data analaytics.04. data visualization
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Data Analytics process in Learning and Academic
Analytics projects
Day 4: Data visualization
Alex Rayón Jerezalex.rayon@deusto.es
DeustoTech Learning – Deusto Institute of Technology – University of DeustoAvda. Universidades 24, 48007 Bilbao, Spain
www.deusto.es
“Perfection is achieved not when there is nothing more to add, but when there is nothing
left to take away”
Antoine de Saint-Exupery
Narrative+
Design+
Statistics
“[...] people almost universally use story narratives to represent, reason about, and make
sense of contexts involving multiple interacting agents, using motivations and goals to explain
both observed and possible future actions. With regard to learning analytics, I’m seeing this as how
it can contribute to the retrospective understanding and sharing of what transpired
within the operational contexts”
[Zachary2013]
Objectives
● Know the foundations○ Learn the principles of information visualization
● Learn about existing techniques and systems○ Effectiveness
○ Develop the knowledge to select appropriate visualization techniques for particular tasks
● Build○ Build your own visualizations○ Apply theoretical foundations
Table of contents
● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
Table of contents
● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
Introduction
● Danger of getting lost in data, which may be:○ Irrelevant to the current task in hand○ Processed in an inappropriate way○ Presented in an inappropriate way
Source: http://www.planetminecraft.com/server/padlens-maze/
Introduction (III)
● Good graphics….○ Point relationships, trends or patterns○ Explore data to infer new things○ To make something easy to understand○ To observe a reality from different viewpoints○ To achieve an idea to be memorized
Introduction (IV)
● It is a way of expressing○ Like maths, music, drawing or writing
● So, it has some rules to respect
Source: http://powerlisting.wikia.com/wiki/Mathematics_Manipulation
Table of contents
● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
HistoryDefinition and characteristics
18th Century 19th Century 20th Century
Joseph PriestleyWilliam Playfair
John SnowCharles J. Minard
F. Nightingale
Jacques BertinJohn Tukey
Edward TufteLeland Wilkinson
History18th Century: Joseph Priestley
Source: http://en.wikipedia.org/wiki/A_New_Chart_of_History#mediaviewer/File:A_New_Chart_of_History_color.jpg
History18th Century: Joseph Priestley (II)
● Lectures on History and General Policy (1788)○ A Chart of Biography (1765)○ A New Chart of History (1769)
● Beautiful metaphors of an inaccurate and abstract dimension (time) translated to a concrete one (space)○ Time thinking consumes cognitive
resources
History18th Century: William Playfair
Source: http://en.wikipedia.org/wiki/William_Playfair
History19th Century: John Snow
Source: http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak
History19th Century: Charles J. Minard
Source: http://en.wikipedia.org/wiki/Charles_Joseph_Minard
History19th Century: Florence Nightingale
Source: http://en.wikipedia.org/wiki/Florence_Nightingale
History20th Century: Jacques Bertin
Source: http://www.amazon.com/Semiology-Graphics-Diagrams-Networks-Maps/dp/1589482611
History20th Century: John W. Tukey
Source: http://books.google.es/books/about/Exploratory_Data_Analysis.html?id=UT9dAAAAIAAJ&redir_esc=y
History20th Century: Edward R. Tufte
Source: http://www.edwardtufte.com/tufte/books_vdqi
History20th Century: Leland Wilkinson
Source: http://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448
History20th Century: Leland Wilkinson
Source: http://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448
Table of contents
● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
ConceptsIntroduction
● Data Visualization● Information visualization● GeoVisualization● Visual Analytics● Information Design● Infographic
ConceptsIntroduction (II)
● Cognitive tools: extending human perception and learning○ Were invented and developed by our ancestors for
making sense of the world and acting more effectively within it
■ Stories that helped people to remember things by making knowledge more engaging
■ Metaphors that enabled people to understand one thing by seeing it in terms of another
■ Binary oppositions like good/bad that helped people to organize and categorize knowledge
ConceptsIntroduction (III)
Source: http://ierg.net/about/briefguide.html#cogtools
ConceptsIntroduction (IV)
Source:http://en.wikipedia.org/wiki/Cognitive_ergonomics
ConceptsData visualization
The use of computer-supported, interactive, visual
representations of abstract elements to amplify cognition
[Card1999]
ConceptsInformation visualization
● Also known as InfoVis● Focuses on visualizing non-physical, abstract
data such as financial data, business information, document collections and abstract conceptions
● However, inadequately supported decision making [AmarStasko2004]○ Limited affordances○ Predetermined representations○ Decline of determinism in decision-making
ConceptsGeovisualization
● Geo-spatial data is special since it describes objects or phenomena that are related to a specific location in the real world
Source: http://www.boostlabs.com/why-geovisualization-geographic-visualization-works/
ConceptsVisual Analytics
The science of analytical reasoning facilitated by
interactive visual interfaces
[ThomasCook2005]
ConceptsVisual Analytics (II)
[Keim2006]
ConceptsVisual Analytics (III)
[Keim2006]
“Visual analytics is more than just visualization and can rather be seen as an integrated approach
combining visualization, human factors and data analysis. [...]integrates methodology from information analytics, geospatial analytics, and scientific analytics. Especially human factors (e.g., interaction, cognition,
perception, collaboration, presentation, and dissemination) play a key role in the communication
between human and computer, as well as in the decisionmaking process.”
