This document discusses how data science and big data are changing the world. It describes the growth of data and computing power from the early internet to today. Now, half the world's population is connected online and enormous amounts of data are generated daily by both people and devices. This data is stored, analyzed by companies to accomplish business tasks, and to capture consumer behavior for targeted ads and recommendations. The field of data science emerged to make discoveries from big data using skills like problem solving, statistics, and machine learning. Examples of data science uses in many industries are provided. Advancements in artificial intelligence research, like deep learning, have also driven major changes through technologies from companies like Google, Microsoft, and Nvidia.

1. HOW DATA SCIENCE AND BIG DATA
ARE CHANGING THE WORLD
Alexander Sokolov

2. WE ARE LIVING IN A RAPIDLY
CHANGING WORLD
Technology is all around us
It is tightly integrated in our life

3. THE BIRTH OF WORLD WIDE WEB
▸1989 - Tim Berners-Lee submits a proposal for a distributed
information system at CERN
▸1990 - The world's first website and server go live at CERN
▸1991 - First web server outside of Europe
▸1993 - CERN releases WorldWideWeb source code
▸1994 - Tim Berners-Lee founds the World Wide Web
Consortium

4. RAPID GROWTH OF SERVICES IN EARLY
INTERNET
▸Instant messengers
▸P2P clients for file sharing
▸Internet search services
▸Pizza ordering!

5. INTERNET CHANGES OUR DAILY LIFE
HABITS
▸Read news
▸Communicate
▸Travel
▸Meet new people
▸Share knowledge

6. HALF OF THE WORLD’S POPULATION IS
CONNECTED TO THE INTERNET
3.2 billion people online

7. ENORMOUS AMOUNT OF DATA IS
GENERATED
0
12.5
25
37.5
50
2010 2012 2014 2016 2018 2020
Zettabytes

8. MOST OF THIS DATA ARE PHOTOS OF
CATS!

9. NOT ONLY PEOPLE GENERATE DATA
By 2020 more than 34 billion internet-connected devices will be
installed globally

10. INTERNET OF THINGS
▸Connected devices
▸Networks of sensors
▸Smart home appliances
▸Health trackers
▸And many many more…

11. INDUSTRY 4.0

12. APOLLO GUIDANCE COMPUTER
▸In 1969 AGC had 64Kbyte of memory
and operated at 0.043MHz
▸Today our household appliances are
more advanced then AGC.
▸Imagine that tiny fraction of data and
processing power was enough to put a
man on the Moon

13. WHAT WE DO WITH MOST OF THAT
DATA?
▸We store and analyse it
▸Companies gather it to accomplish their business tasks
▸Capture our behaviour
▸Showing relevant ads
▸Recommend new products

14. IS THIS ETHICAL?
▸Probably some things aren’t
▸Intelligence agencies are among first who adopted Big Data
▸Palantir Technologies is a private company who's original
clients were federal agencies of the United States
Intelligence Community.

15. SOME SOFTWARE OPEN SOURCED BY
INTELLIGENCE AGENCIES
▸Accumulo was created in 2008 by the US National Security
Agency and contributed to the Apache Foundation as an
incubator project in September 2011
▸Gaffer is a framework for building systems that can store and
process graphs released by GCHQ (British Intelligence
Agency)

16. IT ALL STARTED IN LABS OF SOFTWARE
GIANTS AND ACADEMIA
▸2003 - The Google File System
▸2004 - MapReduce: Simplified Data Processing on Large Clusters
▸2006 - Hadoop. Development started in the Apache Nutch project, but
was moved to the new Hadoop subproject
▸2008 - Hive. Development started by Facebook
▸2009 - Spark was initially started at UC Berkeley's AMPLab. Open
sourced in 2010 under a BSD license.
▸Kafka was originally developed by LinkedIn, and was subsequently open
sourced in early 2011

17. THIS GAVE START TO NEW AREA IN
SOFTWARE ENGINEERING
Companies like Cloudera, Hortonworks and MapR specialise in
Big Data software and consulting

18. IT’S NOT ONLY ABOUT GATHERING AND
STORING DATA
The main purpose is analytics

19. THE FIELD OF DATA SCIENCE EMERGED
Data Scientist: The Sexiest Job of the 21st Century, HBR
October 2012. Thomas H. Davenport, D.J. Patil

20. HIGHLY SKILLED DATA WIZARDS
▸Highly skilled professional with advanced training and
natural curiosity to make discoveries in the age of big data.
▸Term was coined by D.J. Patil, and Jeff Hammerbacher who
were leading data analytics efforts in LinkedIn and
Facebook.

