DSC/e. Data Science Center Eindhoven Turning Data into Value. Wil van der Aalst. Scientific Director DSC/e

Transcription

1 DSC/e Data Science Center Eindhoven Turning Data into Value Wil van der Aalst Scientific Director DSC/e

2 DSC/e in a Nutshell The Data Scientist: Engineering Profession of the Future (growth of data will not stop) DSC/e will: educate data scientists at all levels (BSc, MSc, PhD), address research challenges related to data science, work together closely with industry, increase TU/e's visibility as a leader in data science. Ambition: Become a globally leading data science center focusing on the transfer of data into economic/societal value. Unique selling points: top researchers in key areas, unique positioning in terms focus and capabilities, well-connected to industry and scientific networks, Brainport (Smartest Region of the World).

3 the new oil

4 Clive Humby (dunnhumby)

5 We are creating data all the time!

6 Proximity sensor GPS WIFI Bluetooth Touchscreen 14+ sensors Gyroscopic sensor Magnetometer Camera (back) Accelerometer Microphone Camera (front) Ambient light sensor GSM/HSDPA/LTE Finger-print scanner

7 Motivation: Internet of Things PAGE 6

8 "Always On": Anything, Anytime, Anywhere

9 exploration (locating the data), extraction (how to get it), transform (clean and filter data) storage (Big Data) transport (getting it to the right person) usage (analysis, actions, etc.) data can be copied, oil not data is specific, oil is not if small, data storage and transport are cheap

10 fancy analytics tools without suitable data are like sports-cars without petrol

11 Moore's Law: 2 20 = x in 40 years processor speed size hard disk network capacity pixels on screen 1/costs of storage

12 train Eindhoven-Amsterdam in 5 ms

13 flight Amsterdam-New York in 24 ms

14 drive around the world on 3.8 ml petrol

15 so we can produce amazingly Big amounts of data oil but

16 Big Data? PAGE 15

17 Winner Cruiser Class of the World Solar Challenge 2013 Big or fast and efficient?

18 DSC/e's Motto Turning Data into Value

19 What happened?

20 Why did it happen?

21 What will happen?

22 What is the best that can happen?

23 Data Science aims to answer such questions

24 a new profession

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26 What is a Data Scientist?

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28 More than MapReduce and Mining

29 An emerging discipline on the crossroads of multiple existing disciplines

30 Growing Demand for Data Scientists UK

31 Google Searches data science big data

32 survival of the fittest

33 why TU/e?

34 1957

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36 shortest path ALGOL 60 time-shared operating system semaphores dining philosophers problem no GOTO's weakest liberal precondition Turing Award, 1972

37 1981

38 2013

39 A new discipline 1957 mathematics

40 A new discipline 1981 mathematics computer science

41 data science A new discipline mathematics computer science

42 10x best TU CS: 42th globally 5 th EU

43 Societal Challenges: TU/e's Strategic Areas

44 Example Question in health domain

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48 Relevant organizations (small selection) End users Consultancy Software vendors Data science experts + any 50+ organization

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50 organization

51 Overview DSC/e

52 Founding Fathers DSC/e Wil van der Aalst vdaalst.com Emile Aarts Johan van Leeuwaarden Jack van Wijk Ton Koonen Wijnand IJsselsteijn Matthias Rauterberg Fred Langerak

53 20 chairs/groups involved Department of Mathematics and Computer Science Department of Industrial Engineering & Innovation Sciences Department of Electrical Engineering Department of Industrial Design

54 Main focal points of DSC/e Teaching: Creating excellent Data Science education at all levels Research: Addressing the main Data Science challenges of our time Valorization: Transferring results and being inspired by reality/society Ambition: Larger projects involving TU/e, industry, NWO, STW, Horizon 2020, etc. Visibility: Data Science? Come to TU/e!

55 ingredients hardware - software - people disclaimer: just some examples, not aimed at completeness

56 handling the new oil

57 Data science requires Required system bandwidth, it increases a Smart factorphone of 1000 every tenvideo years CallWeb Albums (Amdahl s law) Bandwidth gap can speedonly and be energy addressed by concurrency: more processors. Most of the energy is lost on communication. YouTub Energy consumption e On using copper for Video communication Demand Processing speed following Moore's Using photons instead law (starts to slow of electrons for data down) transmission

58 How much time is needed to get new oil? Amsterdam (120 km) next building (36 m) on table (90 cm) New York (6000 km)

59 In-Memory Computing, e.g., SAP HANA

60 What has happened? Why did it happen? What will happen? What should happen? models and analysis techniques

61 Models should be: - descriptive, - predictive, and/or - prescriptive Are they? Engineers and scientists love models

62 olie-fantenpaadjes

63 We cannot ignore the facts surrounding us PAGE 62

64 PAGE 63

65 Data/Process Mining

66 Example: Process Discovery 627 gynecological oncology patients, events PAGE 65

67 Models as maps Visual analytics interaction between analyst and data human is able to find unknown unknowns

68 Anscombe's Quartet (Francis Anscombe 1973) 4 data sets of 11 elements mean x = 9 var. x = 11 mean y = 7.5 var. y = 4.12 corr. = same lin. regr.

69 SequoiaView: What's on your harddisk?

70 Visualizing trees inspired by nature

71 Visualizing networks

72 Psychology 2.0 Data science as a tool for psychological research

73 Value of data Nov 11, er-valued-investors-marketers-part May 21, aphiti/427964/is-facebook-worth-it/

74 If you are not paying for it, then you are the product!

75 New Business Models If a certain number of people sign up for the offer, then the deal becomes available to all. Crowdsourcing: members can submit ideas and help to determine which products Quirky will design, manufacture, and sell. Service exchange that uses units of time as currency. People connect with others who have the same disease or condition and share their own experiences. The generated data is used by professionals.

76 With great power comes great responsibility! TARGET

77 conclusion

78 Data is the new oil Internet of Events

79 What's new? Evolution or Revolution? "We tend to overestimate the effect of a technology in the short run and underestimate the effect in the long run" (Roy Amara)

80 Organizations not using data smartly will not survive

81 new opportunities

82 Data scientist is the engineer of the future!

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84 DSC/e Let's start!