Importance of Data - Where to find it, how to store, manipulate, and characterize it Artificial Intelligence (AI)- Introduction to AI & ML Technologies/ Applications Machine Learning (ML), Basic Machine Learning algorithms. Applications of AI & ML in Marketing, Sales, Finance, Operations, Supply Chain & Human Resources Data Governance Legal and Ethical Issues Robotic Process Automation (RPA) Internet of Things (IoT) Cloud Computing

1. 1
Emerging Technologies
Dr. Kamal Gulati
drkamalgulati@gmail.com
www.mybigdataanalytics.in

2. Emerging Technologies
 Importance of Data - Where to find it, how to store, manipulate, and characterize it
 Artificial Intelligence (AI)- Introduction to AI & ML Technologies/ Applications
 Machine Learning (ML), Basic Machine Learning algorithms.
 Applications of AI & ML in Marketing, Sales, Finance, Operations, Supply Chain
& Human Resources Data Governance
 Legal and Ethical Issues
 Robotic Process automation (RPA)
 Internet of Things (IoT)
 Cloud Computing
2

3. Some Key Discussions
 1. The Biggest retailer in the World who doesn't own a single retail
store?
 AMAZON
 2. The Biggest hotel chain of the world who doesn’t own a single
hotel
 AIRBNB
 3. The Biggest fleet owner of the world who doesn’t own a single
car?
 UBER
 4. The Biggest entertainment company of the world who doesn’t own
a single cinema or multiplex?
 NETFLIX
 5. The Biggest knowledge bank of the world who has never
published a single book?
 GOOGLE

4. How is this Possible?
4

5. The most common driving distractions by
gender. There are two ways to tell this
 The first is that I give you some statistics as follows:
1.6% of men believe texting is a distraction as compared to 4.2% of the women.
2.Kids in the car cause 9.8% of the men to be distracted as compared to 26.3% of the women.
5

6. Importance of Data –
Where to find it,
how to store,
manipulate, and
characterize it
6

7. What
is
Data ?
A collection of raw
facts and figures.
Raw material
that can be
processed by
any
computing
machine.
A collection of
facts from
which
conclusions
may be drawn.
Data can be
represented in
the form of:
numbers,
Alphabets &
Symbols which
can be stored
in computer's
language.
•i.e. Kamal
Gulati,
kamal@123

8. What is
Information?
Knowledge acquired through
study or experience.
Information helps human beings
in their decision making.
Systematic and meaningful
form of data.

9. Database
• A repository of logically related and similar data.
• An organized collection of related information so that it
can easily be accessed, managed and updated.
• E.g.:
• Dictionary
• Airline Database
• Student Database
• Library
• Railways Timetable
• Bank Customer Database

10. Database Design & Modelling

11. Data Models, Schema and
Instances
 Data Models:
• Describes Structure of the database.
• Aim is to support the development of
information systems by providing the
definition and format of data.
• If the same data structures are used to
store and access data then different
applications can share data.
• Classification:
1. High-Level Model
2. Representation Model
3. Low-Level Model

12. 1. High-Level Model
• Ensures data requirement of the users.
• Not concerned with representation,
but its conceptual form.
• Three Imp terms:
• Entity: Any object, exists physically or
conceptually.
• Attribute: Property or characteristic of entity.
• Relationship: Association or link b/w two entities.
• These 3 terms make Entity-Relationship Model.

