Download Data Visualization INFO-GB 3306.10 (9/17 - 12/10)

Data Visualization INFO-GB 3306.10 (9/17 - 12/10)
Revised July 18, 2015
Course Syllabus
Watch a brief introduction to the course at: https://youtu.be/frwl-YVtmrs
Instructor
Kristen Sosulski, Ed.D. Associate Professor of Information Systems [email protected] | 212.998.0994 | Tisch Hall, Room 509
Office Hours: Thursdays, 3-5pm and by appointment.
Course meetings & format
This is a blended online course. We will meet 9 times in the classroom. During
the weeks of 10/15, 10/29, and 11/5 there will be no in-class meetings. Instead
on 10/15 there will be an “remote online live web conference” with a Tableau
expert. On 10/29 and 11/5 you’ll work on learning how best to use the tools of
data visualization through structured lessons and assignments that I’ve created
for you. This is designed to allow you to work and practice creating
visualizations at your own pace. There is no class on 11/26 for Thanksgiving.
Thursdays from 6:00pm – 9:00pm
September 17 – December 10 Location: KMC 4-90
IMPORTANT: This is a hands-on course. You will also need to have your
computer (Mac or PC) up to date to install, Tableau 9.0 Desktop software
(provided free to students in the class). Also, some knowledge of basic
programming (in any language) will be helpful, but not required. We will use
several tools to refine our data and create, edit, alter, and display their
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visualizations. The primary tools we will be using in this class include: Tableau
9.0 Desktop and Excel. In addition, there will be opportunities to learn how o
create visualizations in R, Google Charts, and D3. To learn these tools we will
begin working with very small data sets. Since this is not a class on data
analysis or models, you’ll be expected to apply your prior knowledge learned
about business analytics to the creation of beautiful data displays (using big or
small data).
Course description
What is data visualization?
Visualization is a kind of narrative, providing a clear answer to a question
without extraneous details. --Ben Fry
This course is an introduction to the principles
and techniques for data visualization.
Visualizations are graphical depictions of data
that can improve comprehension,
communication, and decision making. Visualization is a graphical representation of
some data or concepts. --Colin Ware
Burak Arikan’ s Meta-Markets
In this course, students will learn visual
representation methods and techniques
that increase the understanding of complex data
and models. Emphasis is placed on the
identification of patterns, trends and differences from data sets
across categories, space, and time.
How does design of information support meaning and knowledge making?
Understanding is a path, not a point. It’s a path of connections between
thought and thought; patterns over patterns, it is relationships. --Richard Saul Wurman
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The ways that humans process and encode visual and textual information will
be discussed in relation to selecting the appropriate method for the display of
quantitative and qualitative data. Graphical methods for specialized data
types (times series, categorical, etc.) are presented. Topics include charts,
tables, graphics, effective presentations, multimedia content, animation, and
dashboard design. The goal of effective visuals is to communicate information to maximize
readability, comprehension, and understanding. Information visualization is a
combination of many disciplines. Principles are drawn from statistics, graphic
design, cognitive psychology, information design, communications, and data
mining.
Throughout the course, several questions will drive the design of data
visualizations some of which include: Who’s the audience? What’s the data?
What’s the task?
Course topics
1. Design principles for charts and graphs
2. Common tools for creating data visualizations (Excel, PowerPoint, and
Google Visualization API)
3. The process creating visualizations and selecting the appropriate visual
display
4. Hands on with Tableau
5. Designing effective digital presentations
6. Telling stories with data
7. Visualization as exploration
8. Visualizing categorical data
9. Visualizing time series data
10. Visualizing multiple variables
11. Visualizing geospatial data
12. Dashboard design
13. Web-based visualizations
14. Interactive visualizations and motion
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Learning outcomes
• Present data with visual representations for your target audience, task,
and data;
• Experiment with and compare different visualization tools;
• Create multiple versions of digital visualizations using various software
packages;
• Identify appropriate data visualization techniques given particular
requirements imposed by the data;
• Apply appropriate design principles in the creation of presentations and
visualizations; and
• Analyze, critique, and revise data visualizations.
