INTRODUCTION TO VISUAL ANALYTICS, CSDM 1N50 Please fill out this survey (if you haven’t already): https://www.surveymonkey.com/r/RKJJ6R3 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:
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INTRODUCTION TO VISUAL ANALYTICS, CSDM 1N50
Please fill out this survey (if you haven’t already):
https://www.surveymonkey.com/r/RKJJ6R3
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
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.
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