INFORMATION VISUALIZATION, CS 4460

From left to right Visualizations created by: Edward Tufte, Florence Nightingale, W.E.B. Dubois, Hans Rosling, Ben Fry/Fathom Information Design

Location: Georgia Institute of Technology, Instructional Center Room 211, Tuesday & Thursday 9:30-10:45 am with Recitation Thursdays 4:30-5:45 pm
Instructor: Dr. Ben Rydal Shapiro
Teaching Assistants: Cody O’Donnell, Arpit Mathur, Raveena Shah
Office Hours: Tuesdays, Wednesdays & Thursdays 1:30 pm-2:30 pm, TSRB 220/in the open office space near TSRB 220
Email: benjamin.shapiro@cc.gatech.edu, codytodonnell@gatech.edu, arpit.mathur@gatech.edu, rshah386@gatech.edu

OVERVIEW & LEARNING GOALS

In this course, you will be introduced to techniques for creating effective and compelling data visualizations from a range of fields including graphic design, computer science, architecture, semiotics, perceptual psychology, cartography, art, and cognitive science. You will also apply these techniques through a team design project where you will develop an interactive visualization of a real world data set to address a specific problem and/or serve the needs of a particular user or organization. Interactive lectures, in class discussion, design challenges, and assignments form the basis of this course. The course is targeted both towards students interested in exploring the use of visualization in their own work (e.g., in HCI, data science, education, or research) as well as students interested in building better computational/visualization tools and systems. There are no prerequisites for the class, however, basic working knowledge of, or willingness to quickly learn, graphics/visualization tools (e.g., D3.js, Processing/p5.js, Vega, HTML5, OpenGL etc.) and data analysis tools (e.g., Python, Excel, Matlab) is expected.

Acknowledgements: This course integrates ideas and work from many people working in information visualization and education including John Stasko, Jeffrey Heer, Sheelagh Carpendale, Alberto Cairo, Danielle Szafir, Alex Endert, Hadley Wickham, Jennifer Kahn and others.

TEXTS

You are required to purchase or make a sketchbook with NO lines. Scott Murray's book Interactive Data Visualization for the Web, 2nd edition will be helpful for the D3 lab exercises. Although not required, another highly recommended book about visual design is Envisioning Information by Edward Tufte, Graphics Press 1990. Also not required, another highly recommended book for those interested in pursuing careers in information visualization is Amelia Wattenberger’s Fullstack D3 and Data Visualization Guide

All other texts and readings will be made available online or on Canvas

PROJECT

The course project will run the duration of the semester with three primary design reviews that require deliverables.  You will work in teams of 3-4 members (assigned in class). You will select or collect a data set and develop an interactive visualization of that data set to address a specific problem and/or serve the needs/goals of a particular user organization or learner. You will be expected to implement your visualization in D3.js or comparable programming language/library. An initial list of data sets is provided at: Open Data Resources for Atlanta/Georgia and Example Data Sources for Information Visualization CS 4460. Project areas will be discussed in class, but you are expected to explore/propose your own ideas as a team based on your personal interests and cultural backgrounds.

USEFUL RESOURCES

Recommended visualization blogs: Flowing Data, Data Journalism, Fathom Notebook, Beautiful News, Parametric Press, The Functional Art, Visualizing Data Resources, Visual Complexity, Edward Tufte: Ask E.T. Forum, Visual Business Intelligence by Stephen Few, Statistical Modeling, Causal Inference, and Social Science, Information is Beautiful, Visual.ly, FiveThirtyEight, Data-Elixir

Course visualization software: Tableau for students, Spotfire, Infozoom, Trifecta Wrangler, Flourish

Course programming languages/visualization grammars: D3.js, Processing, p5.js, Vega & VegaLite, Data Illustrator

Course data resources: Open Data Resources for Atlanta/Georgia, Example Data Sources for Information Visualization CS 4460

Tips for a successful project: see Tips for a Successful Project (adapted from John Stasko)

For infovis web development resources see: Jeffrey Heer’s resources page, John Stasko’s resources page

Relevant online courses: Data Visualization for Storytelling & Discovery, Python for Data Journalists

ASSIGNMENTS & GRADING

* All assignments are described and will be submitted on Canvas

Individual Assignments - 33%

  • Assignment 1: Data Exploration & Analysis (5%)

  • Assignment 2: Critiquing Commercial Visualization System (10%)

  • Assignment 3: Personal Data Collection & Visualization (10%)

  • Programming assignments (8%)

Team Project - 40%

  • Design review I: Proposal & task/domain analysis (5%)

  • Design review II: Concepts & prototypes (10%)

  • Design review III: System video/Presentation & Submission of Visualization Tool (25%)

Participation (attendance, canvas posts, peer evaluations) - 15%

Summary Exam - 12%

Class Attendance. Class attendance is required. If you need to miss class for a legitimate reason, please speak/email with the instructor and TA, preferably before class.