ConceptsVisual Analytics (IV)
● [Shneiderman2002] suggests combining computational analysis approaches such as data mining with information visualization
● People use visual analytics tools and techniques to○ Synthesize information and derive insight from
massive, dynamic, ambiguous and often conflicting data
○ Detect the expected and discover the unexpected
○ Provide timely, defensible, and understandable assessments
○ Communicate assessment effectively for action
ConceptsVisual Analytics (V)
Interactivevisualization
Computational analysis
Analyticalreasoning
ConceptsVisual Analytics (VI)
● Combine strengths of both human and electronic data processing [Keim2008]○ Gives a semi-automated analytical process○ Use strengths from each
ConceptsVisual Analytics (VII)
[Verbert2014]
ConceptsInformation design
The practice of presenting information
in a way that fosters efficient and effective
understanding of it
ConceptsInformation design (II)
Source: http://www.nytimes.com/imagepages/2007/03/17/nyregion/nyregionspecial2/20070318_TRAIN_GRAPHIC.html
ConceptsInfographics
The graphic visual representations of data,
information or knowledge intended to present complex
information quickly and clearly
ConceptsInfographics (II)
Source: http://blog.crazyegg.com/2012/02/22/infographics-how-to-strike-the-elusive-balance-between-data-and-visualization/
ConceptsInfographics (III)
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef016760ebbbcd970b-550wi
ConceptsComparison
Source: http://www.slideshare.net/SookyoungSong/hci-tutorial0212
Table of contents
● Introduction● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
ProcessIntroduction
The purpose of analytical displays of evidence is to assist thinking. Consequently, in constructing displays of evidence, the first question
is, “What are the thinking tasks that these displays are supposed to serve?” The central claim of the book is that effective analytic
designs entail turning thinking principles into seeing principles. So, if the thinking task is to understand causality, the task calls for a design principle: “Show causality.” If a thinking task is to answer a question
and compare it with alternatives, the design principle is: “Show comparisons.” The point is that analytical designs are not to be
decided on their convenience to the user or necessarily their readability or what psychologists or decorators think about them;
rather, design architectures should be decided on how the architecture assists analytical thinking about evidence.
Edward T. Tufte in an interview
ProcessData Visualization Reference Model
[Chi2000]
Process1) Data transformation
● Encoding of value○ Univariate data○ Bivariate data○ Multivariate data
● Encoding of relation○ Lines○ Maps and diagrams
Process1) Data transformation (II)
● Encoding of value○ Univariate data○ Bivariate data○ Multivariate data
● Encoding of relation○ Lines○ Maps and diagrams
Process1) Data transformation (III)
[Shneiderman1996]
Process1) Data transformation (IV)
Data Visualization [Jarvainen2013]
Univariate data
Process1) Data transformation (V)
Data Visualization [Jarvainen2013]
Bivariate data
Process1) Data transformation (VI)
Anscombe's quartetSource: http://en.wikipedia.org/wiki/Anscombe's_quartet
Process1) Data transformation (VII)
Data Visualization [Jarvainen2013]
Multivariate data
Process1) Data transformation (VIII)
● Encoding of value○ Univariate data○ Bivariate data○ Multivariate data
● Encoding of relation○ Lines○ Maps and diagrams
Process1) Data transformation (IX)
● Relation○ A logical or natural association between two or more
things○ Relevance of one to another○ Connection
Process1) Data transformation (X)
Source: http://www.digitaltrainingacademy.com/socialmedia/2009/06/social_networking_map.php
Social network
Lines indicate relationship
Process1) Data transformation (XI)
Process1) Data transformation (XII)
Source: http://www.d3noob.org/2013/02/formatting-data-for-sankey-diagrams-in.html
Sankey Diagram
Process1) Data transformation (XIII)
Source: http://en.wikipedia.org/wiki/Harry_Beck
Process1) Data transformation (XIV)
A Tour Through the Visualization Zoo Source: http://homes.cs.washington.edu/~jheer//files/zoo/
Process2) Visual mapping
Ranking of elementary perceptual tasks [ClevelandMcGill1985]
Process2) Visual mapping (II)
● Two researchers of the AT&T Bell Labs, William S. Cleveland y Robert McGill, published a core article in the Journal of the American Statistical Association
● The title was: “Graphical perception: theory, experimentation, and application to the development of graphical methods”
● It proposes a guide the most suitable visual representation depending on the objective of each graph
Process2) Visual mapping (III)
“A graphical form that involves elementary perceptual tasks that lead to more accurate judgements than another
graphical form (with the same quantitative information) will result in a
better organization and increase the chances of a correct perception of
patterns and behavior.”
