Data Visualization for All course

Data Visualization for All course

Prof. Jack Dougherty, Trinity College, Hartford CT, USA

Educ 206: Data Visualization For All

Spring 2023 Syllabus

Our course meets in-person, wearing face masks in compliance with the campus Covid policy, on Tuesdays & Thursdays 1:30-2:45pm in Seabury S205 at Trinity College, Hartford.

Instructors

Professor Jack Dougherty, Trinity College, Hartford CT. Email me a quick question or schedule a Zoom appointment on my calendar.

Teaching Assistant Keelyn McNamara ‘23 (Tuesdays in class and by appointment)

Research Assistant Shayla Whitaker ‘25 (to compile data on metro Hartford waste)

Course description: How can charts and maps tell meaningful stories? How can they mislead us from the truth? In this introductory hands-on course, we will create data visualizations in order to better understand design principles and develop a critical analysis of the field. Students will learn skills in both quantitative reasoning and digital storytelling as we advance from beginner tools to editing code templates. For the community learning component, our class will build interactive charts and maps on a public policy issue with a Hartford-area partner organization. No coding experience is necessary, but curiosity is required.

  • Fulfills Numerical and Symbolic Reasoning (NUM) requirement.
  • Cross-referenced with Community Learning, Public Policy & Law, Writing & Rhetoric, Urban Studies.
  • Enrollment limited to 19 students.

Community Learning Partners for Crash and Trash Projects

This semester we will work with community learning parters on two data visualization projects: Crash and Trash.

Crash: Grace Yi (Senior Planner, Bike & Ped) and Owen Deutsch (Principal Planner), from the Planning Division, City of Hartford, have asked us to help visualize answers to this question: Which Hartford street intersections and corridors have the highest frequency of motor vehicle crash injuries and fatalities, to help the city prioritize traffic-calming measures?

Intro video for Crash project, Spring 2023 with partners Grace Yi and Owen Deutsch Planning Division, City of Hartford

Trash: Sarah McCoy from the Center for Leadership and Justice in Hartford has asked us to help visualize answers to this environmental racism question: Where does our trash go, whose neighborhoods are affected, and how much can we reduce solid waste?

Intro video for Trash project, Spring 2023 with partner Sarah McCoy, Center for Leadership and Justice

Course Materials and Tools

book covers

  • Two open-access books are freely available online (or you can purchase print editions).
  • Bring a laptop computer (Mac, Windows, or Chromebook) to every class. Install more than one browser (such as Firefox, Safari, Chrome) for testing purposes. Tell me if you need a temporary laptop loaner.
  • You will need to sign up for several free web services (such as Google Drive, Datawrapper, GitHub). See my list of recommended free digital tools in this course.

Learning Goals and Assessments

In this course you will demonstrate the knowledge and skills you have developed to meet 12 broad learning goals (with more specific sub-goals listed in the schedule further below):

  1. Strengthen Your Spreadsheet Skills
  2. Find and Question Your Data
  3. Clean Up Messy Data
  4. Make Meaningful Comparisons
  5. Chart Your Data
  6. Map Your Data
  7. Transform Your Map Data
  8. Edit and Host Leaflet Code on GitHub
  9. Explore Leaflet Map Code Templates
  10. Tell and Show Your Data Story
  11. Embed on the Web
  12. Detect Lies and Reduce Bias

Your progress toward these learning goals will be assessed in three ways:

A. Open-Book Weekly Quizzes

  • The purpose is to demonstrate your knowledge and skills through questions that emphasize conceptual understanding and how to apply it in new contexts. You will have more than one opportunity to successfully complete each quiz while working independently with your notes, readings, and any web resources.
  • Each week, select any 2-hour period during the 3-day window (typically from Friday afternoon to Monday 9pm) to complete a Moodle quiz of around 5 questions on the designated learning goal. You may see different versions of questions than other students, but all questions address the same goal.
  • Students have at least two attempts on every quiz. Short-answer questions will be scored automatically by the computer, and show-your-work questions will be scored manually by the instructor. If you respond incorrectly to an auto-scored question, the quiz will offer advice and allow you to try again. If you respond incorrectly to a manually-scored question, the instructor will offer advice via email or a meeting and allow you to try again within 24 hours.
  • Before you start the quiz, prepare by reading assigned chapters, doing practice exercises, and discussing the material with classmates who have not yet started it.
  • When you start the 2-hour quiz period, you may use your materials (including your notes, the online book, and the entire Internet), but you must work independently and you may not communicate about the quiz content in any way (except with the instructors) until the quiz has closed. You are responsible for following the Intellectual Honesty policy as described in the Trinity Student Handbook. If doubts arise, the instructor may require you to retake a quiz under direct supervision and/or refer the issue to the Honor Council.
  • If you do not begin a quiz or demonstrate sufficient effort during the initial quiz window, a 20 percent late penalty will be deducted for every 12-hour period beyond the deadline. Exceptions are granted only for documented health or family emergencies.
  • 12 broad learning goals x 5 points per quiz = 60 points

