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DHUMS7300_Fall19_syllabus in PDF form

 

• CUNY Graduate Center | Fall 2019
• 6:30 to 8:30pm | Thursdays
• Graduate Center, Room 5417
• Michelle McSweeney (mmcsweeney@gc.cuny.edu)
• Office Hours By Appointment
• https://dhum73000.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 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.

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 assignments: 2 guided projects and a final portfolio accompanied by a white paper. You will likely 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.

Blog Post 1

20% Final Grade | Guidelines

One visualization built with New York City’s 311 data

Blog Post 2

20% Final Grade | Guidelines

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

Final Portfolio

30% Final Grade | Guidelines

A series of three 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

In-Class Reflections

10% Final Grade | Participation in the in-class reflections and critiques

Tableau Tutorials

10% Final Grade | Completion of Tableau tutorials

Schedule

This schedule is subject to change.

 

Date	Topics & Readings	Labs & Visualizations
8/29	Introduction & Course Goals     Suggested: Friendly, 2007 A Brief History of Data Visualization	Tableau Set Up (0)   Setup
9/5	CUNY MONDAY
9/12	Structuring Research Questions for Data Visualization   Yau 2013 Chapter 1 Data Points	(1)   Google Sheets Data Prep (2)   Simple Restaurant Visualization (3)   Setting Up a dashboard
9/19	Data Viz Types: The Basics Yau 2013, Chapter 3 of Data Points Nussbaumer Knaflic 2015. Chapter 2, Storytelling With Data: Choosing and Effective Visual   Blog Post 1 PROPOSAL Due (9/22, 6am)	(4)   311 Data Download
9/26	Blog Post 1 Due 6pm   Pin Up #1   Viegas & Wattenberg 2015 Design and Redesign in Data Visualization Optional: Tufte 1997 The Decision to Launch the Space Shuttle Challenger in Visual and Statistical Thinking	LABS 0-4 DUE
10/3	Quantified Self   Giorgia Lupi Dear Data TED Talk   Lupi & Posavec Dear Data (this is not a reading, per se, but please interact with some of the visuals)   A year in Numbers	(5)   Data Structures (6)   Data joins (7)   Calculated Fields
10/10	LAB CLASS  – ONLINE	(8)   Dashboard Design Part I: The visualizations (9)   The Dashboards
10/17	Data & Data Manipulation     Gitelman, 2013: “Raw Data is an Oxymoron” Introduction   Wang, 2013 Thick Data   Blog Post 2 PROPOSAL Due (10/20, 6am)	LABS 5-9 DUE
10/24	Blog Post 2 Due 6pm   Pin Up #2
10/31	Text as Data   Schulz 2011 NYTimes Book Review of Graphs, Maps, and Trees & Moretti 2007	(10) Text Analysis (11) Mapping
11/7	LAB CLASS – ONLINE	(12) Parts of a Whole   OR   (13) Progressions through Time
11/14	Spatial Analysis & Grounded Visualization   Solnit, 2016 Nonstop Metropolis   Knigge & Cope 2006 Grounded visualization: integrating the analysis of qualitative and quantitative data through grounded theory and visualization	LABS 10-11 & 12 OR 13 DUE
11/21	Narrative & Storytelling   McCandless TED Talk   Suggested Andrew Stanton TED Talk: The Clues to a Great Story   Blog Post 3 PROPOSAL Due (11/24, 6am)
11/28	HAPPY THANKSGIVING
12/5	Blog Post 3 Due (12/5, 6pm)   Pin Up #3
12/12	PRESENTATIONS
12/15	WHITE PAPERS & Final Portfolios DUE

 

 

 

 

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