Celebra(ng 50 years of the Victorian Branch of the Sta(s(cal Society of Australia. Terry Speed, Walter & Eliza Hall Ins7tute of Medical Research

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1 Celebra(ng 50 years of the Victorian Branch of the Sta(s(cal Society of Australia Terry Speed, Walter & Eliza Hall Ins7tute of Medical Research 1

2 The beginning With many thanks to Prof Ian Gordon, and the Archives of the University of Melbourne Why? Unaided, I remember nothing. 2

3 Preliminary mee7ng J.R. Bainbridge A. Doig G. Gregory J.S. Maritz P.R.N. SuOon A.M.W. Verhagen E.J. Williams (M.H. Belz) 3

4 No(ce of the mee(ng 4

5 From the No(ce of Mee(ng The aims of the Society can be best fulfilled if the interests of the members cover as wide a range as possible. This is a necessary condi7on since sta7s7cs is used today in a diversity of subjects: actuarial science, agriculture, biology, business and industrial sta7s7cs, chemistry, computa7on and numerical analysis, demography, economics, engineering, gene7cs, market research, psychology, sample surveys and sociology, to name only the obvious ones. Likewise, the members should be drawn from a variety of occupa7ons: government services, industry, universi7es, research organiza7ons, and any other group to whom the use of sta7s7cs is relevant. 5

6 The people who ahended covered this wide range. A comparison with the Society today could be revealing, and a possible connexion with my remarks later is hard to avoid. 6

7 Government services, Federal: Commonwealth Bureau of Census and Sta7s7cs, Civil Avia7on, Postmaster General s, Supply, Labour and Na7onal Service, Commonwealth Serum Laboratories, TAA. State: Public Service Board, Sta7s7cal Office, Electricity Commission, Rivers & Walter Supply, Forests Commission, Country Roads Industry: Yorkshire Insurance, Colonial Mutual Life Assurance, Arthur Anderson, AM Parker & Co, ANZ Bank, G & R Block Architects, Mobil Oil, ICIANZ, Moulded Products, Felt & Tex7les of Australia, Volkswagen Clayton Universi(es: Melb Sta7s7cs, Mathema7cs, Physiology, Economics, Chemistry, Educa7on, Forestry, Dental School, Agriculture. Monash: Mathema7cs, Economics. Other: Australian Administra7ve Staff College Research organiza(ons: CSIRO Divisions Poultry Research, Mathema7cal Sta7s7cs, Chemical Physics 7

8 AHendance sheet (in part: 77 present) 8

9 THE FUTURE OF STATISTICAL PRACTICE Prof Evan J Williams, 2 April 1964 Summary: The standing of a profession in the community depends on the quality of its prac7ce. The prac7ce of sta7s7cs can best advance through its prac77oners being thoroughly trained. Their training should include (1) thorough grounding in mathema7cs and sta7s7cal theory (2) postgraduate experience working on prac7cal problems with a team of experienced prac77oners, and (3) inculca7on of research- mindedness being able to iden7fy and solve new problems, developing the solu7ons to the point of prac7cal applica7on. 9

10 Up and running! 10

11 A talk I m prehy sure I heard that year at the new SSA Vic, but perhaps it was at the IBS, Aust Region Language and Speech : 196 Here ends my brief reminiscences of SSA Vic c

12 Big data: where are the sta(s(cians? Will we be celebra(ng 100 years of the Victorian Branch of the Sta(s(cal Society of Australia in 2064? 12

13 Big data is like teenage sex: everyone talks about it, nobody really knows how to do it, everyone thinks everyone else is doing it, so everyone claims they are doing it... Dan Ariely, 2013 In God we trust; all others must bring data. W.E. Deming 13

14 Big Data 1. It s over- hyped 2. It s real 3. We could be there 4. We re not 5. Why? 6. It may too late 7. Do we care? 8. If yes, what are we going to do? We = members of SSAI Vic Branch 14