B. Building Data Stories

  • The purpose is to show your work in building different stages of data visualizations and written narratives that explain why and how it matters to our community partners.
  • Depending on the stage of the build, your work will be evaluated by the instructor, your peers, or guest experts, based on a rubric.
  • 7 stages x 5 points = 35 points. A 20 percent late penalty will be deducted for every 12-hour period beyond the deadline. Exceptions are granted only for documented health or family emergencies.

C. Class Participation

  • The purpose is to encourage active learning and accountability. Each student begins the course with 5 participation points. During class, you may be randomly called on to discuss a concept in our reading, or to share your computer screen for everyone to view while we learn tools and build data stories. You do not necessarily need to know the correct answer. But if you are not present or not able to participate when called, a point will be deducted from your participation score. Exceptions are granted only for documented health or family emergencies, or scheduling conflicts where you have notified me well in advance.

If you are quarantined or isolated due to Covid, or if you have concerns about your health, email me in advance to request permission to participate remotely via Zoom during a specific class session. Last-minute requests may not be granted.

Summary

60 points quizzes + 35 points data stories + 5 class participation = 100 total points. In this course, unsatisfactory work (below 70%) falls in the D or F range, adequate work (70-79%) in the C range, good work (80-89%) in the B range, and outstanding work (90 to 100%) in the A range. Each range is divided into equal thirds for minus (-), regular, and plus (+) letter grades. For example, 80 to 83.33% = B-, 83.34 to 86.67 = B, and 86.68 to 89.99 = B+. Access your individual assessments on the password-protected Moodle site for this course at https://moodle.trincoll.edu.

Academic accommodations: please notify me before our third class session, and schedule an appointment on my calendar to discuss how we will implement your approved plan. For those students with accommodations approved after the start of the semester, a minimum of 10 days’ notice is required. Learn more at the Student Accessibility Resource Center.

Religious observances or other advance scheduling conflicts: please request accommodations well ahead of time, ideally by our third class session. Last-minute requests may not be granted.

How to Succeed in this Course

  • Bookmark this online syllabus and check it for important updates, which will appear in bold.
  • Keep a calendar (paper or digital) to manage your time and meet deadlines.
  • Bring a laptop (with a fully-charged battery) to every class, and turn off distractions to help you (and others) focus on learning. Set notifications on digital devices to “Do Not Disturb.”
  • Use a password manager to keep track of your digital accounts. See my introduction to Bitwarden, an open-source password manager with free core features for Windows/Mac/Linux computers, all major web browsers, and iOS and Android mobile devices.
  • Take initiative and ask questions: during or after class, via email, or by appointment on my calendar. If you don’t understand something, other students probably are puzzled, too. Go ahead and ask.
  • Meet up with other students outside of class. Create a small study group to review the course material and work together. The secret to success in college is teaching yourselves how to learn new material.
  • If anything is interfering with your learning, email or talk with us about it. We care about how you’re doing in life, not just in our classroom.

Schedule

Important updates from the instructor will be marked in bold.

Thu 26 Jan

  • Bring a mask to wear in our classroom (extras will be available), and bring a laptop computer (or use a desktop in the classroom).
  • If you are enrolled but do not attend our first class, the Registrar will be instructed to drop you from the roster.
  • Overview of the syllabus, Hartford community-learning partners, and past projects
  • Sample quiz about syllabus for 1-point bonus, due in class on Moodle
  • Why data visualization matters in the “disinformation age”. See Hands-On DataViz (HODV), Chapter 1 https://handsondataviz.org/introduction.html
  • What sparks your interest in this course? Anonymous free-write on shared Google Doc
  • Why data visualization matters to me: Telling and showing hidden stories of housing and education inequality in On The Line book-in-progress

Tue 31 Jan

  • Advice from TA on scheduling appointments and how to succeed in this course
  • Overview of Learning Goal #1: Strengthen Your Spreadsheet Skills. Start reading and doing sample exercises in HODV Chapter 2 https://handsondataviz.org/spreadsheet.html
    • Copy, Share, and Convert Google Sheets
    • Geocode locations
    • Sort and filter data
    • Calculate with formulas
    • Summarize with pivot tables
    • Match columns with VLOOKUP
  • Google Spreadsheet Skills Practice
  • Google Username and Sheet Sharing Practice
  • Prep for Spreadsheet quiz, to be taken during any 2-hour period before Mon 6 Feb 9pm on Moodle
  • One day after the quiz window closes, some students may receive an email requesting quiz revisions within 24 hours. If no email arrives, you likely earned full credit, which can be confirmed in your Moodle gradebook.
  • Start reading background news for next meeting with Hartford partners, since Build 1 will ask questions based on reading.

