Data Analaytics.04. Data visualization

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Published on July 18, 2014

Author: alrayon

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Data Analytics process in Learning and Academic Analytics projects. Day 4: Data visualization

Data Analytics process in Learning and Academic Analytics projects Day 4: Data visualization Alex Rayón Jerez alex.rayon@deusto.es DeustoTech Learning – Deusto Institute of Technology – University of Deusto Avda. 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 (II)

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

History Definition and characteristics 18th Century 19th Century 20th Century Joseph Priestley William Playfair John Snow Charles J. Minard F. Nightingale Jacques Bertin John Tukey Edward Tufte Leland Wilkinson

History 18th Century: Joseph Priestley Source: http://en.wikipedia.org/wiki/A_New_Chart_of_History#mediaviewer/File:A_New_Chart_of_History_color.jpg

History 18th 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

History 18th Century: William Playfair Source: http://en.wikipedia.org/wiki/William_Playfair

History 19th Century: John Snow Source: http://en.wikipedia.org/wiki/1854_Broad_Street_cholera_outbreak

History 19th Century: Charles J. Minard Source: http://en.wikipedia.org/wiki/Charles_Joseph_Minard

History 19th Century: Florence Nightingale Source: http://en.wikipedia.org/wiki/Florence_Nightingale

History 20th Century: Jacques Bertin Source: http://www.amazon.com/Semiology-Graphics-Diagrams-Networks-Maps/dp/1589482611

History 20th Century: John W. Tukey Source: http://books.google.es/books/about/Exploratory_Data_Analysis.html?id=UT9dAAAAIAAJ&redir_esc=y

History 20th Century: Edward R. Tufte Source: http://www.edwardtufte.com/tufte/books_vdqi

History 20th Century: Leland Wilkinson Source: http://www.amazon.com/Grammar-Graphics-Statistics-Computing/dp/0387245448

History 20th 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

Concepts Introduction ● Data Visualization ● Information visualization ● GeoVisualization ● Visual Analytics ● Information Design ● Infographic

Concepts Introduction (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

Concepts Introduction (III) Source: http://ierg.net/about/briefguide.html#cogtools

Concepts Introduction (IV) Source:http://en.wikipedia.org/wiki/Cognitive_ergonomics

Concepts Data visualization The use of computer-supported, interactive, visual representations of abstract elements to amplify cognition [Card1999]

Concepts Information 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

Concepts Geovisualization ● 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/

Concepts Visual Analytics The science of analytical reasoning facilitated by interactive visual interfaces [ThomasCook2005]

Concepts Visual Analytics (II) [Keim2006]

Concepts Visual 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.”

Concepts Visual 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

Concepts Visual Analytics (V) Interactive visualization Computational analysis Analytical reasoning

Concepts Visual Analytics (VI) ● Combine strengths of both human and electronic data processing [Keim2008] ○ Gives a semi-automated analytical process ○ Use strengths from each

Concepts Visual Analytics (VII) [Verbert2014]

Concepts Information design The practice of presenting information in a way that fosters efficient and effective understanding of it

Concepts Information design (II) Source: http://www.nytimes.com/imagepages/2007/03/17/nyregion/nyregionspecial2/20070318_TRAIN_GRAPHIC.html

Concepts Infographics The graphic visual representations of data, information or knowledge intended to present complex information quickly and clearly

Concepts Infographics (II) Source: http://blog.crazyegg.com/2012/02/22/infographics-how-to-strike-the-elusive-balance-between-data-and-visualization/

Concepts Infographics (III) Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef016760ebbbcd970b-550wi

Concepts Comparison Source: http://www.slideshare.net/SookyoungSong/hci-tutorial0212

Table of contents ● Introduction ● History ● Concept ● Process ● Mistakes in visualization ● Tools ● Designing a Dashboard

Process Introduction 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

Process Data Visualization Reference Model [Chi2000]

Process 1) Data transformation ● Encoding of value ○ Univariate data ○ Bivariate data ○ Multivariate data ● Encoding of relation ○ Lines ○ Maps and diagrams

Process 1) Data transformation (II) ● Encoding of value ○ Univariate data ○ Bivariate data ○ Multivariate data ● Encoding of relation ○ Lines ○ Maps and diagrams

Process 1) Data transformation (III) [Shneiderman1996]