15 1. It s over- hyped 15

16 Interest in Big Data has growing since ~ Google Trends: Big Data 16

17 And will probably con(nue for a while, at least Google Trends: Bioinforma7cs Google Trends : Sta7s7cs 17

18 The Gartner Hype Cycle 18

19 Some specifics 19

20 The promise big data refers to things one can do at a large scale, that cannot be done at a smaller one, to extract new insights, or create new forms and value, in ways that change markets, organisadons, the reladonship between governments, cidzens and more. p.6 Big Data: A Revolu7on that Will Transform how We Live, Work, and Think by Viktor Mayer- Schönberger and Kenneth Cukier

21 Some of the book s claims Chapter 2: ALL Reaching for a random sample in the age of big data is like clutching at a horsewhip in the era of the motor car. n=all Chapter 3: MESSY But invariably simple models and a lot of data trump more elaborate models based on less data. Chapter 4: CORRELATION Knowing what, not why is good enough. In my view, significant parts of this book are off- base; other parts ring true. It is probably one of the beoer books on this topic. 21

22 2. It s real Ini7ally I wondered whether this sec7on was necessary. Aser all, we are all sta7s7cians, and so we all appreciate the value of data. 22

23 Managing the environmental impact on rivers by streaming informa7on!"#$"%&$'()($ *+,,-..$*)/0%-. Applies emerging technologies to deliver instantaneous people searches Analyzing huge volumes of customer comments in real 7me delivers a compe77ve edge Analyzes real- 7me data streams to iden7fy traffic paoerns Putng real- 7me data to work and providing a plauorm for technology development Helping companies deliver the web experience their customers want. Streaming data technology supports covert intelligence and surveillance sensor systems Leveraging key data to provide proac7ve pa7ent care Streaming real- 7me data supports large scale study of space weather Turning climate into capital with big data 23

24 !"#$%"$&'(%)*+,-.'' /%)'0#1'2/,/!"#$%&'()$*&+,#-'$(./$0!$1#2,.(3!"#$%&'()(*+,-.(/0123(4&5(6(7+5( 24

25 Program Session 1: Interna7onal and interdisciplinary dimension challenges for Science and Industry Social networks and Big Data Big Data in Bioinforma7cs Big Data at CERN Risk and finance with Big Data Big Data and Astronomy Session 2: Science- Industry collabora7on Cyber security and data analy7cs Big Data in Engineering Big Data and IBM Research Big Data in Pharma 25

26 26

27 Panels on Big Data Integra7ng Big Genomics Popula7on Scale Big Data Big Data in Health Care Imaging Computers and Architecture Infec7ous Disease Genomic Medicine Devices and Technology Keynotes from NIH FDA Google Khosla Ventures EMBL Sta7s7cs and Machine Learning 27

28 New sources of data Data from sensors, posts to social networking sites, digital images, videos posted online, transac7on records of online purchases, data from mobile phone GPS signals, bus records, traffic, BIG players: Google, Microsos, Amazon, Verizon, Walmart,,, NSA, GCHQ, 28

29 3. We could be there 29

30 Not all Big Data is big 30

31 Some (tles from that Big Data conference Big insights into pa7ent flow Iden7fying and predic7ng community care and health needs Enhancing diagnos7cs for invasive Aspergillosis using machine learning Big data from small sites: Crea7ng a primary care data warehouse Spa7o- temporal visualisa7on of disease incidence and respec7ve interven7on strategies 31

32 Some Big Data is just novel Soil sensors can help dairy farmers op7mise fer7liser use. UTAS 32

33 The US Na(onal Ins(tutes of Health are inves(ng heavily in Big Data 33

34 What is Big Data for the US NIH? The term 'Big Data' is meant to capture the opportuni7es and challenges facing all biomedical researchers in accessing, managing, analyzing, and integra7ng datasets of diverse data types [e.g., imaging, phenotypic, molecular (including various ' omics'), exposure, health, behavioral, and the many other types of biological and biomedical and behavioral data] that are increasingly larger, more diverse, and more complex, and that exceed the abili7es of currently used approaches to manage and analyze effec7vely. 34

35 4. We re not [there] (most of the (me) 35

36 The absence of sta(s(cians in Big Data ac(vi(es is striking (to a sta(s(cian) The US NSF convened a working group on Big Data in 2012 of 100 experts: guess how many sta7s7cians? The US NIH BD2K Execu7ve CommiOee of 17 has how many sta7s7cians? Conferences and commioees are popping up around the world with no (or very few) sta7s7cians involved $Ms (and other currencies) are being thrown at Big Data experts (no, this is not envy, it s puzzlement) Marie Davidian s editorial in the July 2013 issue of Amstat News. 36