Thu 2 Feb

Tue 7 Feb

Thu 9 Feb

Tue 14 Feb

  • Overview of Learning Goal #3: Clean Up Messy Data. Start reading HODV Chapter 4 at https://handsondataviz.org/clean.html
    • Smart Cleanup in Google Sheets
    • Find and Replace with Blank
    • Transpose Rows and Columns
    • Split Data into Separate Columns
    • Combine Data into One Column
    • Extract Tables from PDFs
      • Hint: When you download and launch the Tabula app, Mac users will need to approve it. Also, the Tabula app opens up in your default browser, so if your default browser does not open it (such as Safari for Mac), copy and paste the link into another browser (such as Firefox or Chrome).
      • Practice Table Extraction from PDFs: CT DEEP Solid Waste and Recycling Data https://portal.ct.gov/DEEP/Reduce-Reuse-Recycle/Data/Solid-Waste--Recycling-Data, see MSW Data for Individual CT Municipalities, Summary Reports (2014 and prior)
  • Prepare for Clean Up quiz, to be taken during any 2-hour period before Mon 20 Feb 9pm on Moodle
  • Up to 3 students may sign up to accompany community partner Sarah McCoy on Trash Photo Mission on new date Tuesday 28 Feb, departing in her car from Mather Circle 12:15pm and returning to campus before our class. In exchange for writing detailed captions for Sarah’s photos, these students will be exempted from Quiz 4: Make Meaningful Comparisons.

Thu 16 Feb

Tue 21 Feb

Thu 23 Feb

  • No class: Trinity Days

Tue 28 Feb

  • Meet as we normally do in our classroom
  • Trash Photo Mission moved to Tues 7 March
  • Before class, create a free account on Datawrapper.
  • Overview of Learning Goal #5: Chart Your Data. Start reading HODV Chapter 6 at https://handsondataviz.org/chart.html
    • Chart types
    • Chart design principles and rules
    • Create bar/column, histogram, line, area charts
    • Create annotated, range, scatter charts
  • Hint: Always test if your work is visible to the public by pasting the link in a private incognito window in your browser OR a second browser, without being logged into your tool account
  • Prepare for Chart Your Data quiz, to be taken during any 3-hour period before Mon 6 March 9pm on Moodle. Plan ahead and budget your time because this quiz contains 5 show-your-work questions and may require more time than you have spent on prior quizzes.

Thu 2 March

  • Assign Build 3: Draw, Pivot, and Create Crash Charts, due in class, worth 5 points (+1 potential bonus point). Discuss your ideas in randomly assigned pairs, but each person must show their work to earn credit.
  • Draw your concept on paper for approval by instructor, then set up pivot table and create chart using this data: 2019-21 Hartford crash sample UConn
  • Finish your builds and submit paper sheet by 1:40 (changed to GDoc by 9pm)

Tue 7 March

  • New date for Trash Photo Mission: Rashaud, Theodora, Ali to ride with partner Sarah McCoy, depart Mather Circle at 12:15pm, return to campus for class, with exemption from Quiz 4 Meaningful Comparisons
  • Overview of Learning Goal #6: Map Your Data. Start reading HODV chapter https://handsondataviz.org/map.html
    • Map types (focus on point, symbol point, and choropleth maps)
    • Map design principles and rules
    • Choropleth colors and intervals (focus on basic concepts for now)
    • Normalize choropleth map data
    • Use Datawrapper to create point, symbol point, and choropleth maps
  • Prep for Map Your Data quiz, to be taken during any 3-hour period by Mon 13 March 9pm on Moodle.

