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V is for Visualization: Practical Considerations for Visualizing "Big Data"

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Presented as part of Big Data Week 2014 in Toronto at Viafoura HQ: http://viafoura.com/blog/big-data-week-2014/

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V is for Visualization: Practical Considerations for Visualizing "Big Data"

  1. 1. V is for Visualization Practical Considerations for Visualizing “Big Data” Big Data Week 2014 05-07-14 Myles Harrison @everydayanalyst www.everydayanalytics.ca myles@mylesharrison.com
  2. 2. 01100010 01101001 01100111 00100000 01100100 01100001 01110100 01100001
  3. 3. DATA
  4. 4. The 3 “V”s of Big Data: Volume Velocity Variety Visualization! 4
  5. 5. i.
  6. 6. DIMENSIONS MEASURES
  7. 7. attentive processing
  8. 8. pre-attentive processing
  9. 9. 1172 / 293 = ? 1172 293
  10. 10. Adapted from Show Me The Numbers, 2nd ed. by Stephen Few. Analytics Press, 2012
  11. 11. orientation length closure size (area) curvature density estimation colour (hue) intensity intersection termination depth
  12. 12. f(x) = data-ink total ink used
  13. 13. ii. (how filling)
  14. 14. STOP!
  15. 15. quantity of interest density
  16. 16. iii. (quickly now)
  17. 17. iv. (the spice of life)
  18. 18. METADATA
  19. 19. ABC 1010 1010 1010 1011 0101 0110
  20. 20. information retrieval semantic analysis shingling named-entity recognition Natural Language Processing (NLP) bag o’ words tf-idf sentiment analysis stemming N-grams
  21. 21. 1.0 0.0 -1.0
  22. 22. Problems of High Dimensionality • Dimensionality reduction techniques: – Multi-dimensional scaling (MDS) – Principle Components Analysis (PCA) – Linear Discriminant Analysis (LDA) • Variable selection (forward & backward)
  23. 23. Credit: Kirk, Andy. In Praise of Slopegraphs.
  24. 24. v. (no jumping allowed)
  25. 25. Summary • Keep in mind the basics - understand data & perception in visualization • Use visualization for what it is good at, and analysis techniques to handle the ‘Big’ part • Complexity can be handled with analytical techniques and less-common visualization types (where appropriate)
  26. 26. The Spectrum of Data Visualization Art Science ? DESIGN Data Art Infographics Dashboards ANALYSIS Information Design Graphs Tables
  27. 27. gracias

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