Course Schedule

(This is an initial schedule, but the details and the specificic of the assignments may change as the semester goes on.)

Class 1: Introduction to Data Storytelling Techniques (March 20th)

Syllabus Review, and We will explore the history of data visualization from the first maps to the latest interactive tools from the New York Times, as well as some lesser-known data visualizations.

Following this, we will also discuss the hows and whys of storytelling with data, and will participate in a collaborative exploration of data visualization using Sharpies, Post-It notes, and things that begin with "S".

Readings: Chapter 1 of Data Visualisation (p. 20-52)
Homework: In-class Software Competency Survey

• https://eagereyes.org/basics/data-continuous-vs-categorical
• http://www.perceptualedge.com/articles/dmreview/quant_vs_cat_data.pdf

Class 2: Introduction to Charts and Graphs (March 22nd)

The fundamental tools of data visualization are the spreadsheet and the chart. Modern spreadsheet software like Microsoft Excel or Google Sheets make generating charts easy, but there are so many types of charts and ways to configure them that it can be difficult to know how to get started, or how to choose the best chart to help tell your story.

We will explore the types of charts available, describe the differences between them, when each is appropriate, and work through the details of creating and customizing a chart to help tell a specific story.

Technology Overview: Google Charts.
Readings: Data Visualisation: Intro to Part D & Chapter 11 (p. 313-334), Chapter 6 (p. 152-160, 210-221. Skip the grey section.)
Homework: Visualization Quiz (in-class), Google Charts Technology Project

Supplementary Material

• http://kosara.net/papers/2016/Kosara-BELIV-2016.pdf

Class 3: Humans as our Audience: Perception and Storytelling (March 27th)

This class will dive deeper into our understanding of human perception. Why and how do we as humans perceive color, space, and compare information? What are the fundamental types of stories that can be told with data, and when is each one appropriate? How do we take classical journalism and storytelling techniques and apply them to data visualization?

Readings: Data Visualisation: Chapter 9 (p. 263-292)
Homework: Visualization Quiz (in-class)

Supplementary Material

• Colorgorical
• Information Visualization Manifesto
• Finding a Story in Data
• Design Principles: Visual Perception And The Principles Of Gestalt
• ColorBrewer
• Subtleties of Color: Different Data, Different Colors
• MPL Colormaps (useful color scales)

Class 4: Data as Raw Material (March 29th)

This class will focus on data as the raw material for data visualizations. It will be an overview of standard data formats, an understanding of where and how to find and process data, and a basic review of mathematical and statistical techniques for exploring data in the context of summarizing or understanding data.

Readings: Chapter 4, Data Visualiation. (p. 97-129.)
Technology Overview: Highcharts
Homework: Visualization Quiz (in-class), Highcharts Technology Project

Class 5: Lying With Data (April 3)

Data visualization is a powerful tool for telling stories with data. However, not all stories are true.

This class will explore, using real examples, of how to conceal, deceive, and outright lie with data through omissions, visual design, and other techniques. Knowing how to selectively focus attention, choose datasets that support your argument, and selectively provide context will provide new ways to think about your data visualization, make you a more sophisticated consumer of data, and help you realize when you're accidentally lying. We will also discuss the ethical decisions and principles involved in communicating with data.

Readings:

• http://kosara.net/papers/2015/Skau-EuroVis-2015.pdf
• https://eagereyes.org/papers/evaluation-of-the-impact-of-visual-embellishments-in-bar-charts
• http://fellinlovewithdata.com/research/deceptive-visualizations
• https://visualisingadvocacy.org/blog/disinformation-visualization-how-lie-datavis
• https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=2566968

Homework: Visualization Quiz (in-class)

Class 6: The State of the Art (April 5)

This class will discuss what is currently going on in the data visualization world, and how the changing world of digital technologies is informing the way that we visualize data.

We will also discuss the role of interactivity in data visualization: techniques for effective use, and developing best practices.

This will also provide an overview of important projects and resources from practitioners around the world: a new canon for Data Visualization.

Technology Overview: D3.js s Readings: Data Visualisation, Chapter 7. (p. 223-246)
Homework: Visualization Quiz (in-class), D3 Technology Project

Class 7: Creating a Brief (April 10)

This class will kick off our final project by discussing how to define a scope and a reason for the data visualization, otherwise known as a brief. We will discuss preparing a written document that describes the audience, purpose, thesis, and other goals for a data visualization.

Readings: Chapter 2 & 3, Data Visualiation. (p. 53-95.)
Homework: Visualization Quiz (in-class)

Class 8: Exploring our Datasets (April 12)

This class will focus on how to explore a data set to determine possible avenues for meeting the needs of your brief. We will discuss data exploration, data examination, and basic transformation. We will also discuss exploratory data visualization techniques for discovering new insights in an existing data set, and also how to combine data sets from disparate sources.

Technology Overview: Tableau Public
Readings: TBD
Homework: Visualization Quiz (in-class), Tableau Technology Project

Class 9: Sketching with Data (April 19)

This class will focus on how to quickly prototype data visualizations, and how to make sure that you have fully explored the possible space for visualization before committing to a final form.

Readings: Dear Data
Homework: Visualization Quiz (in-class)

Class 10: Defining your Project's Voice (April 17)

Once a problem and data has been established, it is important to define the tone of a data visualization. Thinking through your presentation medium, your conversational voice, and your prospective audience influences the final form of your data visualization. We will discuss the differences between data art, scientific visualization, and journalistic infographics, and how the various techniques of these similar-but-different models for data visualization can be used for more effective presentation.

Technology Overview: [P5.js][http://p5js.org]
Readings: Chapter 5 & Intro. to Part C, Data Visualiation. (p. 131-149.)
Homework: Visualization Quiz (in-class), P5 Technology Project

Class 11: Typography and Design in Data Visualization (April 24)

This class will focus on how to effectively design a data visualization. We will (of course) discuss Tufte, XKCD, as well as other practitioners and styles for effective data visualizations.

We will also provide a basic history and description of typographical style, focusing on typefaces, white space, balance, and other traditional graphic design techniques.

Readings: Chapter 8 & 10, Data Visualiation. (p. 247-262, p.293-312.)
Homework: Visualization Quiz (in-class)

Additional Reference Material:

• Elements of Typographic Style, Applied to the Web
• http://infoscape.org/publications/ivs12.pdf

Class 12: Visualization of Space and Time (April 26)

This class will provide an overview of techniques and tools needed for spacial and temporal representations of data. We will explore mapping, geolocation, timelines, choropleths, and other ways that space and time can and should be represented in data visualization.

Technology Overview: Carto
Readings: TBD
Homework: Visualization Quiz (in-class), Carto Technology Project

Class 13: Project Presentations (May 1)

Class 14: Project Presentations (May 3)

This week will consist of short presentations by students of their visualization projects.