Insights into the World of Statistics and Data Science

Musings on life intertwined with the intricate realms of statistical learning, data science, and their multi-faceted applications across various disciplines. Delve into the wisdom shared by prominent statisticians like John Tukey and Bill Cleveland, uncovering the essence of statistics in science and society.

• Statistics
• Data Science
• John Tukey
• Bill Cleveland
• Insights

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1. Some Musings on Life & Data Science Statistical Learning and Data Science Friday Center, UNC, Chapel Hill J. S. Marron Dept. of Statistics and Operations Research University of North Carolina

2. Some Views of Statistics Statistics z X 1 : s 2 n = = H 1 2 o EY X Most People

3. Some Views of Statistics Statistics z X 1 : s Bayes 2 n Bootstrap = = H 1 2 o Kernels EY X HDLSS Survival Analysis Sparsity Functional Data Machine Learning MCMC Time Series Mixed Models Etc. Etc. Etc. Reality

4. Some Views of Statistics Statistics Statistics in Science

5. Some Views of Statistics Medicine Physics Statistics Biology Geology Agriculture Economics Psychology Statistics in Science

6. Some Views of Statistics John Tukey Quote: From: http://www.morris.umn.edu/~sungurea/introstat/history/w98 Statistics in Science

7. Some Views of Statistics John Tukey Quote: The best thing about being a statistician is that you get to play in everyone's backyard From: http://www.york.ac.uk/depts/maths/histstat/tukey_nytimes.htm Statistics in Science

8. Some Views of Statistics Words coined by John Tukey: Bit (0 1 data unit) Software (mention to Computer Science friends )

9. Some Views of Statistics Another Prescient Statistician: Bill Cleveland Coined the Term Data Science Cleveland, W. S. (2001). Data science: an action plan for expanding the technical areas of the field of statistics. International Statistical Review.

10. Some Views of Statistics Statistics z X 1 : s 2 n = = H 1 2 o EY X Most People

11. Some Views of Statistics Data Science (Analytics) Computer Science Math (Applied) Bus. / Finance Statistics z X 1 : Others (Info. Sci., Psych, ) s 2 n = = H Caution: a desire to replace old ideas with exciting new ones 1 2 o EY X

12. Some Views of Statistics What is (should be) the relationship? Statistics Data Science Machine Learning (Cleveland View)

13. Some Views of Statistics What is (should be) the relationship? Data Science Machine Learning Statistics

14. The Big Question What are the Boundaries of Statistics? NSF/DMS Program Director (late 2004): That is not statistics

15. The Big Question What are the Boundaries of Statistics? OK, then where are they? We should discuss this much more Openly, not in the Rejection Process (Publications, Grants, etc.)

16. Variation Thoughts From Business Statistics Course

17. Variation A Fundamental Concept: Sounds Obvious Easy to Not Consider (Forget) {Surprisingly So}

18. Variation Easy to Not Consider (Forget) E.g. An Explorer Drowned in a Lake That Averaged 6 Inches in Depth o Hard to visualize? Thanks to N. I. Fisher

19. Variation Easy to Not Consider (Forget) E.g. An Explorer Drowned in a Lake That Averaged 6 Inches in Depth o Hard to visualize? Lake Eyre, Australia, from Wikipedia

20. Variation Easy to Not Consider (Forget) E.g. An Explorer Drowned in a Lake That Averaged 6 Inches in Depth o Hard to visualize? Lake Eyre, Australia, from www.airadventure.com.au

21. Variation Easy to Not Consider (Forget) E.g. An Explorer Drowned in a Lake That Averaged 6 Inches in Depth o Hard to visualize? o Key is Variation About Average oSimple Idea Takes a Minute to Recall (happens a lot)

22. Variation A Fundamental Concept: Sounds Obvious U.S. Presidential Politics ?!? Common Gross Oversimplification: Theyare going to Group of people: Political. Religious, Ethnic Origin, They all want to ..

23. Variation Homework C0.1 Find an Example of Ignoring Variation. Send me an email, with: text, and attribution. Plan to discuss in class.

