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Andy Kirk talk at Big Data World Europe, September 2012


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Published in: Design, Technology, Travel

Andy Kirk talk at Big Data World Europe, September 2012

  1. Understanding learning inorder to implement efficient visualisation methods Andy Kirk @visualisingdata
  2. The stuff you need to learn to do data visualisation well Andy Kirk @visualisingdata
  3. Hebden Bridge
  4. Data Visualisation Blogger
  5. Data Visualisation Design Consultant
  6. Data Visualisation Trainer ata Visualisation Training Courses Introduction to D le Current Public Schedu Visual isation The Growth of Data middle of 2012: public training course through to the means for These are the scheduled ded us with ubiquitous Arts, Copenhagen | £250 COP2 in technology have provi nts of data. Where once data sh Academy of Fine Exponential advances lising incredible amou Thu 8 Mar | Royal Dani Arts, Copenhagen | £250 COP1 ing, recording and mobi attitudes as consumers have sh Academy of Fine creat dance. Our Fri 9 Mar | Royal Dani on | £235 LON3 was scarce, now it is captured in abun for visual insight e, University of Lond openness and yearn Thu 26 Apr | Senate Hous Y, New York City | £250 NYC1 nd transparency and ol of Journalism, CUN also evolved: we dema Fri 11 May | Grad Scho n DC | £250 WDC1 to aid our understan ding. n Center, Washingto for the Mon 14 May | Foundatio £250 BAL1 widespread capabilities Wed 16 May go | £250 CHI1 s to fantastic tools and iques required Center Conference, Chica Yet, whilst we have acces knowledge and techn Fri 15 Jun | University analysis of data, the Toronto | £250 TOR1 storage, handling and Mon 18 Jun | Venue TBC, £235 BRS1 instinct ach based on intuition, Fri 29 Jun Edinburgh | £235 EDI1 e world, a design appro n Hotel, University of a cluttered, competitiv Fri 6 Jul | Salisury Gree AMS1 Amsterdam | £250 e data visualisati on comes in. Fri 13 Jul | Venue TBC, overload. This is wher al A 10% discount comm unications that appe Training page on and innovation, designing unleashing creativity ter to attend an event. brains process .com where you can also regis and exploiting the way our eyes and www.visualisingdata aimed at understanding recent times th in popularity over lisation and its grow sizes and The interest in data visua isations of all shapes, e story. As a result, organ ister now to reserve a Places are limited so reg has been a remarkabl tial value. ng up to the realisation of its poten domain are now waki workshop. place on your preferred training nt Training Course Conte Visit the www.visualisin, select the rehensive, tion. The objective of the training is to provi de delegates with a comp Training page and click on your preferred loca excitement events buzzing with ition. You will leave the have acquired, impact and amplify cogn ical capabilities you knowledge and pract s and opportunities about the foundation visualisation challenge Further Information on future data inspiring you to take include: environment ed in the courses will Class size a supportive learning The main topics cover size is 20 to facilitate xt of data visualisation The maximum class d and modern conte Historical backgroun an visual system een all attendees. of design and the hum group discussion betw Foundation principles selection design and The essentials of chart and resources Refreshments tial visualisation tools held in city central locat ions. Exploration of the essen process ded. All events will be n methodology and lunch will not be inclu The visualisation desig on design ing to visualisati Applying critical think itioners ice examples and pract Laptops Showcase of best pract case studies Visualisation project lisation challenges g the day’s activities. re your own data visua across the group durin Opportunities to explo have a some devices Times ? end of the Who Should Attend time allocated at the g from 9:00 and extra registration commencin er discussions. is interested in questions or hold furth responsibility for, or session to pick up any d for anyone who has data. The courses are suite and communicating s for visually exploring best practice approache . Visualising Data Ltd who lex datasets, or somebody st with large and comp t be an You might be an analy gement report. You migh the occasional mana Ltd, a UK based data visualisation just wants to enhance er of Visualising Data ber of this from the crowd. You might be a Andy Kirk is the found has been an active mem looking to stand out ing skills. training service. He to advertising and are ner without programm design consultancy and design training or a desig r. programmer with no g or the public secto cine, the media, engineerin popular blog www.visua You might be in medi we’ve all Data is everywhere and is no typical delegate. is most The point is that there . Anyone and everyone with it, so let’s do it right got to do something d! d to atten welcome and encourage
