Course Description: In this course, we will explore effective techniques for creating data visualizations through graphic design, information science, cognitive science, and the humanities. This course is designed to be accessible to students with limited programming knowledge. The course will focus primarily on Tableau, but there will be opportunities for more advanced students to Python, but we will also incorporate some R-Language. It will include a considerable amount of discussion, critique of design (both external and student), and some hands-on experience with data visualization.
Required Texts:
Kieren Healy, Data Visualization: A Practical Introduction
Lupi, Dear Data
Additional readings will be posted on 2U
Software:
You will receive an invitation to join the Tableau website we will work on this semester. Tableau will be the default software for the course. Students with existing programming experience will have the opportunity to use R or Python.
You should also install OpenRefine, which is a web-based open source software that’s used to clean data.
Modules run from Monday to Sunday. The dates for the modules are listed on the syllabus and also on the course site. ALL written assignments (visualizations, discussion posts, etc) are due by 11 pm your time each Sunday.
Moodule 1: Understanding Data Visualization (8-13 Sept)
Reading: Healey, Intro & Ch. 1; Dear Data, Intro & Week 1; Jarke van Wijk, “The Value of Visualization, [On 2U].
Activities: Lecture, The Minard Paradox; and watch the
Lynda Tableau Essentials 2020 Chapter 1.
Discussions:
- Write a response of at least 100 words that explains why visualization is important, and what you think its limitations might be [as you understand them].
- Pick one visualization from the Tableau visualization lists posted on 2U and write a critique.
Assignment: Drawing from Intro & Ch. 1 of Dear Data, please create your own visualization. Keep track of the major activities during your week, and how much time you spend doing them, and create a chart (pie or graph) that illustrates the demands on your time. You may do this as a drawing, as seen in Dear Data.
Week 2: Statistics, Data, and Data Bias (14-21 Sept)
Readings: [on 2U] Lupi, Ch. 2; Perez, Invisible Women, Intro & Ch. 10; Lange, “Can Data Be Human?,” Tracking COVID Project, Exploratory Data Analysis [Wikipedia]
Activities: Lecture, Lynda Tableau Essentials 2020 Chapter 2.
Assignment: Drawing from Ch. 2 of Dear Data, please create your own visualization. Consider how you might encounter bias and data in your own life, whether directed at you, or someone else. You may do this as a drawing, as seen in Dear Data.
Discussions:
- Why do statistics and data sampling matter in data visualization?
- How might bias impact the outcome of a visualization?
- Post your Dear Data assignment with a brief explanation.
Week 3: Telling Stories with Data (22-28 Sept)
Readings: Lupi, Ch. 3; Healy, Ch. 2; Knaflic, Storytelling With Data, Ch.7; And [On 2U] Tufte, Envisioning Information, Ch. 2; Exploratory Data Analysis [Wikipedia]
Activities: Lecture, Lyndra Tableau Essentials 2020, Chapter 3
Assignment: Drawing from Ch. 3 of Dear Data, please consider the ways that you might use data in storytelling, and create a small visualization. You may do this as a drawing, as seen in Dear Data.
Discussion:
- What are data-driven stories?
- What are some of the critical points that a data scientist needs to keep in mind when designing a data-driven story?
- Post your Dear Data story with a brief explanation.
Week 4: Data Cleaning (28 Sept-4 Oct)
Readings: Chen, Ch. 6; Structured vs. Unstructured Data; Grammar of Graphics, Ch. 2 [Github]; Wickham, Tidy Data [PDF]; Optional, for advanced students: Hould, Tidy Data in Python [PDF]; Data Wrangling with Pandas Cheat Sheet [PDF]
Activities: OpenRefine Demo & Lynda Tableau Essentials 2020, Chs. 4 & 5
Assignment: Data cleaning exercise, using Open Refine, or, if you are comfortable with Python, you may use Python to work through one of the data cleaning exercises in Wickham or Hould.
Discussion:
- What is the difference between tidy data and untidy data?
- What sorts of conditions might produce untidy data, and how does cleaning fix it?
Week 5: Proportions and Data Relationships (5-11 Oct)
Readings: Lupi, Ch. 4, Yau, Ch. 4 & 5; On Moodle: Lima; Visualizing Complexity, Ch. 1; Link: Levels of Measurement [Wikipedia]
Activities: Lecture, Lynda Tableau Essentials 2020, Chs. 6 & 7
Assignment: Drawing from Ch. 3 of Dear Data, please create your own visualization. Focus particularly on proportions in the ways you visualize your data.
Discussion:
- Find a data visualization that you believe makes poor use of proportions. Link to it or post a screenshot, and offer a brief critique. What would you change?
- Find a data visualization that makes good use of proportions. Link to it or post a screenshot, and offer a brief critique.
Week 6: Interactivity and Animation (12-18 Oct)
Readings: Lupi, Ch. 5, [2U] Bermudez, “Data Representation Architecture;” Robertson et al, “Effectiveness of Animation in Trend Visualization” [PDF]; Heer, et al, “Animated Transitions in Statistical Data Graphics” [PDF]
Activities: Lecture and Lynda Tableau Essentials 2020, Ch. 8 & 9
Discussion:
- Find an interactive data visualization that you believe is poorly designed. Link to it and offer a brief critique. What would you change?
- Find an interactive data visualization that you think makes good use of interactivity or animation. Link to it and offer a brief explanation.
- When does it make sense to use interactivity or animation techniques?
Week 7: Lab Week Visualization Project 1 (19-25 Oct)
No reading. We will have our live Zoom Discussion, and some additional drop in times to discuss your projects this week.
Assignment: Data Visualization 1 is due by the end of this module.