13. Entity-Relationship (E-R) Model
College Principal
College
Student C
Student A
Student B
College 3
College 2
College 1
Course C
Course B
Course A
Student Course
Admissio
n
Stud_Nam
e
Stud_Roll
No
Course_I
d
Course_Na
me
Relationships E-R diagram

14. 2. Representation Model
• Representation of data stored inside
a database.
• Describes the physical structure of
the database.
• It uses the concepts which are close to
the end-users.
• Classification:
A. Hierarchical
B. Relational
C. Network

15. A. Hierarchical Database Model
• Developed by IBM, is the Oldest database model.
• Represented using a tree-diagram.
(Parent-child relationship)
• Each box is called a Node
• The nodes represent a record type.
• A line connecting nodes
represents the link.
Director
Manager
(Market.)
Manage
r
(Sales)
Manager
(HR)
Area
Manager
1
Area
Manage
r 2
Area
Manage
r 3
Sales Exe.
1
Sales Exe.
2
Sales
Representative

16. Cont…
• Parent-child type is suited for One-to-many
relationship between two entities.
• But difficult to implement
many-to-many relationship.
e.g.:
IMS system from IBM.
*IMS -Information Management System
Director
Manager
(Market.)
Manage
r
(Sales)
Manager
(HR)
Area
Manage
r 1
Area
Manage
r 2
Area
Manage
r 3
Sales Exe.
1
Sales Exe.
2
Sales
Representative

17. B. Relational Database Model
• Simplest and the most common model.
• Developed in 1970 by E.F. Codd, it became commercial in
the 80s.
• Data elements are stored in
different tables made up of
rows and columns.
Roll No Name Surname Section
1001 Kamal Gulati D
1002 Rahul Singh A

18. Cont…
• Terminologies:
-Data Values: alphanumeric raw data (Kamal)
-Columns: fields (item or object that holds the data)
-Rows: record (a group of data for related field)
-Table: collection (all records & fields)
-Key: identifier (uniquely identifies a row in the
table. It can be value of a single or multiple column.
e.g.:
DB2, ORACLE, SQL Server.
Roll No Name Surnam
e
Section
1001 Kamal Gulati D
1002 Rahul Singh A

19. C. Network Database Model
• Represented using a Data-Structure Diagram.
• Boxes represents the records & lines the links.
• Based on owner-member relationship
• Members of an owner may
be many but for many membe
owner is one.
• Can represent one-to-one and
many-to-many as well.
Teacher 1 Teacher 2 Teacher 3
Course A Course B Course C
Student 1 Student 2 Student 3

20. Cont…
• One-to-many relationship is converted into a set of
one-to-one.
• Also, many-to-many is
converted into 2 or more
one-to-many relationship.
e.g.:
IDMS, IMAGE.
*IDMS: Integrated Database Management System
Teacher 1 Teacher 2 Teacher 3
Course A Course B Course C
Student 1 Student 2 Student 3

21. Database Languages
• Once data is filled, manipulation is required
(insertion, deletion, modification of data)
• For these, a set of languages is provided by DBMS:
1. Data Definition Language.
2. Data Manipulation Language.
3. Data Control Language.

22. 1.Data Definition or Description
Language (DDL):
-Used by DB designers to define schema.
-DDL compiler converts DDL statements and
generate a set of tables which are stored in.
e.g.: CREATE, ALTER & DROP
2. Data Manipulation Language (DML):
-For accessing and manipulating the data.
e.g.: CONNECT, SELECT, INSERT, UPDATE,
DELETE, EXECUTE
3. Data Control Language (DCL):
-Similar to a computer programming language used to control
access to data stored in a database.
-e.g.: GRANT, REVOKE

23. Database System Architectures
• The journey from big mainframe to pc has
also evolved the database and its architecture.
• Classification:
1.Centralized DBMSArchitecture
2.Client-ServerArchitecture
3.Distributed Databases

24. 1. Centralized DBMS
Architecture
• Traditional form, all data, functionality,
apps are located on one machine.
• Access via communication links.
Enterpris
e
databas
e

25. 2. Client-Server Architecture
• Involves a client and a server.
• Clients are PCs or workstations.
• Servers are powerful computers, can manage files,
printers, e-mails.
• Client interacts server when additional functionality
Doesn'texits in its ownmachine.
Client
User interface
Application program
Database server
Database tables
Application server

26. 3. Distributed Database
Architecture
• Decentralized functionality, distributed among many
computers.
• Storage computers are at diff. geographical locations.
Enterpris
e main
database
Fragme
nt
Fragme
nt
Fragme
Fragme
nt
Fragme
nt

27. Advantages of DBMS
1. Controlling Data Redundancy: Data is recorded in
only one place in the database and it is not
duplicated.
2. Data Consistency: Data item appears only once,
and the updated value is immediately available to
all users.
3. Control Over Concurrency : In a computer file-
based system in updating, one may overwrite the
values recorded by the other.

