PADM-GP 4119

Data Visualization and Storytelling

Spring 2025

Course Description

In our increasingly data-reliant and data-saturated society, people who understand how to leverage data to generate insights have the power to change the world. Data visualization and storytelling is a crucial skill for policy and data analysts, communications and marketing professionals, and managers and decision-makers within nonprofits, social organizations, and the government. With the advent of visualization tools that do not require coding, data storytelling in the digital age is also an attainable skill set for people with varying levels of technical ability.

This hands-on introductory course will teach students how to develop meaningful data stories that reveal visual insights accessible for relevant audiences. Students will also learn the basics of Tableau, the industry standard in data visualization tools, to make sense of and visualize publicly available data. Students will leave the course with a portfolio of data visualization projects, analog and digital, that demonstrate the application of data storytelling. This course is intended for a beginner in data visualization and storytelling. Students with extensive prior experience should consult the instructor before enrolling.

Course and Learning Objectives

By the end of the course, students should be able to:

1.   Evaluate and critique data visualizations to become better consumers of data.

2.   Gain experience with presenting data insights through visualizations.

3.   Understand and apply data visualization and storytelling best practices to communicate accessible and meaningful insights.

4.   Develop meaningful data stories, gaining experience with the iterative process of data storytelling.

5.   Construct captivating and engaging visualizations, dashboards, and stories in Tableau.

Learning Assessment Table

Class Policies

This is a fast-paced, hands-on course with a lot of material condensed into seven weeks.

Students should be mindful of the following expectations to ensure that they are benefitting from the sessions and achieving intended learning objectives:

●   Attendance for the entire class session for all seven sessions is mandatory. Students should not register for the class if they anticipate any conflicts.

●   Active engagement during the sessions is essential. This course is designed to be a largely practice-based course. Students will maximize class learning if they come prepared having completed their assigned reading and training materials, developed a basic knowledge and theory of the weekly session topic, and are ready to engage during the course discussions, labs, and recitations.

●    Deeper engagement with the content outside of the class sessions will be needed to ensure students are able to complete assignments and projects successfully. Due to the condensed nature of the course, students will need to put in additional time outside of class sessions and should plan accordingly.

You are permitted to use generative AI tools in your written assignments, as long as you disclose the tool you used and any related prompts (including system prompts or other customization).

Please note that the onus for ensuring quality and accuracy of any output from a genAI model is entirely up to you –– you are ultimately held responsible for what you submit as your work in this class. Your work – especially your written work – must utilize the vocabulary and conceptual material that we introduce in lecture.

Required Materials

Readings: There is no textbook requirement for this class. Required readings will come from noteworthy articles, blogs and book excerpts; all materials are available online via hyperlinks on this syllabus or on our [SP25] Google Drive .

Software: To ensure successful lab/recitation participation, students are required to:

●    Have downloaded a Tableau Desktop license on your laptop (students are eligible for a free one-year license).

●    Ensure you have Microsoft Excel or Numbers on your laptop.

●   Sign up for a Miro for Education (Student) account.

Course Components

Readings

This course is designed to be a largely practice-based course. Therefore, it is crucial to come prepared to class with the basic knowledge and theory needed to have interactive discussions and a hands-on lab. (See Detailed Course Overview for more information for each week.) All materials are available online via hyperlinks on this syllabus or on our [SP25] Google Drive. Students must read assigned chapters/articles before coming to the respective session.

Orienting Discussions

Most course sessions will begin with a brief orienting discussion to recap best practices and lessons on data visualization and storytelling. Each discussion will build on the assigned reading material for that week and should be an opportunity to deepen knowledge and clarify questions.

Labs and Recitations

Most course sessions will include an experiential lab session. Students will also have an opportunity to hone their Tableau skills during a hands-on recitation immediately following each course session. To ensure successful lab/recitation participation, students are required to:

●   With the exception of Week 1, please complete all readings, pre-work assignments, and deliverables before class.

