Some Musings on Life Data Science Statistical Learning

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

Some Views of Statistics Most People

Some Views of Statistics Bootstrap Statistic s Bayes Kernels HDLSS Survival Analysis Sparsity Functional Data Machine Learning MCMC Mixed Models Time Series Etc. … Reality

Some Views of Statistics in Science

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

Some Views of Statistics John Tukey Quote: From: http: //www. morris. umn. edu/~sungurea/introstat/history/w 98 Statistics in Science

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

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

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.

Some Views of Statistics Most People

Some Views of Statistics “Data Science (Analytics)” Ø Computer Science Ø Math (Applied) Ø Bus. / Finance Ø Others (Info. Sci. , Psych, …) Statistics

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

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

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

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. )”

Variation Thoughts From Business Statistics Course

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

Variation Ø 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

Variation Ø 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

Variation Ø 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

Variation Ø 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” o Simple Idea Takes a Minute to Recall (happens a lot)

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

Variation Homework C 0. 1 Find an Example of Ignoring Variation. Send me an email, with: text, and attribution. Plan to discuss in class.

Variation Homework C 0. 1 Results: Out of First 10 Quotes 9 Were From Donald Trump

Ideas on Human Relationships Common Question: “How Are Dep’t Politics Going? ” Background: v Long Dubious History v Merger of Statistics & OR (More Diverse Interests) v Rapidly Changing University

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

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

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

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

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

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

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

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

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

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

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

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

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

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}

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

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

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

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

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

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

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

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

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

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

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

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

The Big Question What are the Boundaries of Statistics? We Should Openly Discuss Much More… Statistics Data Science Machine Learning … Data Science OR Machine Learning … Statistics

The Big Question What are the Boundaries of Statistics? We Should Openly Discuss Much More… How Much Leadership Should We Take? Let’s Embrace Our Wide Diversity of Opinions on This Point

Challenges for You ü Lead Statistics (D. S. ) into the Future ü Promote Increasing Breadth ü Embrace New Ideas ü Advocate Them While Reviewing ü Speak Up Serving On Panels ü Openly Discuss Boundaries