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Introduction to Data Visualization
1. INTRODUCTION TO VISUAL ANALYTICS,
CSDM 1N50
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Hello, and welcome!
- Introductions, Course objectives
- Overview – What is data visualization, and what makes a good visualization?
- Data – types of data, mapping data to visual variables, where to get data,
TODAY:
3. COURSE DESCRIPTION
The Introduction to Visual Analytics course will expose students to:
1) fundamental concepts in data, statistics, data visualization and visual analytics
2) the diversity of data visualization work across different domains
c) hands-on work with data using existing open source data visualization tools.
The Introduction to Visual Analytics course covers the basic principles of data
analysis, cognitive perception, and design. It includes a survey of data
visualization work in various domains (art, journalism, information design,
network analysis, science, and map-based applications) as well as different media
(print, screen, interactive, 3d). Students will apply these principles, and take
inspiration from the examples, to create their own visualizations.
LEARNING OUTCOMES
Upon the successful completion of this course, students will have:
learned some basic principles in data analysis, design, and data visualization
been exposed to a wide range of data visualization work across different domains
created their own visualizations using the tools provided in class
TEACHING METHODS & DELIVERY
This is a studio-based learning environment. Teaching methods and delivery will
include a combination of lectures, demonstrations, critiques, individual and group
discussions and in class labs. Attendance will be taken at the beginning of each
class. Two absences will result in an incompletion of the course.
4. WEEK 1 October 31
• Introductions
• Topic and Course Overview
• Introduction to data visualization – some basic principles
• What is data?
• Extracting data
WEEK 2 November 7
• Processing data: curating, managing, cleaning data.
• Review of statistics
• Introduction to some data visualization tools
WEEK 3 November 14
• Visualization Design
• Cognitive science and perception
• Bertin’s semiotics and use of metaphors
• How not to lie with graphics
Weekly Plan (subject to adjustments)
5. WEEK 4 November 21
• Taxonomy of representation
• Survey of visualization typologies and organizational structures (spatial,
temporal, network, multi-dimensional, treemaps etc.)
• Students will have time today to work with their choice of data visualization
tool(s) to create a visualization
WEEK 5 November 28
• Infographics vs data visualization vs visual analytics (Discussion)
• Review of best practices (Discussion)
• Beyond visualization: data materialization, data sonification, ambient data
displays
• Students will have time today to work with their choice of data visualization
tool(s) to create a visualization
WEEK 6 December 5
• Synthesis and review
• Students will have time today to work with their choice of data visualization
tool(s) finish their visualizations
• Student critique
6. What is Data Visualization?
http://images.all-free-download.com/images/graphicthumb chart_elements_of_color_vector_graphic_530706.jpg
7. What is Data Visualization?
http://www.ted.com/talks/david_mccandless_the_beauty_of_data_visualization#t-576041
http://www.informationisbeautiful.net/
https://public.tableau.com/s/gallery
https://github.com/mbostock/d3/wiki/Gallery
http://labratrevenge.com/nation-of-poverty/
http://demographics.coopercenter.org/DotMap/
http://www.davidmccandless.com/
http://www.iadb.org/en/topics/energy/energy-database/energy-database,19144.html
http://www.informationisbeautiful.net/visualizations/billion-dollar-o-gram-2013/
http://infobeautiful4.s3.amazonaws.com/2015/05/1276_left_right_usa.png
Gapminder!
http://www.on-broadway.nyc/
8.
9.
10. • Show the data
• Induce the viewer to think about the substance of the findings rather
that the methodology, the graphical design, or other aspects
• Avoid distorting what the data have to say
• Present many numbers in a small space, i.e, efficiently
• Make large data sets coherent
• Encourage the eye to compare different pieces of data
• Reveal the data at several levels of detail, from a broad overview to the
fine structure
• Serve a clear purpose: description, exploration, tabulation, decoration
• Be closely integrated with the statistical and verbal descriptions of the
data set
Principles of Graphical Excellence
from E.R. Tufte
E. R. Tufte. The Visual Display of Quantitative Information, 2nd Ed. Graphics Press, Cheshire, Connecticut, 2001.
11. Show the data means high data to ink ratio.
http://socialmediaguerilla.com/content-marketing/less-is-more-improving-the-data-ink-ratio/
www.darkhorseanalytics.com
14. Beyond Visualizations
Fundament, Andreas Nicolas Fischer. 2008.
http://anf.nu/fundament/
Tokyo earthquake data sculpture. Luke Jerram
http://www.lukejerram.com/projects/t%C5%8Dhoku_earthquake
http://dl.acm.org/citation.cfm?id=2481359
Jansen, Yvonne, Pierre Dragicevic, and Jean-Daniel
Fekete. "Evaluating the efficiency of physical
visualizations." Proceedings of the SIGCHI Conference
on Human Factors in Computing Systems. ACM, 2013.
Keyboard frequency sculpture. Michael Knuepfel
aviz.fr/Research/PassivePhysicalVisualizations
http://dataphys.org/list/tag/data-sculpture/
15. Manifest Justice Exhibition, Los Angeles, May 2015
http://www.afropunk.com/profiles/blogs/feature-manifestjustice-art-exhibit-in-los-angeles
16. DATA
Quantitative
(Numerical)
Qualitative
(Descriptive)
Nominal
Data has no
natural order.
Includes objects,
names, and
concepts.
Examples:
gender, race,
religion, sport
Ordinal
Data can be
arranged in order
or rank
Examples: sizes
(small, medium,
large), attitudes
(strongly
disagree,
disagree, neutral,
agree, strongly
agree), house
number.
Continuous
Data is measured
on a continuous
scale.
Examples:
Temperature,
length, height
Discrete
Data is
countable, and
exists only in
whole numbers
Examples:
Number of
people taking
this class,
Number of candy
bars collected on
Halloween.
18. Some Data Sources:
Universities:
http://lib.stat.cmu.edu/DASL/
http://sunsite3.berkeley.edu/wikis/datalab/
www.stat.ucla.edu/data/
General Data Applications
www.freebase.com
http://infochimps.org
http://numbrary.com
http://aggdata.com
http://aws.amazon.com/publicdatasets
Geography
www.census.gov/geo/www/tiger/
www.openstreetmap.org
www.geocommons.com
World
www.globalhealthfacts.org
http://data.un.org
www.who.int/research/en/
http://stats.oecd.org/
http://data.worldbank.org
https://www.cia.gov/library/
publications/the-world-factbook/
index.html
US Government
www.census.gov
http://data.gov
www.followthemoney.org
www.opensecrets.org
Canadian Government
http://www12.statcan.gc.ca/census-
recensement/index-eng.cfm
http://open.canada.ca/en/open-data
