Data Analytics process in Learning and Academic Analytics projects. Day 4: Data visualization
Text of Data Analaytics.04. Data visualization
Data Analytics process in Learning and Academic Analytics projects Day 4: Data visualization Alex Rayn Jerez [email protected] 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, Im 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 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 (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
