Data Visualization for All course

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

Fall 2024 Syllabus

Our course meets in-person on Mondays and Wednesdays 11:30am-12:45pm in Library B02 (basement level, first room near the stairwell) 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 Nellie Conklin - contact and scheduling info on Moodle

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 Project

Our project and Hartford-area community learning partners this semester:
Affordable Homeownership: Cori Mackey, Tieasha Gayle, and Bea Santiago of the Center for Leadership and Justice and its Urban Suburban Affordables, Inc. (USA) community land trust. See history of this homeownership program. The CLJ has asked us to help visualize answers to these broad questions:

  1. Where are USA community land trust homes located in the Hartford region, and in what types of neighborhoods?
  2. How have USA homeownership trends and sales prices changed over time?
  3. How much homeownership wealth has been created by the USA program?
  4. How do USA homes measure up to block/neighborhood comparison groups (comps)?
  5. How does the USA program compare with other community land trusts for affordable homeownership across Connecticut and the nation?

Intro video for CLJ USA community land trust affordable housing project, Fall 2024

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. Detect Lies and Reduce Bias
  10. Explore Leaflet Map Code Templates
  11. Tell and Show Your Data Story
  12. Embed on the Web

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 quiz window (typically a four-day window from Wednesday afternoon to Sunday 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 can make at least two quiz attempts during the typical four-day window. 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 before the quiz window closes, if sufficient time remains.
  • HINT: Start your quiz by Friday 12 noon if you want to receive the instructor’s feedback on show-your-work questions before the quiz window typically closes on Sunday 9pm. If you wait until the last minute and don’t start your quiz until Saturday or Sunday, you may not receive feedback from the instructor in time to do a second attempt.
  • 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 typical 2-hour quiz period, you may use your materials (including your notes, the online book, and any Internet content or tools), 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. See the Intellectual Honesty and Artificial Intelligence policy further below.
  • If you do not begin a quiz or demonstrate sufficient effort during the 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 learning goals x 5 points per quiz (but no quiz for Goal #11) = 55 points

B. Building Data Stories

  • The purpose is to show your original work in building different stages of data visualizations and written narratives that explain why and how it matters to our community partners. See the Intellectual Honesty and Artificial Intelligence policy further below.
  • Depending on the stage of the build, your work will be evaluated by the instructor, your peers, or guest experts, based on a rubric.
  • 8 stages x 5 points = 40 points. A 20 percent late penalty will be deducted for every 12-hour period beyond the deadline, with exceptions 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, one point will be deducted each time from your participation score.
  • Exceptions will be granted for any pre-scheduled absences (such as religious observations or any pre-planned events, if you email me at least one week ahead of time), or health or family emergencies (if you email me at least one hour before class). If you have health concerns that prevent you from attending class in person (such as Covid or other illness), email me at least one hour before class, and if feasible, request permission to participate remotely. Learning on Zoom is not as good as learning in real life, but it’s better than not attending at all. Last-minute requests will not be granted.

Summary

55 points quizzes + 40 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.

Intellectual Honesty and Artificial Intelligence

  • You are responsible for following the Intellectual Honesty policy as described in the Trinity Student Handbook.
  • Artificial intelligence tools are not prohibited in this course, but think carefully before using any technology that might interfere with your learning. Large Language Models (LLMs), such as ChatGPT by OpenAI or Claude by Anthropic, can be helpful for repetitive coding tasks or proofreading paragraphs you’ve already composed. But LLMs also create plausible-sounding text with invented facts and bad calculations. LLMs also raise ethical questions about unpaid labor, energy usage, harmful biases, and safety for humanity. Don’t cheat yourself out of an education by substituting LLM-generated responses in place of authentic learning.
  • For online quizzes, you may use your materials (including your notes, the online book, and any Internet content or tools), 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. If any doubts arise, the instructor may require you to show your work, retake a quiz under direct supervision, or refer a case to the Honor Council.
  • For build assignments, You (and your partner) are expected to create all original content (text and visualizations), with proper citations to any paraphrased or quoted text written by others, image credits to any visuals produced by others, and a methods section that describes all tools used and how. If any doubts arise, the instructor may require you to show your work, or independently reproduce content under direct supervision, or refer a case to the Honor Council.

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.

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 me. I care about how you’re doing in life, not just in our classroom.

Schedule (to come)

  • Important updates from the instructor will be marked in bold.