28. Advantages of DBMS Contd.,
4. Backup and Recovery Procedures: automatically
create the backup of data and restore data if required.
5. Data Independence: Separation of data structure of
database from application program that uses the data is
called data independence.

29. Disadvantages of DBMS
1. Cost of Hardware and Software: Processor with
high speed of data processing and memory of large
size is required.
2. Cost of Data Conversion: Very difficult and costly
method to convert data of data file into database.
3. Cost of Staff Training: A lot of amount for the
training of staff to run the DBMS.

30. 3. Appointing Technical Staff: Trained technical persons
such as database administrator, application
programmers, data entry operators etc. are required to
handle the DBMS.
 4. Database Damage: All data is integrated into a single
database. If database is damaged due to electric failure or
database is corrupted on the storage media, then your
valuable data may be lost forever.
Disadvantages of DBMS Contd.,

31. Examples of DBMS
• Some of the commonly used DBMSs are:
-Oracle, IBM’s DB2, Microsoft's SQL Server and
Informix.
• Some of the desktop-based DBMSs are:
-Microsoft FoxPro, Borland dBase and
MicrosoftAccess.

32. Applications of DBMS
1. Airlines and Railways: Online databases for reservation,
and displaying the schedule information.
2. Banking: Customer inquiry, accounts, loans, and other transactions.
3. Education: Course registration, result, and other information.
4. Telecommunications: Communication network, telephone numbers,
record of calls, for generating monthly bills, etc.
5. E-commerce: Business activity such as online shopping, booking of
holiday package, consulting a doctor, etc.
6. Human resources: Organizations use databases for storing
information about their employees, salaries, benefits, taxes, and for
generating salary checks.

33. 1. Introduction to Databases
2. Fundamentals of Data Modeling and
Database Design
3. Database Normalization
4. Types of keys in database management
system
5. Distributed Database
More Contents on Database

34. DATA EXAMPLE
34

35. Artificial Intelligence (AI)-
Introduction to AI & ML Technologies/
Applications
Machine Learning (ML), Basic Machine
Learning algorithms.
Applications of AI & ML in Marketing,
Sales, Finance, Operations, Supply
Chain & Human Resources Data
Governance
35

36. 2
First,Let’sget
the PathRight

37. Sci-Fi Movies that might Become Reality

38. The Terminator (Parts)

40. What is Artificial
Intelligence?

41. History of Artificial
Intelligence

42. Artificial Intelligence Use Cases

43. Artificial Intelligence Use Cases

44. Artificial Intelligence Applications

45. Artificial Intelligence Applications

46. Artificial Intelligence Applications

47. Artificial Intelligence Applications

48. Artificial Intelligence Applications

49. Artificial Intelligence Applications

50. Ways of Achieving AI
1
Machine
Learning
2
Deep
Learning

51. Machine Learning
Machine learning refers to
the use of algorithms to
parse data, process and
learn from it, in order to
make predictions or
determinations about
something.
One of the best application
for machine learning is
computer vision: OCR,
object tracking, object
recognition etc.