Class Participation. This is about more than attendance but about contributing to learning in class through asking questions, giving suggestions to your classmates and generally being part of the discussion. In class participation also involves both your careful preparation for class of readings and tasks, and your genuine support of peers in the learning process. You should come to class prepared to discuss and raise questions about the readings on the day they are listed in the weekly schedule (below). Participation also entails your weekly contributions to the online discussion forum on Canvas as well as your contributions to your team throughout your design project (we will discuss this in class). 

Reference Format. Unless otherwise specified in the assignment, written work must follow APA format described here.  Basic guidelines include that all written work should be double-spaced in Times New Roman, and have 12-point font. Citations should be used for ideas, statements, comments, etc. that are not common knowledge or your own original thought.

Late Policy. Students need to submit all of their materials on or before the deadline to qualify for 100% credit. Unless an exception for unusual circumstances has been agreed upon with your instructor, 24 hours delay will result in a 25% penalty; 48 hours late submissions will incur a 50% penalty, materials submitted past 48 hours will not be accepted, and will entered a zero grade.

Honor Code. This class abides by the Georgia Tech Honor Code. All assigned work is expected to be individual, except where explicitly written otherwise. You are encouraged to discuss the assignments with your classmates; however, what you hand in should be your own work.

Support Services. In your time at Georgia Tech, you may find yourself in need of support. Please see this LINK for information about some resources to support you both as a student and as a person.

SCHEDULE

1/7

Introductions & course overview

1/9 Re-design & concept inventory

  • Complete background survey available on Canvas home page

  • Post to canvas discussion forum

1/14 Framing Visualization

1/16 Framing Data in Visualization

1/21 Intro to Visualization on the Web/D3.js I

Assignment 1

1/23 Intro to Visualization on the Web/D3.js II

1/28 Principles of perception, cognition & color

1/30 Statistical Graphs

2/4 Team Assignments/project discussion

  • Post to canvas discussion forum (project ideas, data set, and task analysis links)

2/6 Visual Encoding I

2/11 Visual Encoding II

2/13 Redesign activities/Project time

  • Post to canvas discussion forum

  • Bring data sets and visualizations relevant to your project area to class

  • Lab 4 (due Friday)

2/18 Design Review I

  • Prepare for design review 1

  • Design Review I

2/20 Data Storytelling, Presentation & Communication

2/25 Tableau Tutorial

  • Download Tableau (use code provided by instructor to receive student copy)

  • Review examples, data sets and instructional videos in Tableau (come to class with questions)

  • Begin working on Design Review II/your prototypes

2/27 Interaction I: Guest Visit, Arjun Srinivasan

3/3 Interaction 2: Overview & Detail

  • Work on assignment 2

  • Post to canvas discussion forum

  • Assignment 2

3/5 Task, Analysis & Context in Visualization

3/10 Project work

  • Prepare for design review II

3/12 Design Review II

  • Prepare for design review II

Design Review II

3/16-3/22 Spring break

3/24

Class canceled

TBD

3/26

Class canceled

TBD

Lab 8a/8b (Optional)

3/31 Course planning/organizational day/technology testing

  • Canvas Discussion Post

  • Review Design Review II feedback, continue working on projects

4/2 Individual team meetings with Teacher/TAs

  • Be prepared to share 1 page description of 1) a project plan for how you will conduct/complete your project for the rest of the semester and 2) tools you are/will use to collaborate and work virtually

4/7 Exam preparation

  • Prepare for exam

4/9 Summary exam (in class)

  • Prepare for exam

4/14, 4/16

TBD

TBD

4/21

Design Review III & Summing Up

* Topics and readings are subject to change. Please always check the online schedule.