Wed Sept 4

  • 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.
  • Foldable name cards: write your preferred first name on both sides, and on the inside, add a unique detail about yourself to help me remember your name. I will collect them at the end of each class.
  • Overview of the syllabus, Hartford community-learning partners, and past projects
  • Advice from TA on scheduling appointments and how to succeed in this course
  • Sample quiz about syllabus for 1-point bonus, with open-note format, due in class on Moodle
  • Fill out simple form to share the Google Drive username (aka Gmail address) that you will use for this course
  • Presentation: Why data visualization matters in the “disinformation age”. See Hands-On DataViz (HODV), Chapter 1 https://handsondataviz.org/introduction.html
  • In-class: What sparks your interest in this course? Anonymous free-write on shared Google Doc
  • About me: How did I learn about data visualization? Telling and showing hidden stories of housing and education inequality in On The Line book-in-progress
  • Strengthen your spreadsheet skills: Starting reading and working on sample exercises before our next class listed below.

Mon Sept 9

  • Before class, fill out quick survey about prior experience and education, then view 3000+ results in public sample dataset
  • Overview of Learning Goal #1: Strengthen Your Spreadsheet Skills, HODV Chapter 2 https://handsondataviz.org/spreadsheet.html
    • Spreadsheet terms, tools, and data formats
    • Download, Copy, Share, and Convert Google Sheets
    • Geocode locations
    • Sort and filter data
    • Calculate with formulas
    • Summarize with pivot tables
    • Match columns with VLOOKUP
  • Prep for Spreadsheet quiz, to be taken during any 2-hour period before Sun 9pm on Moodle.
  • Hint: Start your quiz by Fri 12 noon to ensure sufficient time for instructor feedback, as I might email you during the quiz window with advice on revising your quiz, but only if you start early! If you do not receive an email, check your Moodle gradebook to see if you earned full credit.
  • If you have questions about this week’s quiz, see our TA, or schedule an appointment on my online calendar, or drop in to my Zoom room on Friday 11:30am-12noon, no appointment necessary

Wed Sept 11

Mon Sept 16

Wed Sept 18

  • Before class, read background and write questions to prepare for today’s meeting:
  • Assign random pairs to Build Stage 1: Listen, Find & Question Data, Paste the link to your co-authored starter Google Doc on our Build 1 page and share it so that anyone can comment before you leave the room. Due on Sun Sept 22nd at 9pm (same deadline as the quiz, so plan ahead), worth 5 points.
  • In-class: Prepare and ask questions and take notes while meeting with community-learning partners on Zoom from 12-12:30pm
    • Cori Mackey, Executive Director, Center for Leadership & Justice
    • Tieasha Gayle, Data Manager and USA Program Coordinator, CLJ
    • Lucy Berrios-Taveras, Director of Operations, CLJ
    • Bea Santiago, Greater Hartford Interfaith Action Alliance Organizer
  • Open the Find and Question Data quiz, with deadline reminders.

Mon Sept 23

  • Overall good work on Build 1, and beware of bad or questionable data. Instructor has “frozen” copies of your work and will email feedback soon.
  • 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 (instructor demo)
  • Prepare for Clean Up quiz, to be taken during any 2-hour period before Sun 9pm on Moodle
  • Outreach Opportunity #1: Register for Wed Sept 25th 6:30pm housing film and panel discussion - see details below fighting-for-home

Wed Sept 25

  • Review pivot table skills and revise town spellings in USA Properties restricted data
  • In-class: Social Explorer Tutorial to Download and Clean Census/American Community Survey for CT town AND tract data
    • What levels of US Census data are publicly available? See HODV chapter
    • How is US Census data “socially constructed”? See HODV chapter
  • Assign random pairs to Build 2: Find, Clean, Question, and Match Census Data. Write responses in your co-authored and publicly shared Google Document, and include the link to your co-authored and publicly shared Google Sheet. Due Sunday Sept 29th 9pm (same deadline as the quiz, so plan ahead) worth 5 points.
  • Open the Clean Data quiz on Moodle, with reminders on how to meet with instructors
  • Outreach Opportunity #1: Register and attend tonight’s 6:30pm film and panel discussion at Cinestudio, “Fighting for Home: How Housing Policy Keeps Connecticut Segregated.” Say hello at me at the event, then email me one specific paragraph about something you notice in the film, plus one specific paragraph about how a panelist responded to a question, by Friday Sept 27th at 12 noon, and you will be exempted from Quiz #10 later this semester.

