Data Science 101

0f80748bac600948e3d3a233a85ac16c?s=47 Ronojoy Adhikari
September 29, 2015
Research
2
870

Data Science 101

Presentation at the Data Science 101 workshop at Orangescape.

0f80748bac600948e3d3a233a85ac16c?s=128

Ronojoy Adhikari

September 29, 2015
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Transcript

  1. Data Science 101: insight, not numbers Ronojoy Adhikari The Institute

    of Mathematical Sciences Chennai, India Orangescape Chennai, India Wednesday, 30 September 15

  2. The purpose of computing is insight, not numbers. Wednesday, 30

    September 15

  3. The purpose of computing is insight, not numbers. Wednesday, 30

    September 15

  4. The purpose of computing is insight, not numbers. Richard Hamming

    Wednesday, 30 September 15

  5. What is the purpose of data science ? Wednesday, 30

    September 15

  6. What is the purpose of data science ? Insight, not

    numbers! Wednesday, 30 September 15

  7. Data science Wednesday, 30 September 15

  8. Wednesday, 30 September 15

  9. Data Wednesday, 30 September 15

  10. Data Domain knowledge Wednesday, 30 September 15

  11. Data Domain knowledge Data curation Wednesday, 30 September 15

  12. Data Domain knowledge Data curation Mathematical model Wednesday, 30 September

    15

  13. Data Domain knowledge Data curation Mathematical model A/B testing Wednesday,

    30 September 15

  14. Data Domain knowledge Data curation Mathematical model A/B testing Machine

    learning Wednesday, 30 September 15

  15. Data Domain knowledge Data curation Mathematical model A/B testing Machine

    learning Machine inference Wednesday, 30 September 15

  16. Data Domain knowledge Data curation Mathematical model A/B testing Machine

    learning Machine inference Value from data Wednesday, 30 September 15

  17. 1. Problem or question ? Wednesday, 30 September 15

  18. Wednesday, 30 September 15

  19. Let the data speak for themselves! Ronald Fisher Wednesday, 30

    September 15

  20. Let the data speak for themselves! Ronald Fisher The data

    cannot speak for themselves; and they never have, in any real problem of inference. Edwin Jaynes Wednesday, 30 September 15

  21. Classification Regression Clustering Dimensionality reduction Wednesday, 30 September 15

  22. Classification Regression Clustering Dimensionality reduction predict class, given attributes Wednesday,

    30 September 15

  23. Classification Regression Clustering Dimensionality reduction predict class, given attributes Wednesday,

    30 September 15

  24. Classification Regression Clustering Dimensionality reduction predict class, given attributes predict

    values, given other values Wednesday, 30 September 15

  25. Classification Regression Clustering Dimensionality reduction predict class, given attributes predict

    values, given other values Wednesday, 30 September 15

  26. Classification Regression Clustering Dimensionality reduction predict class, given attributes predict

    values, given other values group similar things together Wednesday, 30 September 15

  27. Classification Regression Clustering Dimensionality reduction predict class, given attributes predict

    values, given other values group similar things together Wednesday, 30 September 15

  28. Classification Regression Clustering Dimensionality reduction predict class, given attributes predict

    values, given other values group similar things together keeping only the relevant variables Wednesday, 30 September 15

  29. Classification Regression Clustering Dimensionality reduction predict class, given attributes predict

    values, given other values group similar things together keeping only the relevant variables Wednesday, 30 September 15

  30. 3. Frame a hypothesis (mathematical models) Wednesday, 30 September 15

  31. Bayesian Blackbox Frequentist Causal Wednesday, 30 September 15

  32. Bayesian Blackbox Frequentist Causal probability is a state of knowledge

    Wednesday, 30 September 15

  33. Bayesian Blackbox Frequentist Causal probability is a state of knowledge

    probability is a frequency Wednesday, 30 September 15

  34. Bayesian Blackbox Frequentist Causal probability is a state of knowledge

    probability is a frequency Wednesday, 30 September 15

  35. Bayesian Blackbox Frequentist Causal probability is a state of knowledge

    ML : toolbox for processing data probability is a frequency Wednesday, 30 September 15

  36. Bayesian Blackbox Frequentist Causal probability is a state of knowledge

    ML : toolbox for processing data probability is a frequency Wednesday, 30 September 15

  37. Bayesian Blackbox Frequentist Causal probability is a state of knowledge

    ML : toolbox for processing data ML : learning generative models of data probability is a frequency Wednesday, 30 September 15

  38. Bayesian Blackbox Frequentist Causal probability is a state of knowledge

    ML : toolbox for processing data ML : learning generative models of data probability is a frequency Wednesday, 30 September 15

  39. Wednesday, 30 September 15

  40. Wednesday, 30 September 15

  41. Wednesday, 30 September 15

  42. We are building a causal learning and inference engine that

    will beat the current state-of-art! Wednesday, 30 September 15

  43. We are building a causal learning and inference engine that

    will beat the current state-of-art! Thank you for your attention! Wednesday, 30 September 15