24. Variation Homework C0.1 Results: Out of First 10 Quotes 9 Were From Donald Trump

25. Ideas on Human Relationships Common Question: How Are Dep t Politics Going? Background: Long Dubious History Merger of Statistics & OR (More Diverse Interests) Rapidly Changing University

26. Ideas on Human Relationships Response: Best I ve Seen in Chapel Hill Reason: Respect Key to Current Interactions Moved Beyond Politics of Disrespect

27. Ideas on Human Relationships Fundamental Observation: Human Interactions Work Best In An Atmosphere of Respect Day to Day Interactions w/ Colleagues Reviews of Papers / Grant Proposals US Congress US Presidential Politics

28. National University of Singapore Special Thanks UNC, Stat & OR Department of Statistics and Applied Prob. National University of Singapore For Many Discussions This Talk 28

29. BIG DATA Models & Concepts UNC, Stat & OR Challenge from the Recent Media: Mayer-Sch nberger and Cukier (2014) Big Data: A Revolution That Will Transform How We Live, Work, and Think 29

30. BIG DATA Models & Concepts UNC, Stat & OR Challenge from the Recent Media: Mayer-Sch nberger and Cukier (2014) Major Premise: Differing Data Analytic Goals Correlational vs. Causal 30

31. BIG DATA Models & Concepts UNC, Stat & OR Causal Data Analysis: Goal: Underlying Causes of Phenomena Approach: Classical Scientific Method Formulate Hypothesis Collect Data Test Hypothesis Consequences: Solid Knowledge w/ Measurable Certainty 31

32. BIG DATA Models & Concepts UNC, Stat & OR Correlational Data Analysis: Goal: Find (and Use) Mere Correlations Motivation: Correlations are Useful (e.g. ___ Recognition Software) Valuable (Buying and Selling of Data ) Insightful???? Consequences: Automatic Solutions to Some Hard Problems 32

33. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? 33

34. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Na ve Readers [Of Mayer-Sch nberger and Cukier (2014)]: This is Exciting!!! Great New Ideas!!! Change Statistics Curricula!!! Start Up Data Analytics !!! 34

35. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Statistics Time 35

36. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Statistics Pattern Recognition Artificial Intelligence Neural Networks Data Mining Machine Learning Time 36

37. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Statistics Pattern Recognition Artificial Intelligence Neural Networks Data Mining Machine Learning ??? Time 37

38. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Statistics Pattern Recognition Artificial Intelligence Neural Networks Data Mining Machine Learning Big Data Data Science Time 38

39. A Small Aside A Personal Apology to Xiaotong Shen For My Skepticism About ASA Section on Data Mining My (Wrong) Idea: Name Would Change, So Not Appropriate as Section {Great to See Recent Name Change}

40. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Statistics Pattern Recognition Artificial Intelligence Neural Networks Data Mining Machine Learning Big Data Data Science Time 40

41. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Some Came With Major New Ideas Pattern Recognition Artificial Intelligence Neural Networks Data Mining Machine Learning Big Data 41

42. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Pattern Recognition Artificial Intelligence Neural Networks Data Mining Machine Learning Big Data Less So For Others, But More Focus On 42

43. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Data Mining Great Correlational Discovery 43

44. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Data Mining Great Correlational Discovery: Super Market Scanner Data Baby Diapers (aka Nappies) & Beer 44

45. Correlation vs. Causation UNC, Stat & OR How New Is This Discussion? Data Mining Baby Diapers (aka Nappies) & Beer Some Perspective: Correlational Discovery Makes Causational Sense (Too Soon To Totally Dump Causation) 45

46. Correlation vs. Causation UNC, Stat & OR Relative Emphasis??? 46

47. Correlation vs. Causation UNC, Stat & OR Relative Emphasis??? Classical Statistics: Correlation vs. Causation 47

48. Correlation vs. Causation UNC, Stat & OR Relative Emphasis??? Mayer-Sch nberger and Cukier: Correlation vs. Causation 48

49. Correlation vs. Causation UNC, Stat & OR Relative Emphasis??? Suggested Actual Future Course: Correlation & Causation 49

50. Correlation vs. Causation UNC, Stat & OR Relative Emphasis??? Suggested Actual Future Course: Correlation & Causation Note: Changes Are Needed in Curricula, Etc. 50

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