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Data Visualization

Data Visualization. Daniel Silver March, 2014 [number of slides courtesy of Stan Matwin ]. The KDD Process. Interpretation and Evaluation. Data Mining. Knowledge. Selection and Preprocessing. p(x)=0.02. Data Consolidation. Patterns & Models. Prepared Data . Data Warehouse.

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Data Visualization

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  1. Data Visualization Daniel Silver March, 2014 [number of slides courtesy of Stan Matwin]

  2. The KDD Process Interpretation and Evaluation Data Mining Knowledge Selection and Preprocessing p(x)=0.02 Data Consolidation Patterns & Models Prepared Data Data Warehouse Consolidated Data Data Sources

  3. The KDD Process The Architecture of a KDD System Graphical User Interface Data Mining Interpretation, Visualization and Evaluation Selection and Preprocessing Data Consolidation Knowledge Warehouse Data Sources

  4. Data Visualization • The greatest value of a picture is when it forces us to notice what we never expected to see – J. Tukey • Visual analytics can lead to discoveries that neither a computer nor a human could make alone – J. Fekete • Let your data talk to you – J. Stasko Big Data, Saint John, NB

  5. What is visualization? • Graphical representation of data and relationships within data • Why? • right (imagery) and left (analytical) brain hemisphere • What makes a good visualization? • Informative • Aesthetically pleasing • Often, the right abstraction of the data Big Data, Saint John, NB

  6. Windmap • http://hint.fm/wind/ [Martin Wattenberg] • Captures the “Big Picture” from the data • Data from the National Digital Forecast Database • Exhibited in MOMA as graphical art Big Data, Saint John, NB

  7. Visualization as a tool for… • Data exploration • Communication of results Big Data, Saint John, NB

  8. How best to visualize? Art is good in conveying complex concepts Big Data, Saint John, NB

  9. Advertising uses great visualizations… Big Data, Saint John, NB

  10. But is it that easy … • … art conveys emotions, mental states… • Visualizing data is different, but still… • A good visualization should require no explanation • Allows us to see the data “in a new light” Big Data, Saint John, NB

  11. Geographic misconceptions… • The True Size of Africa [Kai Kruse] • http://flowingdata.com/2010/10/18/true-size-of-africa/ Big Data, Saint John, NB

  12. Conveying Complex Dynamics • Projecting on the time axis • Using colour, thickness, etc. to combine and overlay data Big Data, Saint John, NB

  13. [Minard 1896!] Big Data, Saint John, NB

  14. Email “mountain” [F. Viergas] -making sense of an email archive Big Data, Saint John, NB

  15. Visual exploration process [Yau, Data Points, 2013] Big Data, Saint John, NB

  16. Exploring data • ACOA database of 28,000 projects • Contains • Client’s address, postal code • Amount of funding • Project description • Program type • …. Big Data, Saint John, NB

  17. ACOA data Big Data, Saint John, NB

  18. Geo mapping • We map the Postal code of each ACOA project to geographic coordinates using the PCCF Canada Post database Big Data, Saint John, NB

  19. ACOA Data [Tableau] Big Data, Saint John, NB

  20. ACOA Program Types • Business Development Program • Productivity and Business Skills (PBS) • ACOA Action Program (AAP) • Other Big Data, Saint John, NB

  21. ACOA data – geolocated by project type Big Data, Saint John, NB

  22. Visualization and text Wordcloud with R Big Data, Saint John, NB

  23. Beyond wordcloud • How to characterize at a high level what the project is all about? • Find, by analyzing all project descriptions, high level “clusters” (“topics”) of descriptions can be identified Big Data, Saint John, NB

  24. 5 LDA topics for ACOA projects Big Data, Saint John, NB

  25. So where is the future? - challenges • Visualization of stream/dynamic data (animation?) • Visualization of very large graphs • Visualization of multi-dimensional data (see OECD life quality demo) • Visualization for mobile devices (limited screen sizes, limited power) • Evaluation of visualization Big Data, Saint John, NB

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