Introduction to Data Visualization

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:

CSDM 1N50

Ana Jofre

Kashmeera Megnath

Maria Astrid Gubitsch Martin Lui

Introductions

https://www.surveymonkey.com/r/RKJJ6R3

Leonardo Restivo

Sarah Obtinalla

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.

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)

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

What is Data Visualization?

http://images.all-free-download.com/images/graphicthumb chart_elements_of_color_vector_graphic_530706.jpg

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/

•  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.

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

churchnumbers.com/less-is-more/

Avoid distorting what the data have to say

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/

Manifest Justice Exhibition, Los Angeles, May 2015 http://www.afropunk.com/profiles/blogs/feature-manifestjustice-art-exhibit-in-los-angeles

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.

http://www.infovis-wiki.net/index.php?title=Visual_Variables&oldid=142161

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  

https://gist.github.com/gjreda/f3e6875f869779ec03db

http://www.gregreda.com/2013/03/03/web-scraping-101-with-python/

Scraping Data off a Webpage with Python

Facepager – scraping tool for facebook and twitter

Scraping data from websites

https://github.com/strohne/Facepager https://www.youtube.com/watch?v=S9kYApoR8U4

  You can get your facebook data from Wolfram Alpha http://www.wolframalpha.com/facebook/