Week 8: Color (26 Oct – 1 Nov)
Readings: Lupi, Ch. 6, Healy, Ch. 8; [On 2U] Cynthia Brewer, “Color Use Guidelines for Data Representation” (1999); Brewer, Selecting Good Color Schemes for Maps; 5 Tips on Designing Colorblind-Friendly Data Visualizations.
Activities: Lecture and Lynda Tableau Essentials 2020, Chs. 10 & 11.
Assignments: Drawing from Dear Data and the Brewer readings, create a data visualization that makes good use of color.
Discussion:
- Find a data visualization that you think makes good use of color and post a screenshot or link to it. Write a brief critique.
- Find a data visualizaton that you think makes poor use of color and post a screenshot or link to it. Write a brief critique.
- Dear Data Assignment
Week 9: Spatial Relationships, Mapping, and Cartography (2-8 Nov)
Readings: Lupi, Ch. 7; Healy, Ch. 3-4; [2U] Brewer, Designing Better Maps, Ch. 2; Dent, Cartography, Ch. 10; Janelle Legg, The Church Mission; Angel . David Nieves, Mapping Soweto. Optional for Advanced Students: Lonij, Interactive Maps with Python, Part 1, Part 2; and Part 3.
Activities: Lecture and Lynda Tableau Essentials 2020 Chs. 12 & 13.
Activities: Drawing on Dear Data and the Brewer and Dent readings, create a data visualization that uses spatial relationships or mapping.
Discussion:
- Find a data visualization that you think makes good use of geography or cartography and post a screenshot or link to it. Write a brief critique.
- Find a data visualization involving geography, cartography, or spatial relationships that you think is poorly-executed. Write a brief critique.
- Dear Data Assignment.
Week 10: Multidimensional Data (9-15 Nov)
Readings: Stolte, et al, “Polaris: a System for Query, Analysis, and
Visualization of Multidimensional Databases” [PDF]; Etemadpour, et al,
“Choosing Visualization Techniques for Multidimensional Data” [PDF];
Optional for Advanced Students: VanderPlas, “Three-Dimensional Plotting in
Matplotlib” [html]
Activities: Lecture
Discussion:
- Consider what we have already learned about proportion, interactivity, and now multidimensional data. How are these concepts related?
- Find a data visualization involving multidimensional data that you think is rendered well, and link to it. Write a brief critique.
- Find a data visualization involving multidimensional data that you think is rendered poorly and link to it. Write a brief critique.
Week 11: Lab Week/Data Visualization 2 (16-22 Nov)
No reading or discussion is due. We will have our Live Zoom Session, and there will be additional opportunities for feedback.
Assignment: Visualization 2 is due at the end of this module. You should also submit a brief abstract explaining what you intend to do for your final project.
Week 12: Data Visualization/3D (23-29 Nov)
Readings: Szabo, “Knowledge in 3D,” [html]; McLaughlin, “Visualization Data in
3D” [html]
Discussion:
- What are are some advantages of using a 3D visualization? When might it be a hindrance to good design?
- Find an example of a good 3D visualization, and post a link to it. Provide a brief critique
- Find an example of a 3D visualization that you think is poorly-executed and post a link to it. Provide a brief critique.
Week 13: Lab Week/Final Project (30 Nov – 6 Dec)
No reading or lecture. We will have our usual weekly live Zoom session, and there will be additional opportunities for feedback. I will make individual check-in appointments with each of you to give you some feedback ahead of your presentation. A nearly completed final project should be finished by the end of this module.
Week 14: Presentations (6-13 Dec)
This week, you will present your visualizations to your classmates. If you’re unable to join the live Zoom sessions (we will use most of our 3 hours this week), please contact me so that we can make arrangements for you to deliver your presentation asychrnonously.
FINAL VISUALIZATION PROJECT is due Dec. 17th.
Engagement (20%): The expectation is that students will be prepared and intellectually present each week, and contribute productively to some combination of asynchronous discussion forums, live Zoom sessions, and virtual “lab” activities Students who are unable to participate in the live Zoom sessions, but who participate regularly in the asynchronous chats and other asynchronous activities will not be punished. Students may call into the Zoom sessions with their phones, and are not required to have their screen on if they have reasons it is an obstacle to their access. If you begin having difficulties accessing the course, please be in touch as soon as possible. Disappearing will limit my ability to help.
Data Cleaning Exercise (10%): Choose and work through one of the data cleaning exercises from your readings on Sept. 26. The assignment will be due by the start of class.
Visualization 1 (15%): Select a dataset from the sample sets on 2U, and determine a question that you would like to ask of that dataset. Create an exploratory data visualization that addresses your question. You may use Tableau. More advanced students may use Python or R-Studio with prior approval by me. Be prepared to provide a thorough explanation of your design decisions.
Visualization 2 (15%): Select a dataset from the sample sets on 2U, and determine a question that you would like to ask of that dataset. Create an exploratory data visualization that addresses your question. You may use Tableau. More advanced students may use Python or R-Studio with prior approval by me. Be prepared to provide a thorough explanation of your design decisions.
Dear Data (15%): You will have some informal data viz exercises that are intended to get you thinking about how much data impacts us. These exercises can be done as simple charts and graphs or hand drawn. You can post them as a screenshot in the “Dear Data” fora.
Final Project (25%): The final project will entail the implementation of the skills you have learned in this class. Be creative. You can use the Edward Tufte, Steven Braun, Cole Nussbaumer Knaflic sites for inspiration and project ideas, but pick a project that you can execute in a few weeks. Students may work in groups of up to 3. You may use Tableau, Python, R-Studio, or D3, or another data viz software with prior approval by me. Be prepared to provide a thorough explanation of your design decisions. You will present your projects in the final week of class.