  7. Data Visualisation Speaker
  8. Data Visualisation Speaker
  9. Data Visualisation Author
  10. What are we covering?What you need to learnWhy you need to learn it How to learn it
  13. First, some eye candy
  14. OECD Better Life Index | Moritz Stefaner
  15. The Expansion of Post Offices Across the US | Derek Watkins
  16. Running the Numbers II: Portraits of global mass culture | Chris Jordan
  17. Yahoo! C.O.R.E Data Visualization | Periscopic
  18. Wind Map | Fernanda Viegas and Martin Wattenberg
  19. The popular emergence of data visualisation
  20. Popularity Google Insights: Keyword Infographic
  21. #1: Data Periscopic: Yahoo! Mail Data Visualization
  22. What’s missing?We are overwhelmed by data,not because there is too much, but because we dont know how to tame it. [Paraphrasing] Stephen Few,
  23. #2: TechnologyThe „eyeo‟ Festival (2011-2012)
  24. What’s missing?Doing data visualisation well is less a technology problem, more a people problem. Paraphrasing Aron Pilhofer, New York Times
  25. #3: ExposureHans Rosling: TEDTalks “Myths about the developing world“ (2006)
  26. What’s missing? The skills required for most effectively displaying informationare not intuitive and rely largely on principles that must be learned. Stephen Few, „Show Me the Numbers‟
  27. What’s missing?Heuristics vs. PrinciplesShould/could vs. Must
  28. What is data visualisation? The representation andpresentation of data that exploits our visual perception abilities in order to amplify cognition
  29. Cerebral Cortex Visual CortexThinking Seeing Human_Brain_sketch_with_eyes_and_cerebrellum.svg.png
  30. Ideas InspirationDiscoveries InsightComplexities Understanding Results PersuasionMessenger Encode Message Decode Receiver
  31. Skills and Knowledge
  32. Multi-disciplinary: Art & Science
  33. Cognitive Science: Gestalt Laws Images from
  34. Cognitive Science: Gestalt Laws
  35. Cognitive Science: Illusions
  36. Cognitive Science: Illusions
  37. Cognitive Science: DeceptionsVisible pixels on left graph: blue = 82% pink =18%Visible pixels on right graph: blue = 91% pink = 9% Office for National Statistics: Presentation by Alan Smith, “The Curious Incident of Kevins in Zurich…and other stories”
  38. Cognitive Science: Deceptions$0.8M out of $7.5M = 10.7%Length of presented bar progress = 24.6%
  39. Cognitive Science: Deceptions
  40. Cognitive Science: Colour theory | |
  41. Cognitive Science: Visual Variables Length Volume Size Area Texture Colour Label Direction Saturation Position SlopeHeight Angle Radius/Diameter Speed Curvature/Arc Shape Orientation Transparency Luminance Glyph Flow Motion Blur/Focus
  42. Cognitive Science: Visual Variables Original – J. D. MacKinlay, „Automating the design of graphical presentations of relational information‟, 1986 | Redesign - Joe Parry
  43. Design: Visualisation ContextExplanatory Analytical/Pragmatic Exploratory Abstract/Emotive
  44. Design: Chart Types
  45. Design: Typography
  46. Design: Instinct
  47. Design: Instinct Chose the chord diagram over the possibly morerevealing matrix design because the matrix doesnt look “tasty” and “muesli shouldnt look like fungi”
  48. Computers: Software/Programming
  49. Computers: HCI/UX
  50. Computers: Digital Cartography
  51. Data: Databases, Wrangling | |
  52. Data: Maths & Statistical Analysis |
  53. How to learn and where from?
  54. CraftPractice, practice, practice – experience is the keySeek potential projects – paid, curiosity, contestsLearn about yourself – take notes, self critiqueTechnical skills – push yourself out of comfort zoneEvaluate others – silently or provide reviewsPublish yourself – encourage and digest peer critique
  55. TheoryOnline content – immerse yourself in the communityBooks – so many invaluable references and inspirationsAcademia – papers, journalsConferences – within the field and around itTraining/education – look for good training provider…
  56. The 8 Hats of Data Visualisation ProjectInitiator Journalist Communicator ManagerCognitive Design Computer Data Science Science Science
  57. Cognitive Scientist = Mind Designer = Eye Journalist = NoseCommunicator = Mouth & Ears Computer Scientist = Hands Data Scientist = Back Project Manager = Torso Initiator = Legs
  58. @visualisingdata