September 29, 2015
870

Data Science 101

Presentation at the Data Science 101 workshop at Orangescape.

September 29, 2015

Transcript

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

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

September 15

September 15
4. The purpose of computing is insight, not numbers. Richard Hamming

Wednesday, 30 September 15

September 15
6. What is the purpose of data science ? Insight, not

numbers! Wednesday, 30 September 15

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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

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

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

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

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