21. TYPICAL SKILL SET OF DATA SCIENTIST
▸Problem Solving
▸Communication
▸Business Understanding
▸Statistics and Math
▸Machine Learning
▸Software Engineering and Distributed Computing

23. TYPES OF DATA SCIENTISTS
▸Type A Data Scientist: The A is for Analysis. Mostly have
statistics background. Have all necessary skills to do data
cleaning, deal with large data sets and have deep
knowledge of particular domain.
▸Type B Data Scientist: The B is for Building. Also have
statistical background, but also have very strong software
engineering skills and are responsible for delivering data
products to production

24. DATA SCIENCE USE CASES
EVERYWHERE

25. DATA SCIENCE USE CASES
▸Marketing
▸Sales
▸Logistics
▸Risk
▸Customer Support
▸Human Resource
▸Healthcare
▸Financial
▸Retail
▸Insurance
▸Construction
▸Life Sciences
▸Travel
▸Education

26. MY FAVOURITE
▸Genome Research
▸Human Brain Research
▸Medical Research
▸Obama Presidential Campaign 2012

28. UKRAINIAN SUCCESS STORIES
▸Looksery acquired by Snapchat
▸Viewdle acquired by Google

29. ALL THIS IS BACKED UP BY
ADVANCEMENTS IN AI RESEARCH

30. THE BIG BANG
▸Yann LeCun’s work in Convolutional Neural Nets
▸Geoff Hinton’s back-propagation and Stochastic Gradient Descent
▸Andrew Ng’s large-scale use of GPUs to accelerate Deep Neural
Networks
▸In 2011 The Google Brain project achieved amazing results — it
learned to recognize cats in movies on YouTube.
▸In 2012 Alex Krizhevsky of the University of Toronto won the
ImageNet computer image recognition competition.

31. NVIDIA CHANGES ITS DIRECTION
▸Nvidia was famous by it’s graphic accelerators for games, 3d
modelling and CAD software
▸Now it’s moving towards deep learning and computer vision
hardware
▸Also provides software to boost computation performance.
E.g. CUDA, CuDNN, cuBLAS, cuSPARSE

32. SPECIALISED HARDWARE
▸Google’s custom ASIC chip called Tensor Processing Unit (TPU). Built
for machine learning and TensorFlow
▸Movidius Fathom (Myriad 2 chip) specialised for computer vision. Packed
in a USB drive.
▸Microsoft accelerates it’s CNNs using FPGA chips while maintaining low
energy usage.
▸Nvidia DGX-1 Deep Learning Supercomputer
▸Nvidia Jetson TX1/TK1 and DRIVE PX 2/DRIVE CX
▸Intel announced new Xeon Phi tweaked for Deep Learning

33. GIANTS GOING OPEN SOURCE
▸In 2015 Google open sourced TensorFlow. Machine
Learning software library used inside Google for both
research and production. Originally developed by the Google
Brain team
▸In August 2016 Facebook open sourced fastText. Software
library for efficient learning of word representations and
sentence classification.

34. DEEP LEARNING SPECIFIC SOFTWARE
▸Caffe
▸Theano
▸Torch
▸Keras
▸Deeplearning4j
▸Microsoft Computational Network Toolkit (CNTK)

35. SOME PEOPLE ARE WORRIED ABOUT AI
▸“We need to be super careful with AI. Potentially more
dangerous than nukes.” Elon Musk
▸"In my view, biological humans will not be outpaced by the AI
because we will enhance ourselves with AI," Ray Kurzweil

36. EVERYTHING IS GOING TO BE FINE