post a summary of the article or the chapter you read (no more than 250 words). Include your personal takeaways. The literature review is due by 12:00am, CST on Wednesday. Read all the summary posts by your fellow learning community members, and comment on no less than two posts. To earn full participation credit, your comments must add additional insight or seek to obtain clarification. For example, your comment might include a thoughtful question that initiates a discussion with the author of the summary.
  • Thursday: Post a technology review (no more than 250 words). The technology review is due by 12:00am, CST on Thursday. Review the responses of the other learning community members and comment on at least two reviews. Again, your comments must add additional insight regarding the technology or seek to obtain clarification from the author of the post.
  • Friday: Post a weekly reflection (no more than 250 words). The reflection post is due by 12:00am, CST on Friday. Here are a few examples of things you might discuss in your reflection post:
  • What are your personal takeaways from this week?
  • Do you feel this week confirmed, expanded, or negated your knowledge/assumptions about data visualization?
  • What additional questions do you have that weren’t addressed or answered?
  • What are you plans for expanding your existing knowledge further on this topic?
  • What plans to you have to expand your knowledge regarding the topics discussed this week?
  • Did you find any resources that you feel are important to share with the learning community?

Final Project

Take this opportunity to share what you’ve learned about dataviz. For example, write and publish a blog post, write a draft of an article you plan to submit to a publication, create a proposal for an upcoming conference, create a draft of content to be submitted to the STC Body of Knowledge (TC-BoK), etc. What you produce is your choice, but keep in mind that the project is a reflection (assessment in this case) of what you have learned while participating in this learning community.

Because each project will be unique, a proposal (nothing formal, just a simple email with a description of what you plan to create and why) is due by 12:00 am, CST Monday of week 3.  You will not receive a grade for the proposal email, but this is your opportunity to receive feedback about the appropriateness of your project. How you addressed that feedback in the final project will impact your final grade.

You can complete the final project on an individual basis or as a team with no more than three members. If you choose to work on a collaborative team, then in addition to the proposal email, you must also submit a team charter that identifies the team members and the agreed upon team member responsibilities. The final project is due by 12:00 am, CST on the last day of class.

Post-Mortem Questionnaire

At the end of each project, it is important to reflect on what went right and what when wrong. You can then use what you learned from the experience for continuous improvement. A project post-mortem is your opportunity for this type of reflection.  In week six, you will be asked to complete a post-mortem questionnaire. The questionnaire is due by 12:00 am, CST on the last day of class.

Questions you will be asked:

  • What went wrong?
  • What went right?
  • What would you do differently?
  • What have you learned from the project?
  • What hindered your progress during the project?
  • What helped you reach deadlines?
  • What one thing about your deliverable would you change?

Grade Book

Assignments Due
Date		 Grade Weight
Week 1: Literature Review Wed by 12:00am 5
Week 1: Technology Review Thu by 12:00am 5
Week 1: Responses to lit and tech reviews and your reflection post Fri by 12:00am 5
Week 2: Literature Review Wed by 12:00am 5
Week 2: Technology Review Thu by 12:00am 5
Week 2: Responses to lit and tech reviews and your reflection post Fri by 12:00am 5
Project Proposal  Due (no grade/ instructor feedback only) Friday by 12:00am —
Week 3: Literature Review Wed by 12:00am 5
Week 3: Technology Review Thu by 12:00am 5
Week 3: Responses to lit and tech reviews and your reflection post Fri by 12:00am 5
Week 4: Literature Review Wed by 12:00am 5
Week 4: Technology Review Thu by 12:00am 5
Week 4: Responses to lit and tech reviews and your reflection post Fri by 12:00am 5
Week 5: Literature Review Wed by 12:00am 5
Week 5: Technology Review Thu by 12:00am 5
Week 5: Responses to lit and tech reviews and your reflection post Fri by 12:00am 5
Week 6: Final Project Post-Mortem Questionnaire Fri by 12:00am 5
Final Project Due Fri by 12:00am 20
Total 100