Thu 9 March

Tue 14 March

  • Class cancelled due to my illness

Thu 16 March

Tue 21 March

  • No class: Trinity spring break

Thu 23 March

  • No class: Trinity spring break

Tue 28 March

  • Overview of Learning Goal #8: Edit and Host Leaflet Code on GitHub. Start reading HODV Chapter 10 https://handsondataviz.org/github.html
    • Analogy: GitHub Code Repository is similar to a Public Recipe Cookbook
    • Copy, Edit, and Host a Simple Leaflet Map
  • Prep for Leaflet Code Editing quiz, to be taken during any 2-hour period by Fri 31 March 9pm (new time to avoid two quizzes due on same date) on Moodle.
  • Start thinking about your preferences for final projects (Builds 4-7). No more than two people per team, but some projects can be divided for two separate teams.
  • Review Build 1: Hartford Partner Requests
    • Crash projects
      • A) Update existing open-source heat map of Hartford crash data for 2015-present
      • B) Which Hartford corridors have had most crashes recently, by type?
      • C) Which Hartford intersections have had most crashes recently, by type?
      • D) Have crashes changed in locations where traffic-calming has been installed?
    • Trash projects (to review on Thursday)
      • E) Where does our trash go? Storymap of recyclable, compostable, and disposed waste
      • F) Whose neighborhoods are most affected by trash disposal process?
      • G) How much trash do Hartford-area residents estimate they create, and is it accurate?
      • H) What percentage of Hartford-area trash can be recycled or composted, and what would be key measurable outcomes of doing so?

Thu 30 March

  • Overview of Learning Goal #9: Explore Leaflet Map Code Templates. Start reading HODV Chapter 12 https://handsondataviz.org/leaflet.html
    • Leaflet Storymaps with Google Sheets
  • Prep for Leaflet Map Code Template quiz, to be taken during any 2-hour period by Mon 3 April 9pm on Moodle.
  • Submit final project preferences in Google Form for Builds 4-7, with final decisions made by instructor to balance numbers. Priority will be given to students who wish to partner with another student.

Tue 4 April

  • Thanks for sharing more about yourselves in your Leaflet Storymaps
  • Everyone can view each other’s GitHub repos: see links at top of Moodle
  • Assign Build 4: Show Your Plan and Preliminary Visualization, due on the Google Doc by Wed 5 April 11pm, worth 5 points. Be prepared to give a 1-minute presentation in our next class
  • Workshop time for Build 4, and group meetings with instructor and TA

Thu 6 April

  • Review Build 4: One-minute presentations of your plan and preliminary visualizations. Instructor will send evaluation by Friday morning.
  • Overview of Learning Goal #10: Tell and Show Your Data Story. Start reading HODV Chapter https://handsondataviz.org/story.html
    • Build Narrative on a Storyboard
    • Draw Attention to Meaning
    • Acknowledge Sources and Uncertainty
  • There is no separate Moodle quiz, because this content is combined with the Build below.
  • Assign Build 5A: Data Story Draft A: Tell a Meaningful Data Story on your assigned project, with around 500-750 words plus visualizations, for peer review. Due by Tues 11 April 1pm as a Google Doc shared for anyone to comment with links to related data files and/or visualizations, worth 5 points (in place of a quiz). Be sure to include:
    • Introduction: What is the question and why does it matter?
    • Findings and Evidence: Tell us key insights (text), then show us evidence (visuals)
    • Sources and Methods: Describe how you did the work and any questions in the data, with links
    • Address these peer-review criteria for use in our next class
  • See sample finished data stories by students on past projects

Tue 11 April

  • Before class: Make sure your Google Doc is shared for anyone to comment
  • In-class: Practice applying evaluation criteria to data stories by students on past projects
    • For your assigned data story, read the introduction and skim the rest
    • Bonnie LaBonte and Lachlan Clark, “Hartford Housing Legislation and When Neighborhoods Will Be Impacted,” DataVizForAll data story, December 2021, https://datavizforall.org/clj-labonte-clark/
    • Anna Tran, “How is funding distributed in the Hartford-Region Schools for Students with Disabilities,” DataVizForAll data story, December 2022, https://datavizforall.org/clj-tran/
  • In-class: Do Build Stage 5B: Peer Review with evaluation criteria of an assigned Data Story, due during class, worth 5 points. Students must submit a full draft in order to participate in the peer review. Choose to either type comments into a shared Google Doc or hand-write on paper. Share copy with instructor who will evaluate the quality of constructive feedback.
  • Assign Build 5C: Revise Data Story Draft C: Improve draft using peer feedback Use peer feedback for evaluation by instructor, due Wed 12 April 11pm. worth 5 points.
  • Checklist for strengthening your Build 5C data stories:
    • Look at your current draft through the lens of the evaluation criteria
    • Craft a catchy and meaningful title that draws in readers
    • Contextualize the broader problem and why it matters to our partner as a way to introduce your specific question. Write for readers who are curious but are not yet familiar with the issue.
    • In each body paragraph, tell readers one of your key insights, then show us the evidence, both in text and visuals. Describe to readers what stands out in your tables, charts, or maps.
    • For each visualization, add a screenshot and link to live version
    • Write about your sources & methods so that future DataViz students could replicate your work, with links to public spreadsheets or repositories.
    • Acknowledge uncertainty and question your data. What remains unanswered?
  • Prioritize your to-do list of revisions for 5C