Mon Sept 30

  • Personal update: Coach for non-profit orgs with CTData.org
  • Coaching advice:
    • Start quizzes before Friday 12 noon to receive feedback for second attempt
    • Borrow or buy a $15 external mouse to right-click more easily on spreadsheets
    • Practice downloading Census tract data: the non-required 2nd half of Build 2
  • Overview of Learning Goal #4: Make Meaningful Comparisons. Start reading HODV Chapter 5 at https://handsondataviz.org/comparisons.html
    • Decipher common US phrases:
      • Apples-to-apples comparison
      • Don’t cherry-pick your data
    • The big question: “Compared to what?”
    • Precisely describe comparisons: practice in this Google Sheet
    • Normalize your data
    • Beware of biased comparisons
  • Prepare for Meaningful Comparisons quiz, to be taken during any 2-hour period before Sun 9pm on Moodle

Wed Oct 2

  • 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
  • Review Build 2 (evaluations done, need to email links and insert grades)
  • Assign random pairs to Build 3: Compile Assessor and Zillow URLs and IDs for USA Properties. Start link to your co-authored Google Sheet, set to anyone can view, and insert on our Build doc. Due Sunday Oct 6th 9pm (same deadline as the quiz), worth 5 points.
  • Open the Meaningful Comparisons quiz

Mon Oct 7

  • Before class, create a free account on Datawrapper. Review: Compare student strategies on Covid normalization question
  • Continue overview of Learning Goal #5: Chart Your Data. Finish reading HODV Chapter 6 at https://handsondataviz.org/chart.html
    • Compare Datawrapper versus Google Charts
    • Review line versus area charts with Datawrapper
    • Create annotated, range, scatter charts with Datawrapper
  • Hint: When publishing your visualization, 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.
  • In class: Practice choosing the most appropriate chart type and design it
  • Plan ahead and budget your time for the Chart Your Data quiz, to be taken during any 3-hour period before Sun 9pm on Moodle, because it contains 5 show-your-work questions and may require more time than you have spent on prior quizzes. Start your quiz by Friday 12 noon if you wish to receive feedback for a second attempt. If you have questions, visit our TA’s office hours, or schedule an appointment on my calendar, or come to my Zoom Room drop-in (TBA).

Wed Oct 9

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Mon Oct 14

No class - Trinity Days

Wed Oct 16

  • Continue overview of Learning Goal #6: Map Your Data, https://handsondataviz.org/map.html
    • Map types (more about choropleth maps)
    • Choropleth colors and intervals
    • Normalize choropleth map data
  • In-class: Map practice
  • Prepare for 3-hour Map your Data quiz, due Sunday 9pm on Moodle. Start by Fri 12 noon to receive feedback in time for a second attempt.

Mon Oct 21

Wed Oct 23

  • Assign random pairs to Build 4: Progress Reports for Community Partners. Start your co-authored Google Docs, with links to any Google Sheets, and share them so that anyone can comment, on our Build doc. Due Mon Oct 28th 9pm, worth 5 points.

Mon Oct 28

  • Before class: create a free account on Github. Use a relatively short or simple username that you may wish to share professionally in the future.
  • Overview of Learning Goal #8: Edit and Host Leaflet Code on GitHub. Start reading HODV Chapter 10 https://handsondataviz.org/github.html
  • Prep for Leaflet Code Editing quiz, to be taken during any 2-hour period by Sun 9pm on Moodle.
  • In-class: Finalize Build 4 Progress Reports for Community Partners, due Monday 9pm, in order to forward them one day before our meeting

Wed Oct 30

  • In-class: Progress Report with Center for Leadership & Justice community partners on Zoom, 11:40-12:30
  • Start thinking about your preferences for final projects (Builds 5-8). Working in duos is strongly encouraged and priority will be given to students who wish to partner with another student. No more than two students per team, but some projects can be divided into coordinated separate teams (working on related questions and data, but graded separately). While you can choose to work solo, you will be held to the same expectations as a duo.

Mon Nov 4

  • Overview of Learning Goal #9: 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 Sun 9pm on Moodle.
  • Submit your preferences for final project (Builds 5-8) in Google Form (TODO), with final decisions made by instructor to balance numbers.

Wed Nov 6

  • Brief overview of Learning Goal #10: Explore Leaflet Map Code Templates. Start reading HODV Chapter 12 https://handsondataviz.org/leaflet.html
    • Leaflet Maps with Google Sheets
    • Leaflet Storymaps with Google Sheets
  • Prepare for Leaflet Map Templates quiz, to be taken during any 2-hour period by Sunday 9pm on Moodle. Unless you have earned an outreach event exemption, there are two quiz deadlines this Sunday.