37 !"#$%"$&'(%)*+,-.'' /%)'0#1'2/,/!"#$%&'()$*&+,#-'$(./$0!$1#2,.(3!"#$%&'()(*+,-.(/0123(4&5(6(7+5( 37

38 Program Session 1: Interna7onal and interdisciplinary dimension challenges for Science and Industry Social networks and Big Data Big Data in Bioinforma7cs Big Data at CERN Risk and finance with Big Data Big Data and Astronomy 9 speakers 0 sta7s7cians Session 2: Science- Industry collabora7on Cyber security and data analy7cs Big Data in Engineering Big Data and IBM Research Big Data in Pharma 38

39 An unconscious omission? California Magazine, Winter 2013 (for alumni of UC Berkeley) 39

40 40

41 41

42 42

43 43

44 An explicit rejec(on Mike Flowers, a lawyer, and NYC s first Director of Analy7cs "I had no interest in very experienced sta7s7cians," he says. "I was a liole concerned that they would be reluctant to take this novel approach to problem solving." Earlier, when he had interviewed tradi7onal stats guys for the financial fraud project, they had tended to raise arcane concerns about mathema7cal methods. "I wasn't even thinking about what model I was going to use. I wanted ac7onable insight, and that was all I cared about," he says. Chapter 10, Big Data: A Revolu7on That Will Transform How We Live, Work and Think. 44

45 45

46 Big Data Panels on Integra7ng Big Genomics Popula7on Scale Big Data Big Data in Health Care Imaging Computers and Architecture Infec7ous Disease Genomic Medicine Devices and Technology Sta7s7cs and Machine Learning Keynotes from NIH FDA Google Khosla Ventures EMBL 57 speakers 7 sta7s7cians Guess where? 46

47 What is Big Data for the US NIH? The term 'Big Data' is meant to capture the opportuni7es and challenges facing all biomedical researchers in accessing, managing, analyzing, and integra7ng datasets of diverse data types [e.g., imaging, phenotypic, molecular (including various ' omics'), exposure, health, behavioral, and the many other types of biological and biomedical and behavioral data] that are increasingly larger, more diverse, and more complex, and that exceed the abili7es of currently used approaches to manage and analyze This effec7vely. is exactly what I have been trying to do for the last 15 years 47

48 hhp:// 21- thought- leader- professors- in- data- science/ 1 sta7s7cian 48

49 A new ini(a(ve in Australia Centre of Excellence for FronDers in MathemaDcs and StaDsDcs: Big Data, Big Models, New Insights Centre Director: Professor Peter Hall From the 350 character descrip7on This Centre will create innova7ve mathema7cal and sta7s7cal models for analysing big data sets. The insight gained from analysing these models 49

50 Summary Some of us are there without calling what we do Big Data, others are calling it that, but most. 50

51 5. Why? 51

52 Some possible part- explana(ons Many problems in Big Data are poorly defined, Perhaps we prefer to be precisely wrong than approximately right (Tukey) Perhaps we are misunderstood* Or, worse, we are understood We lack the IT (and other, e.g. marke7ng) skills* We are not connected to data science* Many Big Data projects involve large teams Perhaps we are reluctant to get highly specific, see next slide. 52

53 From: Applied Statistics by DR Cox & EJ Snell! " 1.1 Introduction! Statistical analysis deals with those aspects of the analysis of data that are not highly specific to particular fields of study. That is, the object is to provide concepts and methods that will, with suitable modification, be applicable in many different fields of application; indeed one of the attractions of the subject is precisely this breadth of potential application." 53"

54 Other possibili(es I some7me feel that the Lies, damned lies and stadsdcs jibe has really taken root, and damned us in the eyes of many poten7al supporters. We ve all heard He uses stadsdcs as a drunken man uses lamp- posts...for support rather than illuminadon. But do others know how we illuminate, or just how we support? At other 7mes, I think we ve shot ourselves in the foot with our cau7on, and our concern for theory. 54