Process2) Visual mapping (IV)
Source: http://www.businessinsider.com/pie-charts-are-the-worst-2013-6
“Save the pies for dessert”
(Stephen Few)
Process2) Visual mapping (V)
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0167631df6f7970b-550wi
Process2) Visual mapping (VI)
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef016302299aa9970d-550wi
In some representations, the accuracy is not the
objective, but the perception of general
patterns, concentrations, aggregations, trends, etc.
The shapes in the low part of the list could be
quite useful
Process2) Visual mapping (VIII)
Depictive graphics Symbolic graphics
Source: http://www.dnr.mo.gov/regions/regions.htm
Source: http://trevorcairney.blogspot.com.es/2010_04_01_archive.html
Source: http://pubs.usgs.gov/of/2005/1231/sumstat.htm
Process2) Visual mapping (IX)
● Maria Kozhevnikov, states that not everybody understands statistical graphs easily○ It depends on some activation patterns within the
brain
● In one of her studies, she exposed how artists, architects and scientifics interpret graphs in different ways○ The same happens with regular readers
Process2) Visual mapping (X)
Ranking of perceptual tasks [ClevelandMcGill1985]
Process2) Visual mapping (XI)
Remembering what Tufte said: “What are the thinking tasks
that these displays are supposed to serve?”
Process2) Visual mapping (XII)
Compare numbers?
A bar chart (Source: http://en.wikipedia.org/wiki/Bar_chart)
Process2) Visual mapping (XIII)
Compare numbers?
Source: http://www.improving-visualisation.org/img_uploads/2009-03-09_Mon/200939171254.jpg
?
Process2) Visual mapping (XIV)
Temporal variance of a magnitude?
A line chart (Source: http://en.wikipedia.org/wiki/Line_graph)
Process2) Visual mapping (XV)
Correlation among two variables?
A scatter plot(Source: http://en.wikipedia.org/wiki/Scatter_plot)
Process2) Visual mapping (XVI)
Difference between two variables?
As Cleveland and McGill states, our brain has problems comparing angles, curves and directions → if we want to show the difference, we must represent
directly the difference
or
Process2) Visual mapping (XVII)
Source: http://www.excelcharts.com/blog/uncommon-knowledge-about-pie-charts/#prettyPhoto[gallery]/0/
Process2) Visual mapping (XVIII)
The best strategy?
Represent the same data in different ways
Process2) Visual mapping (XIX)
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903da6ba970b-550wi
A map
Graphics
Numeric table
Process2) Visual mapping (XX)
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903da6ba970b-550wi
Different visualization
configurations
Filters (zoom, search tool, select data by continent and size)
Depth search (click in the bubbles and show
more data, etc.)
Process2) Visual mapping (XXI)
Source: http://www.stonesc.com/Vis08_Workshop/DVD/Reijner_submission.pdf
Process2) Visual mapping (XXII)
Source: http://apandre.wordpress.com/dataviews/choiceofchart/
Process2) Visual mapping (XXIII)
Source: http://apandre.wordpress.com/dataviews/choiceofchart/
Process2) Visual mapping (XXIV)
Source: http://www.visual-literacy.org/periodic_table/periodic_table.html
Process3) View Transformations
Classification of Visual Data Exploration Techniques [Keim2002]
ProcessPrinciples
● Summary of Tufte’s principles○ Tell the truth
■ Graphical integrity○ Do it effectively with clarity, precision, etc.
