Introduction to Data Visualization
- CUNY Graduate Center | 5383
- 6:30 to 8:30pm | Mondays
- Michelle McSweeney (email@example.com)
We live in a data-driven society where decisions are made based on analysis of “the data.” Frequently, that data is presented in the form of visualizations. The practice of visualization has data at its core, and accurate, clear visual presentation depends on deeply understanding the nature and nuance of a dataset. Visualization is also inextricably linked with communication and storytelling. This course situates the practice of data visualization within a larger context of data literacy and data ethics. Using Tableau Software, we will build a series of interactive visualizations that combine data and logic with storytelling and design. We will dive into cleaning and structuring unruly data sets, identify which chart types work best for different types of data, and unpack the tactics behind effective visual communication. With an eye towards critical evaluation of both data and method, projects and discussions will be geared towards humanities and social science research. Regardless of your academic concentration, you will walk away from this class with a portfolio of dynamic dashboards and a new interdisciplinary skillset ready to leverage in your academic and professional work.
By the end of this class, you will be able to:
- Build interactive data visualization dashboards that answer a clear and purposeful research question
- Choose which chart type works best for different types of data
- Iterate with fluidity in Tableau Software leveraging visualization, aesthetic, and user interface best practices
- Evaluate a dataset with an eye towards bias and accountability.
- Structure thoughtful critiques and communicate technical questions and solutions
- Leverage collaborative tools, including Tableau Public, CUNY Academic Commons, and repositories of public data sets
- Contribute to the broader conversation about digital practices in academic research
- Critically read a wide range of chart types with an eye for accuracy, audience, and effectiveness
- Identify potential weaknesses in the collection methods and structure of underlying data sets
- Locate the original source of a visualization and its data
This course is a hybrid lecture-studio format. The lecture will focus on the theory of data and the relationship between data, visualization, and storytelling. The studio aspect is the combination of the Tableau labs and the pin ups.
The Tableau tutorials are only available online (as both videos and pdfs) because I believe asynchronous formats are the best way to learn to use a new tool. They can be completed in any order, though they do correspond to the weekly sessions. The due dates are in the syllabus, but I will only check for completion near the end of the semester. You will not receive feedback on the labs because ultimately your lab will look a lot like the examples.
Weeks are divided into project weeks and non-project weeks. Non-project weeks follow a traditional seminar format: there will be a lecture and forum that you will need to post to. Project weeks will consist of either a meeting or a pin-up (see the syllabus for more details).
There are 3 projects. Before each project, you will submit a project proposal. The project proposal consists of 1 paragraph describing your question, the data you plan to use (specifying the variables), and a sketch of your visualization. We will have a 5-10 minute meeting to discuss your proposal. You will then complete your project.
After you complete your project, you will participate in a pin-up. This is an opportunity to get feedback from the class. The purpose of the pinups is to both practice giving and receiving feedback and it is an opportunity to envision how you can develop your project.
By the end of this course, you will have developed a deep understanding of the context around data visualization and how to effectively and ethically engage in visual communication.
***Spring 2023 ADDENDUM***
This course is intended to be taken mostly in person. However, the 1:1 meetings will be conducted online due to the short nature of them. The format of each session is indicated in the syllabus. If you are uncomfortable or unable to come to campus for any reason, please make up the lecture by watching pre-recorded lecture. There is not 100% parity with these videos anymore, but they are close enough to suffice. If you are unable to attend for a pin-up, you MUST make alternative arrangements as soon as possible.
During this course, you will complete four graded assignments: 3 projects and a white paper. You will get feedback on these 4 items. You will likely turn in each project before you feel fully ready to do so, and will have the opportunity to submit revisions until you’re satisfied with the outcome.
You will also complete tutorials on Tableau, forum discussions, and critiques, these are graded on completion and you will not receive feedback on them.
Submit your PROPOSALS & White Paper via email to firstname.lastname@example.org.
Post the link to your PROJECTS to the spreadsheet in your email.
Post your Tableau Tutorials/Labs to your Tableau Public Site.
Post the Forum responses and Critiques to the Group Forum.
15% Final Grade | Guidelines
One visualization built with New York City’s 311 data
20% Final Grade | Guidelines
One visualization with a data set you created
Final Project & Presentation
25% Final Grade | Guidelines
A series of three visualizations answering an independent research question using a data set of your choice
10% Final Grade | Guidelines
15% Final Grade | Critiques Guidelines
15% Final Grade | Completion of Tableau Labs
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