54. Deep Learning
Deep learning is a subfield
of machine learning
concerned with algorithms
inspired by the structure
and function of the brain
called artificial neural
networks (ANNs)
Compared to older ML
algorithms, Deep Learning
performs better with a
large amount of data

56. Legal and Ethical Issues
56

57. Video Cases
Case 1: What Net Neutrality Means for You
https://www.youtube.com/watch?v=zq-2Yk5OgKc
Case 2: Facebook and Google Privacy: What
Privacy?
https://www.youtube.com/watch?v=NCF54nqabB0
Case 3: Data Mining for Terrorists and
Innocents
https://www.youtube.com/watch?v=nwxUIEfzr6Q
57

58. What Ethical, Social, and Political Issues Are Raised by
Information Systems?
 Recent cases of failed ethical judgment in business
–In many, information systems used to bury decisions from public scrutiny
 Ethics
–Principles of right and wrong that individuals, acting as free moral agents,
use to make choices to guide their behaviors
 Information systems raise new ethical questions because they create
opportunities for:
–Intense social change, threatening existing distributions of power, money,
rights, and obligations
–New kinds of crime

59. The Relationship Between Ethical, Social, and Political Issues in an
Information Society

60. Five Moral Dimensions of the Information
Age
Information rights and obligations
Property rights and obligations
Accountability and control
System quality
Quality of life

61. Key Technology Trends that Raise Ethical Issues
Computing power doubles every 18 months
Data storage costs rapidly decline
Data analysis advances
Networking advances
Mobile device growth impact

62. Advances in Data Analysis Techniques
Profiling
Combining data from
multiple sources to
create dossiers of
detailed information on
individuals
Nonobvious
relationship
awareness (NORA)
Combining data from
multiple sources to find
obscure hidden
connections that might
help identify criminals
or terrorists

63. Nonobvious
Relationship
Awareness
(NORA)
https://www.slideshare.net/kamalgulati7/ethical-and-social-issues-
in-information-systems-92759301

64. Robotic Process automation (RPA)
Internet of Things (IoT)
Cloud Computing
64

65. The new IT platform will enable the 4th wave of economic
revolution

66. Industrial Intelligent
Automation
• Industrial Intelligent Automation will be enabled by IoT, Cognitive/AI, Analytics,
Intelligent Machines and Assembly Lines, Robots and Robotics, Edge (Fog)
Computing and Swarming Technology.
• We are seeing an increased number of robots designed for industry specific
applications.

67. Contd…
The future of farming involves robots and drones

68. Intelligent Automated Transportation
Systems
Technologies such as IoT, Cognitive/AI, analytics, advanced vehicle communications,
edge (fog) computing and swarming technology, will enable
driverless on-demand vehicles… virtually eliminating accidents, reducing
congestion and pollution, while increasing productivity.

69. Contd…
Driverless cars get all the press but think about all the otherautonomous
transportation vehicles that are coming…

70. Autonomous On DemandAviation
The future of air transportation will be autonomous and on demand. Enabling
technologies will include IoT,AI, Cloud, Fog Computing, SwarmingTechnology,
Quantum Computing technology.
Companies to watch:
• Uber
• Airbus
• Kitty Hawk / Zee.Aero
• JobyAviation
• UrbanAeronautics
• LiliumAviation
• AeroMobile
• Volocopter

71. What will come??
The Future of Work will involve a partnership between humansand
cognitive systems technology.

72. Where are we headed
The future (2040-50) IT platform will be very fast and optimizedfor
distributed cloud-based cognitive applications.
Characteristics:
•Distributed / Edge Computing
•Secure
•Data as an Asset
•Blockchain
•Analytics
•Cognitive
•UX by Design
•Very Fast a Zettascale computing (1021)?

73. “How much more IOT can do is only left to your imagination and to
your budget. You can do as little or as much with IoT as you want.”
Internet of Things (IOT)
- WeAre At The Tip of AnIceberg

74. The Internet Of Things
IDC estimates there will
be approximately 212
billion things globally by
the end of 2025.
Extreme Networks
estimates that 5 billion
people will have
Internet
access.
The ‘Internet of Things’
will generate
$14,400,000,000 of
value over the next
decade1.
There will be 40 times
more devices than
people on the Internet in
20252.