Process 1) Data transformation (IV) Data Visualization [Jarvainen2013] Univariate data

Process 1) Data transformation (V) Data Visualization [Jarvainen2013] Bivariate data

Process 1) Data transformation (VI) Anscombe's quartet Source: http://en.wikipedia.org/wiki/Anscombe's_quartet

Process 1) Data transformation (VII) Data Visualization [Jarvainen2013] Multivariate data

Process 1) Data transformation (VIII) ● Encoding of value ○ Univariate data ○ Bivariate data ○ Multivariate data ● Encoding of relation ○ Lines ○ Maps and diagrams

Process 1) Data transformation (IX) ● Relation ○ A logical or natural association between two or more things ○ Relevance of one to another ○ Connection

Process 1) Data transformation (X) Source: http://www.digitaltrainingacademy.com/socialmedia/2009/06/social_networking_map.php Social network Lines indicate relationship

Process 1) Data transformation (XI)

Process 1) Data transformation (XII) Source: http://www.d3noob.org/2013/02/formatting-data-for-sankey-diagrams-in.html Sankey Diagram

Process 1) Data transformation (XIII) Source: http://en.wikipedia.org/wiki/Harry_Beck

Process 1) Data transformation (XIV) A Tour Through the Visualization Zoo Source: http://homes.cs.washington.edu/~jheer//files/zoo/

Process 1) Data transformation (XV)

Process 2) Visual mapping Ranking of elementary perceptual tasks [ClevelandMcGill1985]

Process 2) 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

Process 2) 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.”

Process 2) Visual mapping (IV) Source: http://www.businessinsider.com/pie-charts-are-the-worst-2013-6 “Save the pies for dessert” (Stephen Few)

Process 2) Visual mapping (V) Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0167631df6f7970b-550wi

Process 2) 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

Process 2) Visual mapping (VII)

Process 2) 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

Process 2) 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

Process 2) Visual mapping (X) Ranking of perceptual tasks [ClevelandMcGill1985]

Process 2) Visual mapping (XI) Remembering what Tufte said: “What are the thinking tasks that these displays are supposed to serve?”

Process 2) Visual mapping (XII) Compare numbers? A bar chart (Source: http://en.wikipedia.org/wiki/Bar_chart)

Process 2) Visual mapping (XIII) Compare numbers? Source: http://www.improving-visualisation.org/img_uploads/2009-03-09_Mon/200939171254.jpg ?

Process 2) Visual mapping (XIV) Temporal variance of a magnitude? A line chart (Source: http://en.wikipedia.org/wiki/Line_graph)

Process 2) Visual mapping (XV) Correlation among two variables? A scatter plot (Source: http://en.wikipedia.org/wiki/Scatter_plot)

Process 2) 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

Process 2) Visual mapping (XVII) Source: http://www.excelcharts.com/blog/uncommon-knowledge-about-pie-charts/#prettyPhoto[gallery]/0/

Process 2) Visual mapping (XVIII) The best strategy? Represent the same data in different ways

Process 2) Visual mapping (XIX) Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef0153903da6ba970b-550wi A map Graphics Numeric table

Process 2) 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.)

Process 2) Visual mapping (XXI) Source: http://www.stonesc.com/Vis08_Workshop/DVD/Reijner_submission.pdf

Process 2) Visual mapping (XXII) Source: http://apandre.wordpress.com/dataviews/choiceofchart/

Process 2) Visual mapping (XXIII) Source: http://apandre.wordpress.com/dataviews/choiceofchart/

Process 2) Visual mapping (XXIV) Source: http://www.visual-literacy.org/periodic_table/periodic_table.html

Process 3) View Transformations Classification of Visual Data Exploration Techniques [Keim2002]

Process Principles ● 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]

Process Principles (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

Process Principles (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 visualization Introduction

Mistakes in visualization Some mistakes Problems?