Course requirements and grading
Assignments (40%)
There will be 4 individual assignments due during the first half of the semester.
Each assignment is worth 10% of your grade. The assignments will require you
to work with data and use various technologies to create data visualizations.
Assignment 1: Due 10/1
Assignment 2: Due 10/8
Assignment 3: Due 10/15
Assignment 4: Due 10/22
Online Lessons and Exercises (15%)
There will be one online lesson and exercise for each of the three online
classes.
Week 5 (remote live online meeting) (10/23 to 10/29): Online
Week 7 (self-paced)
Week 8 (self-paced)
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Individual Project & Group Project Pitch (15%)
The project is a demonstration of your knowledge and fluency with data
visualization techniques and tools. The individual project is an opportunity for
you to create a series of data visualizations based on your selected data
source. You will create at least 4 visualizations based on your defined
audience, data, and tasks. You will present visualizations to the class and pitch
an idea for a group project based on your initial work. Project Due: 11/5
Project Presentation & Pitch: 11/12
Group Project (30%)
The group project is a demonstration of you and your team’s effort,
knowledge, and ability to tell an interesting story with data. The group project
will be a presentation using data visualizations to tell a story to your audience. Refined project proposal: Due 11/12 Examples to share during in-class critique: Due 11/20
Final project and presentation: Due 12/11
Project Due: 12/3
Attendance, class participation, teamwork, collaboration, and class
preparation. Students are expected to attend all classes, participate regularly in the large
class discussion, and small group discussions. Part of this course involves
working with others and seeking feedback from your peers on your in-class
exercises. A large portion of the class requires you to actually work with data
and visualization tools to create visual displays. There are regular exercises and
assignments to help you practice and learn the appropriate techniques. These
exercises are short but frequent. PLEASE BRING YOUR FULLY CHARGED
LAPTOPS TO CLASS.
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Required readings, articles, videos, and data
Godin, S. (2007). Really bad PowerPoint (and how to avoid it). Available
at: http://www.sethgodin.com/freeprize/reallybad-1.pdf
Wong, D. (2011). The Wall Street Journal guide to information graphics: The dos
and don’ts of presenting data, facts and figures. New York: W.W. Norton
& Company. Available at the NYU Bookstore
Yau, N. (2013). Data Points: Visualization that means something. Indianapolis:
O’Reilly. Available at the NYU Bookstore
NYU Classes
All articles, videos, and data will be on NYU Classes > Data Visualization
> Lessons. Optional readings
See course bibliography for a complete list.
Few, S. (2012). Show me the numbers: Designing tables and graphs to
enlighten. Burlingame, CA: Analytics Press.
Few, S. (2006). Information dashboard design: The effective visual
communication of data. Sebastopol: O’Reilly.
Ware, C & Kaufman, M. (2008). Visual thinking for design. Burlington: Morgan
Kaufmann Publishers.
Yau, N. (2011). Visualize This: The FlowingData Guide to Design, Visualization,
and Statistics. Indianapolis: O’Reilly.
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Required software
The major graphics tools we will be using in this course for creating visualizations
are Excel and Tableau. You must have both applications installed. You must
have a computer you can bring to class. You must have a computer that allows
you to install additional software (you should administrator access to your
computer).
• Microsoft Excel and PowerPoint
• Tableau 9.0 (student version): http://www.tableausoftware.com/products/
desktop/download and a Tableau public account (FREE. Student access
codes will be given in class)
• Vector Graphics Editor: Adobe Illustrator CS5 or later or you can use a free
open source vector graphic tool such as Inkscape (http://inkscape.org/)
Optional software for advanced exercises
• R and RStudio (additional libraries required): http://www.r-project.org/,
http://www.rstudio.com/ (FREE)
• Dreamweaver / HTML editor
Course Schedule
The outline of course topics and dates is presented below. All readings,
assignments, videos, etc. will be presented in NYU Classes for each week.
There will video tutorials and exercises that you are require to complete at
home during the online weeks.
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Week
Topics
1 - 9/17
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Introduction to the course and data visualization
Telling stories with data
Basic design principles principles for charts and graphs
Common tools for creating data visualizations
2 -9/24
• Application of design principles.