■ Design aesthetics
“The success of a visualization is based on deep knowledge and care about the substance, and the quality, relevance and integrity of the content”
[Tufte1983]
ProcessPrinciples (II)
● Design aesthetics: five principles○ Above all else show the data○ Maximize the data-ink ratio, within reason○ Erase non-data ink, within reason○ Erase redundant data-ink○ Revise and edit
ProcessPrinciples (III)
● Preattentive attributes○ Color○ Size○ Orientation○ Placement on page
or
Source: http://www.storytellingwithdata.com/2011/10/google-example-preattentive-attributes.html
Table of contents
● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
Mistakes in visualizationIntroduction
Mistakes in visualizationSome mistakes
Problems?
Mistakes in visualizationSome mistakes (II)
● Multidimensionality● Lack of context and
understanding○ Are the numbers
relevant?○ What do they mean?○ How do they affect
to me?
An onion with just one layer
Mistakes in visualizationSome mistakes (III)
Problems?
Try to identify:
1) The biggest donor in 20082) The smallest donor in 2009
3) The variation between 2008 and 2009
4) Which region received the biggest amount of moneySource: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903125d9970b-550wi
Mistakes in visualizationSome mistakes (IV)
● A map is not the best way to represent that data
● If I want to answer previously stated questions I must search for the relevant figures, memorize them and then compare
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903125d9970b-550wi
Mistakes in visualizationSome mistakes (V)
Problems?
The graph tries to reveal the size of UK’s deficit (the black
box in the right side)
Does the graph helps in the contextualization?
Can we analyze data deeper?How can we compare?Know the differences?
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef015390a96894970b-550wi
Mistakes in visualizationSome mistakes (VI)
Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef015390a98d8a970b-550wi
Solution
Mistakes in visualizationSome mistakes (VII)
Problems?
Bar values should start at zero
Source: http://www.qualitydigest.com/inside/quality-insider-article/asci-customer-satisfaction-airlines-remains-low.html
Table of contents
● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
ToolsTableau Public (II)
● Free to use● 1 GB of storage● Easy to embed in webpage● Tableau Public Premium
○ Price based on page views
Toolsggplot2 in R
An implementation of the Grammar of Graphics by Leland Wilkinson
“In brief, the grammar tells us that a statistical graphic is a mapping from data to aesthetic
attributes (color, shape, size) of geometric objects (points, lines, bars). The plot may also contain statistical transformations of the data and is
drawn on a specific coordinate system”
Table of contents
● History● Concept● Process● Mistakes in visualization● Tools● Designing a Dashboard
DashboardIntroduction
Fundamentals
PerceptionVisionColor
Principles
Techniques
RepresentationPresentationInteraction
Applications
DashboardsVisual
Analytics
DashboardIntroduction (II)
“Most information dashboards that are used in business today fall far short of their potential”
Stephen Few
DashboardDefinition
“A dashboard is a visual display of the most important information needed to
achieve one or more objectives; consolidated and arranged on a single
screen so the information can be monitored at a glance”
[Few2007]
DashboardCharacteristics
● Visual displays● Display information needed to achieve specific
objectives● Fits on a single computer screen● Are used to monitor information at a glance● Have small, concise, clear, intuitive display
mechanisms● Are customized
DashboardCategories
Role Strategic, Operational, Analytical
Type of data Quantitative, Non-quantitative
Data domain Sales, Finance, Marketing, Manufacturing, Human Resources, Learning, etc.
Type of measures Balanced Scored Cards, Six Sigma, Non-performance
Span of data Enterprise wide, Departmental, Individual
Update frequency Monthly, Weekly, Daily, Hourly, Real-time
Interactivity Static display, Interactive display
Mechanisms of display
Primarily graphical, Primarily text, Integration of graphics and text
Portal functionality Conduit to additional data. No portal functionality
DashboardCommon mistakes
1) Exceeding the boundaries of a single screen
● Information that appears on dashboards is often fragmented in one of two ways:○ Separated into discrete screens to which one must
navigate
○ Separated into different instances of a single screen that are accesses through same form of interaction
DashboardCommon mistakes (II)
2) Supplying inadequate context for the data
● Fail to provide adequate context to make the measures meaningful
3) Displaying excessive detail or precision
● Show unnecessary detail
4) Choosing a deficient measure
● Use of measures that fail to directly express the intended message
DashboardCommon mistakes (III)
5) Choosing inappropiate display media
● Common problem with pie charts ;-)
6) Introducing meaningless variety
● Exhibit unnecessary variety of display media
DashboardCommon mistakes (IV)
7) Using poorly designed display media● A legend was used to label and assign values to the slices
of the pie. This forces our eyes to bounce back and forth between the graph and the legend to glean meaning, which is a waste of time and effort when the slices could have been labeled directly.