Mistakes in visualization Some 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 visualization Some mistakes (III) Problems? Try to identify: 1) The biggest donor in 2008 2) 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 visualization Some 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 visualization Some 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 visualization Some mistakes (VI) Source: http://blogs.elpais.com/.a/6a00d8341bfb1653ef015390a98d8a970b-550wi Solution

Mistakes in visualization Some 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

Tools Pentaho Reporting

Tools Many Eyes

Tools Tableau Public

Tools Tableau Public (II) ● Free to use ● 1 GB of storage ● Easy to embed in webpage ● Tableau Public Premium ○ Price based on page views

Tools d3.js

Tools Highcharts

Tools R Studio

Tools ggplot2 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”

Tools ggplot2 in R (II)

Tools Google Charts

Tools Google Charts (II)

Tools Google Fusion Tables

Tools Google Fusion Tables (II)

Tools Simile Widgets

Tools Processing.js

Tools NodeXL

Tools Spotfire

Tools Advizor Analyst

Tools Datawatch

Tools QlikView

Tools Prefuse

Tools Protovis

Table of contents ● History ● Concept ● Process ● Mistakes in visualization ● Tools ● Designing a Dashboard

Dashboard Introduction Fundamentals Perception Vision Color Principles Techniques Representation Presentation Interaction Applications Dashboards Visual Analytics

Dashboard Introduction (II) “Most information dashboards that are used in business today fall far short of their potential” Stephen Few

Dashboard Definition “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]

Dashboard Characteristics ● 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

Dashboard Categories 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

Dashboard Common 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

Dashboard Common 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

Dashboard Common mistakes (III) 5) Choosing inappropiate display media ● Common problem with pie charts ;-) 6) Introducing meaningless variety ● Exhibit unnecessary variety of display media

Dashboard Common 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.

Dashboard Common 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

Dashboard Common 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

Dashboard Common 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

Dashboard Buzz 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

Dashboard Exploratory 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

Dashboard Exploratory 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)

Dashboard Exploratory Analytics Requirements (III) Source: http://www.perceptualedge.com/articles/visual_business_intelligence/differences_in_analytical_tools.pdf

Dashboard Presentation ● Present: to offer to view; display ● Space limitations ○ Scrolling ○ Overview + detail ○ Distortion ○ Supression ○ Zoom and pan ● Time limitations ○ Rapid serial visual presentation ○ Eye-gaze

Dashboard Interactive data visualizations Graphic design Static visualization Data analysis

Dashboard Interactive data visualizations (II) Graphic design Data analysis Interactive design Exploratory Data analysis Interactive visualization User interface design Static visualization

Dashboard Interactive data visualizations (III) ● When is static representation not enough? ○ Scale ■ Too many data points ■ Too many different dimensions ○ Storytelling ○ Exploration ○ Learning

Dashboard Interactive data visualizations (IV) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect

Dashboard Interactive data visualizations (V) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect

Dashboard Interactive 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

Dashboard Interactive data visualizations (VII) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect

Dashboard Interactive data visualizations (VIII) ● Overcome limitations of display size ● Most common technique: panning

Dashboard Interactive data visualizations (IX) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect

Dashboard Interactive data visualizations (X) ● Show a different arrangement

Dashboard Interactive data visualizations (XI) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect

Dashboard Interactive data visualizations (XII) ● Change visual variables: colors, sizes, orientation, font, shape

Dashboard Interactive data visualizations (XIII) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect

Dashboard Interactive data visualizations (XIV) Show more or less detail: focus + context

Dashboard Interactive data visualizations (XV) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect

Dashboard Interactive data visualizations (XVI) Filter: Show something conditionally

Dashboard Interactive data visualizations (XVII) ● Select ● Explore ● Reconfigure ● Encode ● Abstract/Elaborate ● Filter ● Connect

Dashboard Interactive data visualizations (XVIII) Show related items: brushing and linking

Dashboard Interaction framework ● Continuous interaction ● Stopped interaction ● Passive interaction ● Composite interaction

Dashboard Interaction framework (II) Continuous interaction

Dashboard Interaction framework (III) Stopped interaction

Dashboard Interaction 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

Dashboard Interaction framework (V) Passive interaction

Dashboard Interaction framework (VI) Composite interaction Source: http://vis.berkeley.edu/papers/generalized_selection/

Dashboard Steps Source: http://www.tableausoftware.com/es- es/trial/tableau-software 1. Choose metrics that matter 2. Keep it visual 3. Make it interactive 4. Keep it current or don’t bother 5. 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.

Courses KU 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 resources http://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 Jerez alex.rayon@deusto.es DeustoTech Learning – Deusto Institute of Technology – University of Deusto Avda. Universidades 24, 48007 Bilbao, Spain www.deusto.es

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