• Introduction to Tableau
• Data tasks for data visualization
3 - 10/1
• The process creating visualizations and selecting the
appropriate visual display
• Representing data in basic display types
• Visualizing categorical & time series data
4 - 10/8
• Visualizing multiple variables.
• Statistical displays
5 - 10/15
• Guest speaker from Tableau
• Introduction to the individual and final project
Live remote
online class
(no class
meeting)
6 - 10/22
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Math and data
Showing change instead of raw numbers
Summary statistics in your visualizations
Showing parts of the whole
Visualizing geospatial data
Annotations and pre-attentive attributes
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Week
Topics
7 - 10/29 11/04
Online work
no class
meeting
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•
•
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Dashboard design
Interactive visualizations
Motion
Sharing and collaborating
8 - 11/5 11/11
Online work
no class
meeting
• Project pitches due
• Individual online meetings with the Professor
• D3 tutorial and Javascript tutorial
9 - 11/12
• Project pitch presentations
• Group meetings
10 - 11/19
• Designing effective digital presentations
• Group meetings
NO CLASS 11/26 - THANKSGIVING
11 - 12/03
Project Presentations
12 - 12/10
• Project presentations continued
• Class wrap up
Recording of Classes
Class lectures will be recorded automatically using MediaSite. The links will be
posted to NYU Classes when they are available.
Resources
Data Sources
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Gapminder
Flowing Data
Information Aesthetics
Visual Complexity
Census.gov
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Data.gov
Dataverse Network
Infochimps
Linked Data
Guardian DataBlog
Data Market
Reddit Open Data
Climate Data Sources
Climate Station Records
CDC Data
MBTA Data
World Bank Catalog
Free SVG Maps
Office for National Statistics
StateMaster
NYC OpenData: https://nycopendata.socrata.com/
Google Public Data Directory: http://www.google.com/publicdata/
directory
Examples of Visualizations and References
• Hans Rosling’s Ted Talk http://www.ted.com/talks
hans_rosling_reveals_new_insights_on_poverty.html
• Yau’s Flowing Data website: http://book.flowingdata.com
• Harvard’s data visualization course: http://www.dataviscourse.org/
• ProgrammableWeb. http://www.programmableweb.com/tag/visualization
• Good. http://www.good.is
• ITDashboard. http://www.itdashboard.gov/data_feeds
• Charts and Things. http://chartsnthings.tumblr.com/
• Perceptual Edge. http://www.perceptualedge.com/
• Design IQ. http://www.perceptualedge.com/files/GraphDesignIQ.html
• Nike +: http://yesyesno.com/nike-city-runs
• Traffic in Lisbon: http://www.visualcomplexity.com/vc/project_details.cfm?
id=728&index=728&domain=
• David McCandless: http://www.ted.com/talks/
david_mccandless_the_beauty_of_data_visualization.html [4:00 - 7:39]
• Student Loan Debt: http://www.newyorkfed.org/studentloandebt/
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• Small multiples of unemployment by sector: http://hci.stanford.edu/jheer/
files/zoo/
• Obama’s budget proposal (javascript D3): http://www.nytimes.com/
interactive/2012/02/13/us/politics/2013-budget-proposal-graphic.html
• Olympic Athletes: http://www.nytimes.com/interactive/2012/08/05/sports/
olympics/the-100-meter-dash-one-race-every-medalist-ever.html
• American Time Usage (BLS): http://www.nytimes.com/2009/08/02/business/
02metrics.html?_r=0
• Growth of Target: http://projects.flowingdata.com/target/
• Tufte on exploring multiple forms of display: http://www.youtube.com/
watch?v=Th_1azZA2OY&noredirect=1 [0:00 - 4:00]ß
• Facebook World Map - Produced by Facebook intern, Paul Butler. http://
gigaom.com/2010/12/14/facebook...