Mon Nov 11

Wed Nov 13

  • Assign Build 6: Data Story Draft to designated duos/solos. Tell a meaningful data story on your assigned question, at least 750 words plus at least two visualizations, for review by instructor (and later by peers). Before leaving the room, start your co-authored Google Doc, with links to any co-authored Google Sheets, with screenshots and links to any published visualizations, and insert for anyone to comment on our Build doc. Due by Monday Nov 18th 9pm, worth 5 points.
  • In-class workshop time
    • Quick feedback meetings with instructor
    • Quick to-do list meetings with TA

Mon Nov 18

  • Overview of Learning Goal #12: 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 Sun 9pm on Moodle.
  • In class workshop time
    • Quick feedback from instructor or TA before Build 6 is due Monday 9pm

Wed Nov 20

  • In-class: Assign peer reviews of Build 6 with evaluation criteria (TODO), due during class, for participation credit. Students must have submitted a full draft in order to participate in the peer review. Choose either to type comments into a shared Google Doc or hand-write on paper, and share a copy with the instructor, who will will evaluate the quality of your constructive feedback.
  • Schedule 20-minute Zoom appointments on my calendar for Monday Nov 25th or Tues Nov 26th. If working with a partner, be sure to share the appointment information, since both of you must attend the meeting at the same time to earn participation credit.
  • Assign Build 7: Data Stories for Community Partners. Improve your draft based on instructor and peer feedback, and migrate all content from Google Doc to GitHub Pages format. Insert link to your published GitHub Pages template on our Build 7 doc. Due Monday Dec 2nd 9pm (the first day back from Thanksgiving break) for instructor to forward to community partners for non-graded comments, worth participation credit.
  • Tip: An efficient method to edit your data story on your computer (TODO move this to a separate document)
    • In GitHub, download your index.html file to your computer
    • Use a text editor tool (not MS Word) to edit downloaded index.html. For example, Pulsar is my favorite open-source text editor.
    • While using text editor to revise and save changes to your computer, simultaneously view how they appear in your browser with File > Open File > downloaded index.html. Since this version is temporarily stored on your local computer, your browser bar will display its location in a local format file:///... rather than the online format https://...
    • After editing, upload your newer index.html file from your computer to replace the older version on GitHub. OR copy and paste the contents of the newer version in place of the older contents on GitHub.
  • Tip: Avoid deleting important HTML formatting in your data story template like this common mistake

Mon Nov 25

  • In place of our regular class, duo/solo 20-minute Zoom meetings with instructor for Build 6 feedback, for participation credit.

Wed Nov 27

  • No class - Thanksgiving Break

Mon Dec 2

  • In class: Student course feedback on what helped you learn (TODO). In addition, please fill out the College’s standard online evaluation form when available.
  • Review presentation lineups for Build 7 with Community Partners (non-graded comments only) and Build 8 with Guest Evaluators (worth 10 points)
  • Insert your GitHub Pages data story links in the Build 7 Doc above, which you can continue to revise and republish until the deadline.
  • Assign Build 8: Revise and Finalize Data story for guest experts. Your version is due Friday Dec 6th at 12 noon, with your published GitHub data story link inserted into the Build 8 document. There are no extensions because your data story will be “frozen” and a copy will be published on our public Partners and Projects page, and forwarded to our guest experts to read before your presentations. Worth 10 points, scored by guest experts.
  • Decisions to make before your data story is “frozen” and published:
    • You can insert your full name (recommended), or you have the right to use only your first name or initials. Learn why in my chapter, “Public Writing and Student Privacy” in Web Writing book (2015).
    • See what other students have done in prior Partners and Projects
    • Recommended: Insert link to your published data story in your resume to demonstrate your skills and knowledge to prospective employers and graduate schools. See example (TODO)
  • Keep in touch: See my Advising page on “How to request a reference or recommendation letter”
  • Rehearse your 1-minute presentations of Build 7 highlights for community partners. You will have more time for 2-minute presentations of Build 8 for guest experts.
  • Feel free to schedule a Zoom appointment on my calendar for feedback on your build. If you are working with a partner, please arrange to meet me at the same time if feasible.

Wed Dec 4

  • In-class: Build 7 Data Story Feedback with Community Partners on Zoom, 11:30am-12:30pm. Deliver 1-minute presentation of highlights in your data story to community partners, then listen to their non-graded feedback and write down revisions to consider, for participation credit.
  • Reminder: Build 8: Revise and Finalize Data story is due Friday Dec 6th at 12 noon, and will be “frozen” and published for guest experts to review.

Mon Dec 9

  • Build 8 Final presentations on Zoom with guest evaluators: Ginny Monk, Children’s Issues and Housing Reporter at CT Mirror, and Victoria Asfalg, Trinity ‘23, Policy and Data Analyst at Open Communities Alliance
  • Class will begin at 11:30am and end at 1:20pm and will take place in my Zoom room to ensure all students have sufficient time with guest experts. Students only need to attend their assigned 10 minute time slot and must log into my Zoom room a few minutes before it begins.
  • Build 8: Guest experts 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.