55 Combined view of a Past, Current and Future President of the American Sta(s(cal Associa(on, July 2013 There are three reasons for this disconnect. First, the media and public lack a general understanding of what sta7s7cians contribute to society (the issue that mo7vated the Interna7onal Year of Sta7s7cs). Second, few sta7s7cians are engaged in Big Data projects or have the special skills necessary to handle Big Data challenges. Third, the sta7s7cal community is disconnected from the new (and vaguely defined) community of data scien7sts, who are completely iden7fied with Big Data in the eyes of the media and policymakers. 55

56 6. It may be too late 56

57 Too late? All I can say here is that I feel that s a real possibility. This just means too late for my genera7on, and perhaps the next, but it s never too late for yours! 57

58 7. Do we care? Perhaps we ve had our chance, and we didn t take it? 58

59 Big Data isn t new to sta(s(cians

60 Big Data isn t new to sta(s(cians Part I Treatment of "Huge'' Data Sets

61 Big Data isn t new to sta(s(cians Part I Treatment of "Huge'' Data Sets

62 But perhaps the name is new Part I Treatment of "Huge'' Data Sets KDD

63 Buja A, Keller- McNulty S. Introduc7on to the special sec7on on massive datasets. J Comp Graph StaDst 1999 The area of massive datasets, though currently of great interest to Computa7onal sta7s7cians and to many data analysts, has not yet become part of mainstream sta7s7cal science. However, we see it as an area of ferment that is likely to generate problems of interest to the en7re field and to contribute to the changes in the teaching of sta7s7cs. 63

64 It didn t happen Huber (1994) 33 cites Huber (1998) 21 cites Massive Data sets Proceedings of a Conference (1998) 0 cites Buja & Keller- McNulty (1999) 3 cites Lambert (2003) What use is sta7s7cs for massive data? 8 cites KeOenring (2009) Massive datasets 12 cites On the other hand, 64

65 Who are the data scien(sts? Sta(s(cians are, and Many others as well, from machine learning, computer science, signal processing, physics, applied mathema7cs, domain sciences and social sciences, ci7zen science, We can and do share tools, techniques & tradi7ons for analysing data, some7mes differently named. 65

66 McKinsey Report (2011) Big data: The next fron1er for innova1on, compe11on, and produc1vity. 6. There will be a shortage of talent necessary for organiza7ons to take advantage of big data. By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analy7cal skills as well as 1.5 million managers and analysts with the know- how to use the analysis of big data to make effec7ve decisions.. What will they learn? Who will teach them? Those who ignore sta7s7cs are condemned to re- invent it. Brad Efron 66

67 Summary This is a Golden age of sta7s7cs but not necessarily for sta7s7cians Gerry Hahn 67

68 8. If yes, what are we going to do? 68

69 Some skills needed for Big Data and Data Science Interpersonal, leadership and communica7on, for access to important problems Computa7onal, the gateway to accessing and analysing data Theore7cal, for analyses under idealized models, to gain insight A solid understanding of the subject maoer Cri7cal thinking and common sense, for making sense of data (a sta7s7cians advantage?) 69

70 Would we all be willing to learn and promote those skills? support changing the name of what we do from Sta7s7cs to Data Science (and hence change the names of our books, departments, socie7es..) Put differently, would we all be willing to do something in order to be able to play a much larger role in the revolu7on going on around us, not currently in our name? If so, what? 70

71 Q: Big Data: where are the sta(s(cians? A: They are mainly the young ones, but us older ones could help. 71

72 References/Acknowledgements M. Davidian (2013) Aren t we Data Science? Amstat News, July J.M. Jordan & D.K.J. Lin (2014) Sta7s7cs for Big Data: Are Sta7s7cians ready for Big Data? ICSA BulleDn, January B. Yu (2007) Embracing Sta7s7cal Challenges in the Informa7on Technology Age. Technometrics v.49. B. Yu (2012) President s Welcome. Bull IMS B. Yu (2013) The reladve size of big data: experiences from an interdisciplinary stadsdcian. Tutorial talk at JSM Many thanks to Bin Yu, UC Berkeley Sta7s7cs & EECS 72

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