Educ 206: Data Visualization For All

Fall 2023 Syllabus

Our course meets in-person on Mondays and Wednesdays 11:30am-12:45pm in Library B02 at Trinity College, Hartford. You are welcome to wear a mask at any time on campus, and I may require all students to wear masks in class if Covid rates rise significantly.


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

Teaching Assistant Mia Rodriguez - 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 Projects

This semester we will work with Hartford-area community learning parters on two data visualization projects: Child Care and College Access.

Child Care: Izzi Greenberg, Executive Director of Middlesex Coalition for Children and Merrill Gay, Executive Director of CT Early Childhood Alliance have asked us to help visualize answers to this broad question: How and why does child care availability, affordability, and quality vary across Connecticut, and what can we learn from improvement strategies outside the state?

Intro video for Child Care project, Fall 2023 with partners Izzi Greenberg, Executive Director of Middlesex Coalition for Children and Merrill Gay, Executive Director of CT Early Childhood Alliance

College Access: Melissa Paul, Director of College Partnerships + Reaching Forward at Hartford Promise, has asked us to help visualize answers to this broad question about “successful matches” for their scholarship program: What measurable factors might explain why comparable groups of Hartford Promise scholars tend to experience success in college, relative to other students at these institutions?

Intro video for College Access project, Fall 2023 with partner Melissa Paul, Director of College Partnerships + Reaching Forward at Hartford Promise

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. Embed on the Web
  11. Tell and Show Your Data Story
  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 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 1pm 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.
  • 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 content on the 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. See the Intellectual Honesty 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 broad learning goals x 5 points per quiz = 60 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 policy 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.
  • 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 may be granted for health or family emergencies, if you email me prior to class. If you are quarantined or isolated due to Covid, or have other concerns about your health, email me well in advance to request permission to participate remotely via Zoom during a specific class session. Last-minute requests may not be granted.
  • Exceptions are also granted for any scheduling conflicts (such as religious observances or other pre-planned events) where you have notified me well in advance. Last-minute requests may not be granted.


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

Intellectual Honesty

  • You are responsible for following the Intellectual Honesty policy as described in the Trinity Student Handbook.
  • For online quizzes, you may use your materials (including your notes, the online book, and any content on the 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. If doubts arise, the instructor may require you to retake a quiz under direct supervision and/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, and image credits to any visuals produced by others. If any doubts arise, the instructor may require ask you to independently reproduce content under direct supervision and/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.


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

Wed Sept 6

  • 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
  • Read: Feedback from prior students about this course in Spring 2023
  • Presentation: Why data visualization matters in the “disinformation age”. See Hands-On DataViz (HODV), Chapter 1
  • In-class: What sparks your interest in this course? Anonymous free-write on shared Google Doc
  • Presentation: Why data visualization matters to me: 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 11

  • 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
    • 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
  • In-class: Google Spreadsheet Skills Practice
  • In-class: Google Sheet Sharing Practice
  • Prep for Spreadsheet quiz, to be taken during any 2-hour period before Sun Sept 17th 9pm on Moodle. HINT: Start your quiz by Fri 1pm to ensure sufficient time for instructor feedback.
  • During the quiz window, some students may receive an email from the instructor with instructions to redo or revise. If no email arrives, you may have earned full credit, which can be confirmed in your Moodle gradebook.

Wed Sept 13

Mon Sept 18

  • In-class: Brief review of successes and challenges of last week’s Spreadsheet quiz
  • Before class, read and be prepared to discuss Catherine D’Ignazio and Lauren Klein Data Feminism chapters 1 and 2, especially these selections:
  • Overview of Learning Goal #2: Find and Question Your Data. Start reading HODV Chapter 3 at
    • Challenge power & privilege in data
    • Clarify levels of data
    • Recognize public vs private data
    • Mask or aggregate sensitive data
    • Explore open data repositories
    • Source your data
    • Recognize bad data
    • Question your data
    • Prepare for Find and Question Data quiz, to be taken during any 2-hour period before Sun Sept 23rd 9pm on Moodle
    • Work with your partner to finalize Build 1, due Tues 9pm, and paste the link to your Google Doc shared so that anyone can comment on the Build 1 page before leaving the room

Wed Sept 20

Mon Sept 25

  • Quick review: Can we trust this data?
  • Overview of Learning Goal #3: Clean Up Messy Data. Start reading HODV Chapter 4 at
  • 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 (class demo only)
  • Prepare for Clean Up quiz, to be taken during any 2-hour period before Sun Oct 1st 9pm on Moodle
  • Discuss key insights and supporting evidence from three data stories on family income and education access (links above)

Wed Sept 27

Mon Oct 2

Wed Oct 4

  • Overview of Learning Goal #5: Chart Your Data. Start reading HODV Chapter 6 at
    • Chart types
    • Chart design principles and rules
    • Create bar/column, histogram, line, area charts

Mon Oct 9

No class - Trinity Days

Wed Oct 11

  • 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
    • 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
  • Prepare for Chart Your Data quiz, to be taken during any 3-hour period before Sun Oct 15th 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.
  • Start your quiz by Friday 11:30am if you wish to receive feedback for a second attempt. If you have questions, come to my Zoom Room drop-in (no appointment necessary) on Friday Oct 13th anytime 11:30am-12 noon (see link in Moodle)

Mon Oct 16

  • Brief review of scatter charts and sample student response 1 and response 2
  • Overview of Learning Goal #6: Map Your Data. Start reading HODV chapter
    • Map types (focus on locator point, symbol point, and choropleth maps)
    • Map design principles and rules
    • Use Datawrapper to create point and symbol point
  • Prep for Map Your Data quiz, to be taken during any 3-hour period by Sun Oct 22nd 9pm on Moodle.