Scoring Rubric

Assignment Expectations
Literature Review Post Provide an informative summary (no more than 250 words) of the article or chapter selected. In addition, include personal takeaways. Also provide comments on at least two fellow student’s summaries that add insight or seek to obtain clarification from the summary author.
Technology Review Post Provide an informative review (no more than 250 words) of the technology assigned or selected. Also provide comments on at least two fellow student’s reviews that add insight or seek to obtain clarification from the review author.
Reflection Post Provide a thoughtful reflection of what you learned from participation in the week’s learning activities.
Project Create a professional-quality deliverable that shares valuable takeaways from this course. The project should be something that you would be proud to self-publish or something that you would feel confident submitting to a conference committee or journal editor for review and feedback.
Scoring
5-Exceptional: Performance far exceeds expectations due to an exceptionally high quality of work and the work is superior and/or unique.

4-Exceeds expectations: Performance consistently exceeds expectations in all essential areas and the quality of work is excellent.

3-Meets expectations: Performance consistently met expectations in all essential areas and the quality of work is very good.

2-Improvement needed: Performance did not consistently meet expectations and the deliverable has one or more issues that require improvement.

1-Unsatisfactory: Performance was consistently below expectations in most essential areas and the deliverable has multiple issues and requires significant improvement.

*Scale based on Berkley University rubric: http://hr.berkeley.edu/performance/tools/rating-scale

Course Calendar

Part I: Syllabus, Literature, and Technology Reviews

Week 1 2 3
Syllabus Review ENGL 5300: Data Analysis and Visualization
(Texas Tech)		 ARTG 5120 Information Design Research Methodologies
(Northeastern)		 CS 7450 Information Visualization
(George Tech)
Literature Review See the weekly agendas for list of articles.
Technology Review D3 Processing Tableau

Part II: Practitioners Introductions and Best Practices

Week 4 5 6
Practitioner Introduction Data Journalist
Alberto Cairo		 Data Scientists
Cathy O’Neil		 None: Work on Final Project
Literature
Review		 The Functional Art
Ch: 2,3,4,7,8,9		 On Being a Data Skeptic None: Work on Final Project
Technology Review Student Choice Student Choice None: Work on Final Project

Class Policies

Attendance

This is an asynchronous class, so there are no attendance requirements, only participation requirements. You must complete all learning activities on the due dates identified in order to earn credit for participation. If you miss more than two dues dates, you will automatically be dropped from the course.

Late Work

Late work is disruptive in this asynchronous, social learning environment. Therefore, it is highly discouraged. If an exceptional circumstance arises, notify the instructor immediately. It is at the instructor’s discretion to allow submission of late work.  All late work will be assessed with a 15% penalty per calendar day late. Any work submitted more than two days late will receive no credit (zero points). In addition, if you miss more than two due dates, you will automatically be dropped from the course and will be required to register for another session.

Plagiarism

Plagiarism is defined as the improper use of someone else’s text, graphics, coding, or ideas, without proper citation. Assignments that plagiarize will receive no credit (zero points).

Special Accommodations

If you have a disability that requires special accommodation, forward verification of the disability with your accommodation request to the instructor no less than 2 weeks prior to the start of class.

Class Prep

Class Website: Slack Group

Send an email to the instructor requesting to join the class Slack group.

Class Discussion Forum: Slack Channels

Send an email to the instructor requesting to join the weekly Slack channels.

Bios: Introductions

Post a short bio or provide the URL to your LinkedIn profile in the Slack group once you have been added by the instructor.

Comment on the bios posted by your fellow participants (e.g., acknowledge shared interest, geographic locations, common employment history, similar education, etc.)

Textbook

Purchase/download the required textbooks once you receive confirmation of enrollment.

Introduction to Slack

If you are unfamiliar with Slack groups and channels, watch the introduction video: https://slack.com/is.

Week 1 Agenda: Techcomm View of Dataviz

Syllabus Review

Review the following syllabus:

This syllabus should give you an idea of what technical communicators are taught about dataviz. This knowledge impacts their views and actions in a multi-discipline team.