• Paris Subway Activity - Eric Fisher - http://www.flickr.com/photos/walkingsf/
• Rich Blocks, Poor Blocks - http://www.richblockspoorblocks.com/
• "Hurricanes since 1851" - by John Nelson, http://uxblog.idvsolutions.com/
• "Flight Patterns" by Aaron Koblin - http://www.aaronkoblin.com/work/fligh...
• "We Feel Fine Project" by Jonathan Harris and Sep Kamvar - http://
wefeelfine.org/
• "Every McDonald's in the US" by Stephen Von Worley - http://
www.datapointed.net/2009/09/di...
• "Colours in Culture" by informationisbeautiful.net - http://
www.informationisbeautiful.net...
• DATAVISUALIZATION.CH: http://selection.datavisualization.ch/
Course Bibliography
Arikan, B. Retrieved from http://burak-arikan.com/
Bederson, B. and Shneiderman, B. (2003).The Craft of Information Visualization:
Readings and Reflections. San Francisco: Morgan Kaufmann Publishers.
Chakrabarti, S. (2003). Mining the web: Discovering knowledge from hypertext
data. New York: Morgan Kaufmann Publishers.
Davenport, T., Harris, J, & Morison, R. (2010). Analytics at work: Smarter
decisions better results. Boston: Harvard Business School Publishing Corporation.
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Dewar, M. (2012). Getting Started with D3. O’Reilly Media.
Few, S. (2006). Information dashboard design: The effective visual
communication of data. Sebastopol: O’Reilly.
Few, S. (2012). Show me the numbers: Designing tables and graphs to
enlighten. Burlingame, CA: Analytics Press.
Fry, B. (2007). Visualizing data. Sebastopol: O’Reilly.
Godin, S. (2007). Really bad PowerPoint: And how to avoid it. Retrieved from
http://www.sethgodin.com/freeprize/reallybad-1.pdf
Goodman, A. (2006). When bad presentations happen to good causes.
Retrieved from http://www.agoodmanonline.com/publications
how_bad_presentations_happen
Harris, J. We feel fine: An exploration of human emotion in six different
movements. Retrieved from http://wefeelfine.org/
Herman, I., Melançon, G. & Marshall, S. Graph Visualization and Navigation in
Information Visualization: a Survey.
Laursen, G. H. & Thorlund, J. (2010). Business analytics for managers: Taking
business intelligence beyond reporting. Hoboken: Wiley
Maeda, J. (2011). Redesigning leadership. Cambridge: MIT Press.
Maeda, J. (2006). Laws of simplicity. Cambridge: MIT Press.
Mayer, R. (2001). Multimedia learning. New York: Cambridge University Press
Migut, M. & Worring, M. Visual exploration of classification models for risk
assessment. Visual analytics science and technology (VAST), 2010 IEEE
Symposium, pp. 11-18. Oct 2010.
Provost, F. & Fawcett, T. (in-press). Towards data science: Fundamental
principles of data mining and data-analytic thinking
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Reas, C & Fry, B. (2007). Processing: A programming handbook for visual
designers and artists. Cambridge: MIT Press
Shang, H. L. (2011) Rainbow: An R Package for Visualizing Functional Time Series
available at: http://journal.r-project.org/archive/2011-2
RJournal_2011-2_Lin~Shang.pdf
Tufte, E. (1990). Envisioning information. Cheshire: Graphics Press
Tufte, E. (1997), Visual explanations: Images and quantities, evidence and
narrative. Cheshire: Graphic Press
Tufte, E. (2001). Visualization of quantitative information. Cheshire: Graphics
Press
Ware, C & Kaufman, M. (2008). Visual thinking for design. Burlington: Morgan
Kaufmann Publishers.
Wong, D. (2011). The Wall Street Journal guide to information graphics: The dos
and don’ts of presenting data, facts and figures. New York: W.W. Norton
& Company
Wroblewski, L. (2003). Visible narratives: Understanding visual organization .
Wurman, R. S. (1989). Hats. Design Quarterly. No. 145. pp. 1-32
Yau, N. (2011). Visualize This: The FlowingData Guide to Design, Visualization,
and Statistics. Indianapolis: O’Reilly.
Yau, N. (2013). Data Points: Visualization that means something. Indianapolis:
O’Reilly.
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