●    Ensure you have downloaded a Tableau Desktop license on your laptop (students are eligible for a free one-year license).

●    Ensure you have Microsoft Excel on their laptop.

Assignments

Assignments are formative, intended to help students understand data viz tools and best practices.

They consist of completion of lab-related deliverables, writing a data viz and dataset critique blog, and storyboarding the final project. Details on each assignment will be provided in the previous class session.

Projects

Unlike the formative assignments, projects are intended to assess mastery over data viz content and skills. Evaluation information can be found under Assessment Assignments and Evaluation. Projects will be uploaded via the blog tool on NYU Brightspace.

(1) Analog Data Viz Project

Students will create and present an analog “data postcard” by collecting and hand drawing data they collect over the course of several days/a week (see the Dear Data project for more information/ideas). This project is intended to reinforce the importance of communicating data insights effectively and creatively irrespective of the medium/tool. As students will not be using Tableau, students should be especially mindful about visualization execution (i.e., best  practices on chart types, color schemes, legends, so on). You will still be expected to submit your data analysis in Excel in addition to your analog data viz.

(2) Individual Final Project

All students must create a data story using Tableau that demonstrates their data visualization and storytelling skills through the course. While students are given free rein on content and execution, all data stories must contain at least three visualizations using Tableau Story Points. Data stories  must also serve one of two goals: to help the intended audience make data-driven decisions or to convey meaningful impact information to an intended audience. An accompanying blog post should briefly contextualize the data story and explain how it achieves one of the two intended goals. Students will learn more about the final project during Week 4.

To ensure that students are on track with their final project, the following completion deliverables will be enforced:

● Week 05: Finalize final project topic and data set; bring storyboard idea (we will do a storyboarding workshop during the class session).

● Week 06: Come to class with a rough Tableau workbook of your final project (there will be an opportunity to ask questions during class), and a Miro board of your storyboard.

● Week 07: Final projects due.

Assessment Assignments and Evaluation

Participation (15%):

Students are required to attend all class sessions and come prepared for and actively participate in class. All students will begin with the full 15 points. If students miss class or are unprepared for a class session, a maximum of 3 points will be deducted each session. Given the remote nature of this semester, active participation will include asking/answering questions during the session (including   in chat) as well as contributing to discussion in breakout groups. Please contact the instructor if any issues arise during the semester.

Participation in recitation sessions is strongly encouraged and will help students develop their Tableau skills, but will not be counted toward your Participation grade. However, hands-on exercises in recitations 2 and 4 count toward Tableau lab assignments and should be completed/submitted in  NYU Brightspace, regardless of recitation attendance.

Homework Assignments (30%):

Assignments will be split into three components:

●   Tableau lab worksheets/workbooks (10%) – Graded on a 100-point scale based on completion.

●    Data viz critique blog post (10%) – Graded on a 100-point scale based on completeness and demonstrated understanding (see rubric on page 7).

●    Final project draft (10%) – Graded on a 100-point scale based on completion.

All homework assignments should be submitted via NYU Brightspace by the beginning of class on the specified due date. Late assignments will have 10 points deducted for everyday it is late (even if submitted the same day but after class, 10 points will be deducted). If you receive a zero on a homework assignment, you can resubmit one homework assignment per semester for a maximum of 50% the total value of the assignment.

Analog Data Viz Project (25%):

The project will be evaluated on two components: completion of the project, including a presentation during class and the analog data viz. The data viz evaluation rubric can be found on page 8. The presentation should explain the data story in a compelling, clear, and effective manner. Be sure to share your data file in addition to the viz. Students will have 2-3 minutes to present their data story to the class. Make sure to share details on your process in addition to the image of your analog data viz during your presentation.

Final Project (30%):

The final project will be evaluated on several components: the data story, the orienting blog post and presentation. The evaluation rubric can be found on page 9. Detailed instructions will be in our [SP25] Google Drive.