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Syllabus Fall 2020

Visualization and Design: Fundamentals

  • pdf of syllabus | pdf of schedule
  • CUNY Graduate Center | Fall 2020
  • 6:30 to 8:30pm | Some Mondays | Zoom
  • Michelle McSweeney (mmcsweeney@gc.cuny.edu)
  • Office Hours By Appointment
  • https://introdatavizfa20.commons.gc.cuny.edu

Description

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 a 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 skillset ready to leverage in your academic and professional work.

Objectives

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, the 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

Assignments

During this course, you will complete four graded assignments: 2 guided projects and a final portfolio accompanied by a white paper. You will also complete tutorials on Tableau, forum discussions, and critiques. 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.

Submit your PROPOSALS &White Paper via email to mmcsweeney@gc.cuny.edu.

Post the link to your PROJECTS (the CUNY Academic Commons Page with your project and text) to the respective Project Threads in the Group Forum.

Post your Tableau Tutorials/Labsto your Tableau Public Site.

Post the Forum responses and Critiques to the Group Forum.

Project 1

15% Final Grade | Guidelines
Visualizations built with New York City’s 311 data

Project 2

20% Final Grade | Guidelines
Visualizations of a data set you’ve created

Final Portfolio

25% Final Project| Guidelines
Visualizations answering an independent research question using a data set of your choice

White Paper

10% Final Grade | Guidelines
A 1,500-4,000 word final reflection on data, visualization, and iteration

Critiques

10% Final Grade | Critiques Guidelines

Forums

10% Final Grade | Participation in Group Forums

Tableau Labs

10% Final Grade | Completion of Tableau Labs

 

This is an online hybrid studio-lecture format. Given that I have never attempted this online, we may need to make adjustments as the semester progresses. The lecture will focus on a theoretical component underpinning data visualization. The tutorials will cover essential tools and techniques in Tableau. The Studio is found in the iterative nature of your projects, the pin-ups, and the weekly critiques. The Tableau tutorials will be delivered in video format, and 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 on November 27th.

Every week, you will critique a professionally made visualization. You will get the most out of that exercise by really taking some time to read the visual before analyzing it.

There are 3 projects. You will submit a proposal before each project with 1 paragraph describing your question, the data you plan to use (specifying the variables), and sketch of your visualization. We will have a 5-10 minute meeting to discuss your proposal. After you complete each project, you will have the opportunity to get feedback from the class in the form of a pinup. The purpose of the pinups is to both practice giving and receiving feedback and it is an opportunity for the author to develop their project further.

There is also a Slack channel associated with this course. Please make use of it to stay in touch or to get quicker feedback.

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

Schedule

There are 5 SYNCHRONOUS classes via ZOOM (these will be finalized the first week of the semester — after the survey results are returned)

8/31 6:30-8:30pm (ET)

10/5 6:30-8:30pm (ET)

11/2 6:30-8:30pm (ET)

12/7 6:30-8:30pm (ET)

12/14 6:30-8:30pm (ET)

wk Topic Activities Topics & Readings Labs Due Sunday
31-Aug Introduction Zoom call, survey, forum (during Zoom) Suggested: Friendly, 2007 A Brief History of Data Visualization set up Tableau (0) link to Tableau public page, respond to survey
14-Sep Structuring questions for visualization Recorded lesson, critique (1), forum (1) Yau 2013 Chapter 1 Data Points Labs 1 & 2 (2h) Watch lesson, do labs, respond to forum (1), complete critique (1)
21-Sep Data Viz types: The basics Recorded lesson, critique, forum, proposal Yau 2013, Chapter 3 of Data Points || Nussbaumer Knaflic 2015. Chapter 2, Storytelling With Data: Choosing and Effective Visual Labs 3 & 4 (2h) Watch lesson, do labs, resond to forum (2), complete critique (2), PROJECT PROPOSAL
28-Sep one-on-one meeting Work on Project Optional: Tufte 1997 The Decision to Launch the Space Shuttle Challenger in Visual and Statistical Thinking Work on Project!
5-Oct Project Pin Up ZOOM 10/5!!! Pin Ups Viegas & Wattenberg 2015 Design and Redesign in Data Visualization  Project & Pin Up  6:30-8:30pm
12-Oct Quantified Self Recorded lesson, critique, forum Giorgia Lupi Dear Data TED Talk || Lupi & Posavec Dear Data  please interact with some of the visuals) || A year in Numbers 

At least one of:

Biopolitics of quantified self

Period Tracking apps are not for women

IoT Big Data (Privacy & Wearables)

 

Labs 5, 6, & 7 (3h) Watch lesson, do labs, respond to forum (3), complete critique (3)
19-Oct Data & Data Manipulation Recorded lesson, critique, forum Gitelman, 2013: “Raw Data is an Oxymoron” Introduction || Wang, 2013 Thick Data Medium post Labs 8 & 9 (2h) Watch lesson, do labs, respond to forum (4), complete critique (4), PROJECT PROPOSAL Due 10/23
26-Oct one-on-one meeting Work on Project Work on Project (note:  no CUNY Wednesday, we will end the semester a week earlier instead)
2-Nov Project Pin Up ZOOM 11/2!!! Pin Ups Project & Pin Up 6:30-8:30pm
9-Nov Text as Data Recorded lesson, critique, forum Schulz 2011 NYTimes Book Review of Graphs, Maps, and Trees & Moretti 2007 Lab 10 Watch lesson, do labs, respond to forum (5), complete critique (5)
16-Nov Spatial Analysis Recorded lesson, critique, forum Solnit, 2016 Nonstop Metropolis (please interact with some of the visuals) || Knigge & Cope 2006 Grounded visualization: Lab 11 Watch lesson, do labs, respond to forum (6), complete critique (6)
23-Nov Narrative & Story telling Recorded lesson, critique, forum McCandless TED Talk Lab 12 Watch lesson, do labs, respond to forum (7), complete critique (7) PROPOSAL DUE 11/27
30-Nov one-on-one meeting Work on Project Suggested: Andrew Stanton TED Talk: The Clues to a Great Story Work on Project!
7-Dec Project Pin Up ZOOM  12/7!!! Pin Ups Project (DUE Mon 12/7) & Pin Up  6:30-8:30pm
14-Dec Project Showcase ZOOM 12/14!!! Final Project & White paper Due

 

 

 

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