Wed Oct 18

  • Continue overview of Learning Goal #6: Map Your Data,
    • Map types (more about choropleth maps)
    • Choropleth colors and intervals
    • Normalize choropleth map data
  • In-class: Map practice
  • Prepare for the 3-hour map quiz, start by Friday, and come to my Friday 11:30-12 drop-in time (see link in Moodle).

Mon Oct 23

Wed Oct 25

Mon Oct 30

Wed Nov 1

  • In-class: Progress Report with community-learning partners on Zoom
    • 11:40-12:10 (30 min) College Access with Melissa Paul (Hartford Promise) and Fionnuala Darby-Hudgens (CT Data)
    • 12:15-12:45 (30 min) Child Care with Izzi Greenberg (Middlesex Coalition for Children) and Merrill Gay (CT Early Childhood Alliance)
  • Start thinking about your preferences for final projects (Builds 4-7). 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 6

Wed Nov 8

  • Review Build 4 instructions and updates, due tonight 11:59pm
  • Overview of Learning Goal #10: Embed on the Web. Start reading Chapter 9
    • 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 Nov 12th 9pm on Moodle.
  • Workshop time for remainder of class: ask questions to instructor or TA, work on Build 4 or Quiz 10, etc.

Mon Nov 13

  • Overview of Learning Goal #11: Tell and Show Your Data Story. Start reading HODV Chapter 15
    • 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 5 below for 10 points total.
  • Assign Build 5: Data Story Draft. 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). Due by Sun Nov 19th 9pm as a Google Doc shared for anyone to comment with links to related data files and/or visualizations, worth 10 points (combined value of Quiz 11 and Build 5).
  • Practice applying evaluation criteria to data story by former student

Wed Nov 15

Mon Nov 20

  • In place of our regular class, duo/solo 15-minute Zoom meetings with instructor for Build 5 feedback, for participation credit.

Wed Nov 22

  • No class - Thanksgiving Break

Mon Nov 27

  • Let’s support and coach each other to successfully complete our semester
  • Before class: Make any revisions you wish to your Build 5 drafts, and make sure they are shared for anyone to comment
  • In-class: Peer review assignments with evaluation criteria, due during class, for participation credit. 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 6: Data Stories for Community Partners. Improve draft based on feedback, and migrate all content from Google Doc to GitHub Pages format, due Monday Dec 4th 2pm for instructor to forward to partners (for non-graded comments and participation credit).
  • Tip: An efficient method to edit your data story on your computer:
    • 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

Wed Nov 29

  • Overview of Learning Goal #12: Detect Lies and Reduce Bias. Start reading HODV Chapter 14
    • 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 Dec 3rd 9pm on Moodle.
  • In-class: Rehearse 1-minute presentations of highlights from your draft data stories for upcoming Build 6 meeting with community partners. You will have more time for 2-minute presentations for the Build 7 meeting with 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.

Mon Dec 4

  • 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 presentation lineups for Build 6 with Community Partners and Build 7 with Guest Evaluators
  • Everyone: Insert your GitHub Pages data story links in Google Docs above, and you can improve and revise until cut-off times
  • 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 your skills and knowledge to prospective employers and graduate schools, which shows more than simply listing “proficient in…” at the bottom.
  • Keep in touch: See my Advising page on “How to request a reference or recommendation letter”
  • Workshop time to finish data stories in preparation for community partner meetings

Wed Dec 6

  • In-class: Build 6 Data Story Feedback with Community Partners on Zoom. 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.
    • 11:30-12:10 (40 min) College Access with Melissa Paul (Hartford Promise) and Fionnuala Darby-Hudgens (CT Data)
    • 12:15-12:45 (30 min) Child Care with Izzi Greenberg (Middlesex Coalition for Children) and Merrill Gay (CT Early Childhood Alliance)
  • Community partners have agreed that our session will be video recorded. and the instructor will share the link on Moodle (not the public web). Also, if students wish to quote a community partner response to their work in Build 7, the student must email the exact wording of the quote to the partner (with cc: to instructor) as soon as possible for approval by the partner.
  • Assign Build 7: Revise and Finalize Data story for guest experts due Fri Dec 8th at 12:45pm. There are 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 experts.

Mon Dec 11

  • Build 7 Final presentations on Zoom with guest evaluators: José Luis Martínez, Data Reporter at CT Mirror, and Ilya Ilyankou, HandsOnDataViz co-author and Trinity Class of 2018, now PhD student at University College London researching Large Language Models and complex route planning, LinkedIn page.
  • 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 7: 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.