76. Internet of Things Wave

78. 1. SENSORS andActuators
• We are giving our world a digital nervous system. Locating data using
GPS sensors. Eyes and ears using microphones and cameras, along with
sensory organs that can measure everything from temperature to pressure.

79. 2. CONNECTIVITY
• These inputs are digitized and placed onto networks.
Source: http://postscapes.com/what-exactly-is-the-internet-of-things-

81. Source: http://postscapes.com/what-exactly-is-the-internet-of-things-

84. Source: http://postscapes.com/what-exactly-is-the-internet-of-things-

85. Contd…
Source: http://postscapes.com/what-exactly-is-the-internet-of-things-

86. Contd…
Source: http://postscapes.com/what-exactly-is-the-internet-of-things-

87. Source: http://postscapes.com/what-exactly-is-the-internet-of-things-

88. Source: http://postscapes.com/what-exactly-is-the-internet-of-things-

90. Big Data Analytics

91. Evolution Of
Technology
Source: www.edureka.com/big-data-and-

92. Internet of Things(IoT)
Source: www.edureka.com/big-data-and-

93. Social
Media
Source: www.edureka.com/big-data-and-

94. Other Factors
Source: www.edureka.com/big-data-and-

95. What is BIG DATA?
Source: www.edureka.com/big-data-and-

97. Astonishing Growth of BIG
DATA
Today, every two days we create as much data as we did from the beginning
of time untill 2000.
• By 2020, the amount of digital
information will have grown from
around 5 zettabytes today to 50
zettabytes
• Now a days, almost every action
we take leaves a trial
• We generate data whenever we
go online, use our GPS-
equipped smartphones,
communicate our friends
through social media or do
online shopping Image source:

98. 5 V’s of BIG DATA
• 1. VOLUME
Source: www.edureka.com/big-data-and-

99. 2. VARIETY
Source: www.edureka.com/big-data-and-

100. 3. VELOCITY
Source: www.edureka.com/big-data-and-

101. 4. VALUE
Source: www.edureka.com/big-data-and-

102. 5. VERACITY
Source: www.edureka.com/big-data-and-

103. 5 V’s of BIG DATA

104. BIG DATAas an Opportunity

105. BIG DATA Analytics
• Big Data collected by Smart Meter
Source: www.edureka.com/big-data-and-

106. How Smart Meter BIG DATAis Analyzed

107. IBM Smart Meter
Solution

108. Hadoop: Solution to BIG DATA Problems

109. Hadoop Ecosystem

110. Cloud Computing
When it’s smarter to rent than to buy

111. Cloud Computing
An environment created in a user’s
machine from an on-line application
stored on the cloud and run through
a web browser.
In simple language Cloud computing
is using the internet to access
someone else's software running on
someone else's hardware in someone
else's data center.

112. Cloud Computing
Services
►Software as a Service (SaaS)-
End Users
►Platform as a Service (PaaS)-
Application Developers
►Infrastructure as a Service
(IaaS)-NetworkArchitects

113. Software as a Service(SaaS)
• Just run it for me!
• Also known as On-demand Service.
• An application that can be accessed
from anywhere on the world as long
as you can have an computer with an
Internet connection.
• We can access this cloud hosted
application without any additional
hardware or software.eg: G-mail,
Yahoo mail, Hotmail etc..,
• Also they can provide security
features such as SSL encryption,a
cryptographic protocol.

