• CUNY Graduate Center | Summer 2018
• Course Number DHUM 73000
• Monday - Thursday 6:00 to 8:00 | 5/29 - 6/21 | Graduate Center, Room 7395
• Taught by: Erin Daugherty ([email protected]) and Michelle McSweeney ([email protected])
• Office Hours: By Appointment

As employers in every sector continue to search for candidates that can turn their data into actionable information, this course is designed to demystify data analysis by approaching it visually. 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, identifying which chart types work best for different types of data, and unpacking the tactics behind effective visual communication. With an eye towards critical evaluation of both data and method, projects and discussion 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 skill set 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
• Structure thoughtful critiques and communicate technical questions and solutions
• Leverage collaborative tools, including Tableau Public, Github, 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

During this course, you will complete four assignments: 2 guided projects and a final portfolio accompanied by a white paper. We expect that you will turn in each project before you feel fully ready to do so. You will have the opportunity to submit revisions of the first two blog projects until you're satisfied with the outcome.

One visualization built with New York City's 311 data

One visualization with a quantified self data set you've created

A series of three visualizations answering an independent research question using a data set of your choice

A 1,500-4,000 word final reflection on data, visualization, and iteration

Because this is a Summer Session course, we will cover a lot of ground in just four weeks. Attendance is essential for understanding the material and doing well in the course. Each class period will be divided into two parts: a seminar and a hands-on practicum. The seminar will focus on a theoretical component underpinning data visualization, and the second half will be a hands-on tutorial in Tableau. By the end of this course, you will have developed a deep understanding of the context around data visualization and how to ethically engage in visual communication.

Finally, we want to clearly state that our goal for this course is to expose you to a broad range of technologies and techniques. The schedule above is intentionally designed to move faster than the typical class. We're striving for progress, not perfection. You will experiment with technologies that might feel overwhelming and you will turn in assignments before they feel done. This is how iterative, creative processes work. We're confident that by the end of this course, no matter how many confusing or frustrating moments you've navigated throughout, that you will look back at where you started four weeks ago and be impressed by the ground you've covered. We're here to help you get the most out of this course. Let us know how we can help you succeed!

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Syllabus written by Erin Daugherty and Michelle McSweeney, for Visualization and Design: Fundamentals, a course in the M.A. in Digital Humanities at the Graduate Center at CUNY. More information about the program is available here.