● The order of the slices and the corresponding labels appears random. Ordering them by size would have provided useful information that could have been assimilated instantly.
● The bright colors of the pie slices produce sensory overkill. Bright colors ought to be reserved for specific data that should stand out from the rest.
DashboardCommon mistakes (V)
8) Encoding quantitative data inaccurately
9) Arranging the data poorly
● The most important data ought to be prominent
● Data that require immediate attention ought to stand out
● Data that should be compared ought to be arranged and visually designed to encourage comparisons
DashboardCommon mistakes (VI)
10) Highlighting important data ineffectively or not at all
● Fail to differentiate data by its importance○ Giving relatively equal prominence to everything on
the screen
11) Cluttering the display with useless decoration
● Try to look something that is not● It results in useless and distracting decoration
DashboardCommon mistakes (VII)
12) Misusing or overusing color
● Too much color undermines its power
13) Designing an unattractive visual display
● The fundamental challenge of dashboard design is to effectively display a great deal of often disparate data in a small amount of space
DashboardBuzz words
● Dashboards○ Presents information in a way that is easy to read and
interpret
● Key Performance Indicator○ Success or steps leading to the success of a goal
DashboardExploratory Analytics Requirements
● The tool ideally exhibits the following characteristics:○ Provides every analytical display, interaction, and
function that might be needed by those who use it for their analytical tasks
○ Grounds the entire analytical experience in a single,
central workspace, with all displays, interactions, and functions within easy reach from there
DashboardExploratory Analytics Requirements (II)
● The tool ideally exhibits the following characteristics:○ Supports efficient, seamless transitions from one step
to the next of the analytical process, even though the
sequence and nature of those steps cannot be anticipated
○ Doesn’t require a lot of fiddling with things to whip
them into shape to support your analytical needs
(such as having to take time to carefully position and size graphs on the screen)
DashboardExploratory Analytics Requirements (III)
Source: http://www.perceptualedge.com/articles/visual_business_intelligence/differences_in_analytical_tools.pdf
DashboardPresentation
● Present: to offer to view; display● Space limitations
○ Scrolling○ Overview + detail○ Distortion○ Supression○ Zoom and pan
● Time limitations○ Rapid serial visual presentation○ Eye-gaze
DashboardInteractive data visualizations
Graphic design
Staticvisualization
Data analysis
DashboardInteractive data visualizations (II)
Graphic design
Data analysis
Interactive design
ExploratoryData analysis
Interactivevisualization
Userinterface
design
Static visualization
DashboardInteractive data visualizations (III)
● When is static representation not enough?○ Scale
■ Too many data points■ Too many different dimensions
○ Storytelling○ Exploration○ Learning
DashboardInteractive data visualizations (IV)
● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect
DashboardInteractive data visualizations (V)
● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect
DashboardInteractive data visualizations (VI)
Pick a detail from a larger dataset to keep track of it
Source: http://en.wikipedia.org/wiki/Closest_pair_of_points_problem
DashboardInteractive data visualizations (VII)
● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect
DashboardInteractive data visualizations (VIII)
● Overcome limitations of display size● Most common technique: panning
DashboardInteractive data visualizations (IX)
● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect
DashboardInteractive data visualizations (X)
● Show a different arrangement
DashboardInteractive data visualizations (XI)
● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect
DashboardInteractive data visualizations (XII)
● Change visual variables: colors, sizes, orientation, font, shape
DashboardInteractive data visualizations (XIII)
● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect
DashboardInteractive data visualizations (XIV)
Show more or less detail: focus + context
DashboardInteractive data visualizations (XV)
● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect
DashboardInteractive data visualizations (XVI)
Filter: Show something conditionally
DashboardInteractive data visualizations (XVII)
● Select● Explore● Reconfigure● Encode● Abstract/Elaborate● Filter● Connect
DashboardInteractive data visualizations (XVIII)
Show related items: brushing and linking
DashboardInteraction framework
● Continuous interaction● Stopped interaction● Passive interaction● Composite interaction
DashboardInteraction framework (II)
Continuous interaction
DashboardInteraction framework (III)
Stopped interaction
DashboardInteraction framework (IV)
Passive interaction
Two important aspects of passive interaction:
1) During typical use of a visualization tool, most of the user’s time is spent on passive interaction
– often involving eye movement
2) Passive interaction does not imply a static representation
DashboardInteraction framework (VI)
Composite interaction
Source: http://vis.berkeley.edu/papers/generalized_selection/
DashboardSteps
Source: http://www.tableausoftware.com/es-es/trial/tableau-software
1. Choose metrics that matter
2. Keep it visual3. Make it interactive4. Keep it current or
don’t bother5. Make it simple to
access and use
References[AmarStasko2005] Amar, R. A., & Stasko, J. T. (2005). Knowledge precepts for design and evaluation of information visualizations. Visualization and Computer Graphics, IEEE Transactions on, 11(4), 432-442.
