Big data camp la futures so bright tim-shea
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Big Data Camp LA 2014, • The Future's so bright (You can barely make any predictions about it) by Timothy Shea of DataSift
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Big data camp la futures so bright tim-shea
- 1. Powering The Social Economy
- 3. How do we Make Good Forecasts?
- 4. The Architecture vs The Practice(aka: Form vs Function) Platforms for Big Data storage, processing & analytics. VS Actual applications of Data-at-Scale
- 5. Themes for This Morning How DataSift Manages, Processes & Delivers Data Visualization via Tableau Causal Inference & Statistical Modeling Movies & Coffee
- 6. Who am I? Tim Shea @SheaNineSeven Data Scientist & Sales Engineer at DataSift
- 7. Focus on Alliances & Channels: Tableau, Alteryx, Microstrategy, Informatica, SAP Data Science as a Practice: Disambiguation, Classification, Causality
- 8. What is DataSift? Social Data Platform Full “Firehose” Access 2 Billion Posts per Day ½ Trillion Posts Historical Archive
- 9. Really Intense Architecture Diagram
- 10. We Make it Simple for You Focus on Filtering Big Data < Relevant Data Enrichments: - Demographics - Links - Emotion & Intent - Learned Classification
- 11. Demo
- 12. DataSift: Beyond “Social Listening” Ex. “Does Social have anything to do with my Business?” Line Charts and Graphs Vs Operationalized Decision Making
- 14. “The Enterprise” DataSift Enterprise customers are building: 1. Demand Forecasting 2. Critical Event Processing 3. Market Segmentation/Statistical Classification 4. Establishing Correlative Relationships(**)
- 15. Causality
- 16. Necessary…Connection? Does Event A cause Event B?
- 18. Does The Past have anything at all to do with The Future?
- 19. Defending Your Hypotheses How can I create & defend my Hypotheses? How do I communicate my findings to Laypeople (non-Data Scientists) like your Boss?
- 20. Risk Management in Hollywood
- 21. Movies Through the Lens of: DataSift - What we do as a Social Data Platform Tableau - How to Make Sense of a Mountain of Data Good Data & Good Tools
- 25. Risk Management is Hard Q: What is a “Sure Bet”? Q: Should I spend $100MM making this movie? Q: How can I make this process less risky?
- 26. Enter DataSift & Tableau
- 27. Example Return Every: Tweet Facebook Post Instagram Photo Bitly Click For What? Every single Movie released in 2013
- 28. Compare it With
- 29. Tableau
- 30. What Data do we Have?
- 31. 1. Intuition
- 33. 2. Social => Box Correlation?
- 35. 3. Prove It
- 38. 4. Defend the Model
- 39. The Model Y = a + bX Y = Box Office (the predicted) X = Social Volume (the predictor) B = Coefficient A = Some offset
- 40. Defend the Model v1 P-value: There is an X% chance that the Null Hypothesis is true. Null Hypothesis: The linear coefficient is equal to zero.
- 41. Defend the Model v2 P-value (again): We can be (100 – X)% confident that the correlation were seeing can be explained by our model. R-Squared: Our model explains about Y% of the variability (points outside the regression line) given “Sum of Least Squared”
- 42. Defend the Model v3 Every Bitly click predicts about $240 in Box Office Sales I’m extremely confident (99%) that this is not due to chance. With ~96% confidence we can rely on this model in the future.
- 43. The Model (cont) Y “is predicted by” a + bX Box Office = 0 + $240 * (# bitly clicks) Box Office = 0 + $130 * (# tweets)
- 44. Benchmarking If my Bitly #’s drop below $240 If my Twitter #’s drop below $130 If my Instagram #’s drop below $2809 If my Facebook #’s drop below $3871
- 46. Other Considerations Residuals Other Regression (Logarithmic, Exponential, Polynomial) “Overfitting”
- 47. Additional Dimensions DataSift Social Data: Gender Income Geography “Influence” Industry vs Consumers