Literature Review

Choose at least one of the following articles to read and summarize:

Technology Review

Review training or documentation for the following application:

Weekly Activity Checklist

  1. (Monday) Review the weekly overview post, which provides a summary of the activities for the week.
  2. (Due Tuesday) Complete your reading assignment and begin your technology review.
  3. (Due Wednesday) Post your literature review summary.
  4. (Due Thursday) Post your technology review summary.
  5. (Due Friday) Post your reflection for the week.
  6. (Continuous/Daily) Comment on the posts of other participants. This is your opportunity to share, encourage, and learn from each other.

Week 2 Agenda: Artist View of Dataviz

Syllabus Review

Review the following syllabus:
This syllabus should give you an idea of what technical communicators are taught about dataviz. This knowledge impacts their views and actions in a multi-discipline team.

Literature Review

Choose at least one of the following articles to read and summarize:

Technology Review

Review training or documentation for the following application:

Reflection Post

Reflect on what you discovered, shared, learned this week.

Weekly Activity Checklist

  1. (Monday) Review the weekly overview post, which provides a summary of the activities for the week.
  2. (Due Tuesday) Complete your reading assignment and begin your technology review.
  3. (Due Wednesday) Post your literature review summary.
  4. (Due Thursday) Post your technology review summary.
  5. (Due Friday) Post your reflection for the week.
  6. (Continuous/Daily) Comment on the posts of other participants. This is your opportunity to share, encourage, and learn from each other.

Week 3 Agenda: Comp Science View of Dataviz

Syllabus Review

Review the following syllabus:

This syllabus should give you an idea of what computer scientists are taught about dataviz. This knowledge impacts their views and actions in a multi-discipline team.

Literature Review

Choose at least one of the following articles to read and summarize:

Technology Review

Review training or documentation for the following application:

Reflection Post

Reflect on what you discovered, shared, learned this week.

Weekly Activity Checklist

Complete the following activities:

  1. (Monday) Review the weekly overview post, which provides a summary of the activities for the week.
  2. (Due Tuesday) Complete your reading assignment and begin your technology review.
  3. (Due Wednesday) Post your literature review summary.
  4. (Due Thursday) Post your technology review summary.
  5. (Due Friday) Post your reflection for the week.
  6. (Continuous/Daily) Comment on the posts of other participants. This is your opportunity to share, encourage, and learn from each other.

Week 4 Agenda: The Data Journalist

Practitioners Bio

Alberto Cairo teaches information graphics and visualization at the School of Communication at the University of Miami. He is also the director of the Visualization Program at University of Miami Center for Computational Science. To learn more about Alberto, visit his Wikipedia site: https://en.wikipedia.org/wiki/Alberto_Cairo

Literature Review

Choose at least one of the following chapters from Alberto’s book:

  • Ch 2: Form and Function: Visualization as a Technology
  • Ch 3: The Beauty Paradox: Art and Communication
  • Ch 4: The Complexity Challenge: Presentation and Exploration
  • Ch 7: Images in the Head
  • Ch 8: Creating Information Graphics
  • Ch 9: The Rise of Interactive Graphics

(Optional) Podcast Review

If you have time, listen to this optional podcast: The Poetry of Propaganda http://www.thisamericanlife.org/radio-archives/episode/575/poetry-of-propaganda)

Technology Review

Student’s choice: Summarize a technology you want to share with the class.

Reflection Post

Reflect on what you discovered, shared, learned this week.

Weekly Activity Checklist

Complete the following activities:

  1. (Monday) Review the weekly overview post, which provides a summary of the activities for the week.
  2. (Due Tuesday) Complete your reading assignment and begin your technology review.
  3. (Due Wednesday) Post your literature review summary.
  4. (Due Thursday) Post your technology review summary.
  5. (Due Friday) Post your reflection for the week.
  6. (Continuous/Daily) Comment on the posts of other participants. This is your opportunity to share, encourage, and learn from each other.

Week 5 Agenda: The Data Scientist

Practitioners Bio

Cathy O’Neil earned a Ph.D. in math from Harvard, was postdoc at the MIT math department, and a professor at Barnard College where she published a number of research papers in arithmetic algebraic geometry. She left academia for the private sector and is currently a data scientist at Johnson Research Labs. To learn more about Cathy O’Neil, visit her Wikipedia site: https://en.wikipedia.org/wiki/Cathy_O%27Neil

Literature Review

Choose at least one of the following chapters from Cathy’s free e-book:

  • Skeptic, Not Critic/Audience
  • Trust Data Too Much
  • Trust Data Too Little
  • The Smell Test for Big Data/The Sniff Test for Big Data/Conclusion

Technology Review

Student’s choice: Summarize a technology you want to share with the class.