Thu 13 April

  • Effective today, face masks are optional in class
  • Overview of Learning Goal #11: Embed on the Web. Start reading Chapter 9 https://handsondataviz.org/embed.html
    • Static images versus interactive iframes
    • Get the iframe embed code for a published visualization
    • Embed the iframe code in an HTML web page
    • Make your own copy of dataviz-story-template from GitHub and follow instructions inside the index.html file
    • Start Embed on the Web quiz, due by Mon 17 April 9pm on Moodle.
  • Assign Build Stage 6A & 6B: Submit complete draft of your data story, as a published GitHub Pages link (preferred) or Google Doc (acceptable), for comments (no scores) by community partners. Now due Wed 19 April 5pm, for instructor to send to community partners.

Tue 18 April

  • Workshop to finish data stories in preparation for community partner meetings
    • 5-minute meetings with instructor to review content or HTML formatting
    • 5-minute meetings with TA to rehearse your 2-minute presentations of highlights in your data story on GitHub
    • Everyone: Finish moving text and visualizations from Google Doc to GitHub format
  • Build Stage 6A & 6B, due Wed 19 April 5pm for instructor to send to community partners

Thu 20 April

  • Deliver 2-minute presentation of highlights in your data story to community partners, then listen to feedback and write down revisions to consider. Comments only, not graded.
  • Build 6A: Meet with Crash community partners (Grace Yi and Owen Deutsch) on Zoom from 1:30-2:10pm.
  • Build 6B: Meet with Trash community partner Sarah McCoy in-person from 2:10-2:45pm.

Tue 25 April

  • Assign Build Stage 7: Revise and Finalize Data story on GitHub Pages template for guest expert, Monday 1 May at 12 noon. No extensions because your data story will be “frozen” and a copy will be transferred and published on our public Partners and Projects page, and forwarded to our guest experts, who need to read your work before your presentations. Worth 10 points, scored by guest expert.
  • Workshop time and assigned meetings with TA and instructor (see email list)
  • Carefully revise based on Build 5C feedback (emailed from instructor) and Build 6 feedback (from community partner)

Thu 27 April

  • Everyone: Insert your GitHub Pages data story link (even if it’s not finished) in Build 7, and you can improve and revise until Monday 12 noon cut-off
  • Decisions to make before your data story is “frozen” and published on the public Partners and Projects page:
    • Insert your full name (recommended), or you have the right to use only your first name or initials
    • See what other students did in my “Public Writing and Student Privacy” chapter of Web Writing book (2015).
    • Recommended: Insert link to your published data story in your resume to demonstrate what you learned to prospective employers and graduate schools. Emphasize your data visualization skills at the top, instead of simply listing “proficient in Google Sheets, Datawrapper, GitHub” at the bottom.
  • Keep in touch: See my Advising page on “How to request a reference or recommendation letter”
  • Workshop time and assigned meetings with instructor

Tue 2 May

  • In class: Student course feedback on what helped you learn. In addition, please fill out the College’s standard online evaluation form when available.
  • Review lineup of Build 7 final presentations with guest evaluators
  • Overview of Learning Goal #12: Detect Lies and Reduce Bias. Start reading HODV Chapter 14 https://handsondataviz.org/detect.html
    • How to Lie with Charts
    • How to Lie with Maps
    • Recognize and Reduce Data Bias
    • Recognize and Reduce Spatial Bias
  • Prep for Detect Lies quiz, to be taken during any 3-hour period by Mon 8 May 9pm on Moodle. NEW: This bonus quiz will replace your lowest 5-point score on any prior quiz or build. But you receive only ONE attempt.

Wed 3 May

  • Since the College decided that our last “Thursday” class meets on Wed 3 May, make sure that you do not have any schedule conflicts.
  • Class will begin at 1:15pm and end at 2:45pm and will take place in my Zoom room to ensure time for all presenters to interact with guest experts. Students must log into my Zoom room a few minutes before their designated time.
  • Build 7: Present Final Data Stories to guest experts, Taylor Johnston (Interactive Graphics Reporter, CT Insider) and Ilya Ilyankou (Trinity ‘18 and co-author of https://HandsOnDataViz.org). Both will review final data stories online before the event. Students will have up to 2 minutes to orally present highlights, then 4-5 minutes to respond to questions from evaluators about your data analysis, storytelling, and design decisions. Worth 10 points, scored by guest evaluators.