[Cairo] Alberto Cairo [Online]. URL: https://twitter.com/albertocairo
[Chi2000] Chi, Ed H. "A taxonomy of visualization techniques using the data state reference model." Information Visualization, 2000. InfoVis 2000. IEEE Symposium on. IEEE, 2000.
[ClevelandMcGill1985] Cleveland, William S., and Robert McGill. "Graphical perception and graphical methods for analyzing scientific data." Science 229.4716 (1985): 828-833.
[Few2004] Few, Stephen. "Show me the numbers." Analytics Pres (2004).
[Few2007] Few, Stephen. "Dashboard confusion revisited." Perceptual Edge (2007).
[Fry] Ben Fry [Online]. URL: http://benfry.com/
[Jarvinen2013] Data visualization [Online]. URL: http://lib.tkk.fi/Lic/2013/urn100763.pdf
[Keim2006] Keim, D.A.; Mansmann, F. and Schneidewind, J. and Ziegler, H., Challenges in Visual Data Analysis, Proceedings of Information Visualization (IV 2006), IEEE, p. 9-16, 2006.
[Kosslyn] Kosslyn Laboratory [Online]. URL: http://isites.harvard.edu/icb/icb.do?keyword=kosslynlab&pageid=icb.page250946
[Malamed] Visual Language for Designers: Principles for Creating Graphics that People Understand [Online]. URL: http://www.amazon.com/Visual-Language-Designers-Principles-Understand/dp/1592535151
[Shneiderman1996] Shneiderman, Ben. "The eyes have it: A task by data type taxonomy for information visualizations." Visual Languages, 1996. Proceedings., IEEE Symposium on. IEEE, 1996.
[Shneiderman2002] Shneiderman, B. (2002) Inventing discovery tools: combining information visualization with data mining1. Information visualization, 1(1), 5-12.
[ThomasCook2005] J.J. Thomas and K.A. Cook, "A Visual Analytics Agenda," IEEE Computer Graphics & Applications, vol. 26, pp. 10-13, 2006.
[Verbert2014a] Visual Analytics [Online]. URL: http://www.slideshare.net/kverbert/in-34471961
[Yau] Nathan Yau [Online]. URL: http://flowingdata.com/about-nathan/
[Zachary2013] Zachary, W., Rosoff, A., Miller, L. C., & Read, S. J. (2013). Context as a Cognitive Process: An Integrative Framework for Supporting Decision Making. Paper presented at the STIDS.
CoursesKU Leuven [Online]. URL: http://ariadne.cs.kuleuven.be/wiki/index.php/MM-Course1314
Berkeley [Online]. URL: http://blogs.ischool.berkeley.edu/i247s13/
Columbia university [Online]. URL: http://columbiadataviz.wordpress.com/student-work/
Information Visualization MOOC [Online]. URL: http://ivmooc.cns.iu.edu/
Additional resourceshttp://infosthetics.com/
http://visualizing.org
http://www.visualcomplexity.com/vc/
http://visual.ly/
http://flowingdata.com
http://www.infovis-wiki.net
Data Analytics process in Learning and Academic
Analytics projects
Day 4: Data visualization
Alex Rayón Jerezalex.rayon@deusto.es
DeustoTech Learning – Deusto Institute of Technology – University of DeustoAvda. Universidades 24, 48007 Bilbao, Spain
www.deusto.es
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