Reflection Post

Reflect on what you discovered, shared, learned this week.

Weekly Activity Checklist

  1. (Monday) Review the weekly overview post, which provides a summary of the activities for the week.
  2. (Due Tuesday) Complete your reading assignment and begin your technology review.
  3. (Due Wednesday) Post your literature review summary.
  4. (Due Thursday) Post your technology review summary.
  5. (Due Friday) Post your reflection for the week.
  6. (Continuous/Daily) Comment on the posts of other participants. This is your opportunity to share, encourage, and learn from each other.

Week 6 Agenda: Final Project

Final project

You submitted your proposal on the Monday of week 3, you received feedback from the instructor on your proposal on Friday of week 3, and should have been collecting information and creating content for your project throughout this course. Therefore, use this last week to put the finishing touches on your project.

The project is due at 12:00 am, CST on the last day of class.

Post-Mortem Questionnaire

Answers the flowing questions:

  • What went wrong?
  • What went right?
  • What would you do differently?
  • What have you learned from the project?
  • What hindered your progress during the project?
  • What helped you reach the deadlines?
  • What one thing about your deliverable would you change?

Post Course: Continuing on Your DataViz Journey

Web Articles

Books

Blogs/Websites:

Courses

Resources and Cited Works

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Brewer, C. A. (1999, August). Color use guidelines for data representation. In Proceedings of the Section on Statistical Graphics, American Statistical Association (pp. 55-60). (http://www.personal.psu.edu/cab38/ColorSch/ASApaper.html)

Bostock, M., Ogievetsky, V., & Heer, J. (2011). D³ data-driven documents. Visualization and Computer Graphics, IEEE Transactions on, 17(12), 2301-2309. (http://vis.stanford.edu/files/2011-D3-InfoVis.pdf)

Cairo, A (2014). CVJ 522: Infographics and Data Visualization Syllabus [Class handout]. School of Communication, University of Miami, Miami, FL.

Cleveland, W. S., & McGill, R. (1984). Graphical perception: Theory, experimentation, and application to the development of graphical methods. Journal of the American statistical association, 79(387), 531-554. (http://www.jstor.org/discover/10.2307/2288400?uid=2483694117&uid=2&uid=3&uid=60&sid=21105172987103)

Drucker, J. (2011). Humanities approaches to graphical display. Digital Humanities Quarterly, 5(1). (http://www.digitalhumanities.org/dhq/vol/5/1/000091/000091.html)

Few, S. (2005, November). Effectively Communicating Numbers – Selecting the Best Means and Manner of Display. Retrieved November 15, 2014 (http://www.perceptualedge.com/articles/Whitepapers/Communicating_Numbers.pdf)

Few, S. (2008). Practical rules for using color in charts. Visual Business Intelligence Newsletter, (11). (http://homepages.dcc.ufmg.br/~raquelcm/material/visualizacao/papers/rules_for_using_color.pdf)

Furnas, G. W. (2006, April). A fisheye follow-up: further reflections on focus+ context. In Proceedings of the SIGCHI conference on Human Factors in computing systems (pp. 999-1008). ACM. (http://vis.berkeley.edu/files/chi06/Furnas_p999.pdf)

Harrison, S., Tatar, D., and Sengers, P. (2007, April). The three paradigms of HCI. In Alt. Chi. Session at the SIGCHI Conference on Human Factors in Computing Systems San Jose, California, USA (pp. 1-18). (http://people.cs.vt.edu/~srh/Downloads/HCIJournalTheThreeParadigmsofHCI.pdf)

Hearst, M. (2009). Chapter 10: Information visualization for search interfaces. Search User Interfaces, Published By Cambridge University Press. (http://searchuserinterfaces.com/book/sui_ch10_visualization.html)

Heer, J (2014). CSE 512: Data Visualization Syllabus [Class handout]. Department of Computer Science and Engineering, University of Washington, Seattle, WA.

Heer, J., Bostock, M., & Ogievetsky, V. (2010). A tour through the visualization zoo. Commun. ACM, 53(6), 59-67. (http://homes.cs.washington.edu/~jheer//files/zoo)

Hullman, J., Drucker, S. Riche, N., Lee, B., Fisher, D., Adar, E. (2013). A Deeper Understanding of Sequence in Narrative Visualization. Visualization and Computer Graphics, 19(12), pp. 2406-2415. (http://www-personal.umich.edu/~jhullman/story_sequence_infovis_final.pdf)

Kinross, R. (1985). The Rhetoric of Neutrality. Design Issues, 2(2), pp. 18-30 (http://www.jstor.org/discover/10.2307/1511415?uid=2483694117&uid=2&uid=3&uid=60&sid=21105173114213)

Kosara,  R. (2012, December 26). Visual Math Gone Wrong. Retrieved November 15, 2014. (http://eagereyes.org/criticism/visual-math-wrong)

Lam, H., Bertini, E., Isenberg, P., Plaisant, C., & Carpendale, S. (2011). Seven guiding scenarios for information visualization evaluation. (https://hal.archives-ouvertes.fr/hal-00723057/document)

Lex, A (2014). CS 171: Visualization Syllabus [Class handout]. Department of Computer Science, Harvard University, Cambridge, MA.

Mishra, P. (1999). The Role of Abstraction in Scientific Illustration. Journal of Visual Literacy, 19(2), pp. 139-158. (http://punya.educ.msu.edu/publications/journal_articles/mishra_JVL.pdf)

Moore, K (2014). ENGL 5300: Data Analysis and Visualization Syllabus [Class handout]. Department of English, Texas Tech, Lubbock, TX.

New London Group. (2000). A pedagogy of multiliteracies: Designing social futures. In B. Cope & M. Kalantzis (Eds.), Multiliteracies: Literacy learning and the design of social futures (pp. 9-37). London: Routledge.

Offenhuber, D (2014). ARTG 5120: Information Design Research Methodologies Syllabus [Class handout]. Department of Arts & Graphics, Northeastern University, Boston, MA.

Parke, F (2014). VIST 375: Foundations of Visualization Syllabus [Class handout]. Department of Visual Studies, Texas A&M, College Station, TX.

Segel, E. and Heer, J. (2010). Narrative Visualization: Telling Stories with Data. Visualization and Computer Graphics, 16(6), pp. 1139-1148. (http://vis.stanford.edu/papers/narrative)

Simmons, W. M., & Zoetewey, M. W. (2012). Productive usability: Fostering civic engagement and creating more useful online spaces for public deliberation. Technical Communication Quarterly, 21(3), 251-276.

Shneiderman, B., Plaisant, C. (2006, May) Strategies for evaluating information visualization tools: multi-dimensional in-depth long-term case studies. In Proceedings of BELIV ’06, pp. 1-7 (http://hcil.cs.umd.edu/trs/2006-12/2006-12.pdf)

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Sullivan, P. (1989). Beyond a narrow conception of usability testing. Professional Communication, IEEE Transactions on, 32(4), 256-264.

Toth, C (2014). WRT 2517450: Information Visualization Syllabus [Class handout]. Department Writing, Grand Valley State University, Allendale, MI.

Toth, C. (2013) Revisiting a Genre: Teaching Infographics in Business and Professional Communication Courses. Business Communication Quarterly 74(4), pp. 446-457.

Van Wijk, J. (2005). The Value of Visualization. Proceedings of Visualization ’05, pp.79-86. (https://www.cs.ubc.ca/~tmm/courses/cpsc533c-05-fall/readings/vov.pdf)

Wilson, G., & Herndl, C. G. (2007). Boundary objects as rhetorical exigence: Knowledge mapping and interdisciplinary cooperation at the Los Alamos National Laboratory. Journal of Business and Technical Communication, 21(2), 129-154.

Zer-Aviv, M. (2014, January, 31) Disinformation Visualization: How to Lie with Datavis. Visualizing Information for Advocacy. Retrieved November 15, 2014. (https://visualisingadvocacy.org/blog/disinformation-visualization-how-lie-datavis)