114. Platform as a Service (PaaS)- Application Developers
• Give us nice API (Application
Programming Interface) and take care
of the implementation.
• In the PaaS model, cloud providers
deliver a computing platform and/or
solution stack typically including
operating system, programming
language execution environment,
database, and web server.
• It is a platform for developers to
write and create their own SaaS i.e.
applications. which means rapid
development at low cost.
• E.g.: Salesforce.com, Windows Azure
etc.
Source: www.slideshare.net/cloud-

115. Infrastructure as a Service (IaaS)- NetworkArchitect
• Also known as hardware as a service.
• It is a computing power that you can rent for a limited period of time.
• Allows existing applications to be run on a cloud suppliershardware.
• Cloud providers offer computers – as physical or more often as virtual
machines – raw (block) storage, firewalls, load balancers, and networks
Source: www.slideshare.net/cloud-

116. Cloud Architecture
Source:
https://en.wikipedia.org/wiki/Cloud_computing

117. Modes of Clouds
• Public Cloud
► Computing infrastructure is hosted by cloud vendor at the vendors
premises.
► and can be shared by various organizations.
► E.g. : Amazon, Google, Microsoft, Salesforce
• Private Cloud
► The computing infrastructure is dedicated to a particular organization
and not shared with other organizations.
► more expensive and more secure when compare to public cloud.
► E.g. : HP data center, IBM, Sun, Oracle, 3tera
• Hybrid Cloud
► Organizations may host critical applications on privateclouds.
► where as relatively less security concerns on publiccloud.
► usage of both public and private together is called hybridcloud.
Source:

118. Distributed vs. Grid vs. Cloud
Parameters Distributed Grid Cloud
Time Weeks to Months Days to Weeks Minutes
Scalability Slowest, Rigid and
Costly
Slower, somewhat
flexible, costly
Instant, Flexible,
Pay-per-usage
Cost High CapEx Costly, sometime
monthly/yearly
contracts, no capEx
No contracts, usage
based, no upfront
costs
*Green* Low Low High- Virtualized
Pricing Model Buy servers and pay
fully weather used
or not
Rent servers and
hosting cost
weather used or not
Rent based on usage
only
Source: www.slideshare.net/cloud-

119. Is Cloud Computing reduces E-
Wastes?
• Green IT Cloud Computing
• Cloud Computing is Eco-Friendly.
• We can reduce E-waste by using Cloud
Computing i.e. by Infrastructure as a
Service (IaaS).
• Cloud Computing Helps to Accelerate
Green IT
• Can reduce Global Warming too..

122. BusinessGoals:
Provide visual environment for
building custom mobile
application
Charge customers based on
the platform they are using,
number of consumers’
applications etc.
Business Area:
Cloud based platform for building,
deploying, hosting and managing
of mobile applications
Case Study #1: Usage & Billing Analysis

123. ArchitecturalDecisions
▪ Reliability(24/7)
▪ Security(Multitenancy)
▪ Self-Service(Ad-Hoc reports)
▪ Cost(Thelessthebetter  )
▪ Constraints(PublicCloud)
ArchitectureDrivers:
▪ V
olume(> 10TB)
▪ Sources(Semi-structured- JSON)
▪ Throughput(>10K/sec)
▪ Latency(2min)
▪ Extensibility(Custommetrics)
▪ DataQuality(Consistency)
Trade-off:
Extended
Relationa
l
Non-Relational
Extensibility ‐ +
DataQuality + ‐
Self-Service + ‐
 Extended RelationalArchitecture
 Extensibility viaPre‐allocated
Fields pattern

124. SolutionArchitecture
Technologies:
• AmazonRedshift
• AmazonSQS
• AmazonS3
• ElasticBeanstalk
• JaspersoftB
IProfessional
• Python

125. Business Goals:
Build in-house Analytics Platform for ROI
measurement and performance analysis of every
product and feature delivered by the e-commerce
platform;
Provide the ability to understand how end-users are
interacting with service content, products, and
features on sites;
Do clickstream analysis;
Perform A/B Testing
Business Area:
Retail. A platform for e-commerce
and collecting feedbacks from
customers
Case Study #2: Clickstream for retail website

126. Extended
Relation
al
Non-
Relation
al
Volume/Scalability +/‐ +
Throughput + +
Self-Service + +/‐
Extensibility ‐ +
ArchitecturalDecisions
▪ Reliability(24/7)
▪ Security(Multitenancy)
▪ Self-Service(Cannedreports,Data
science)
▪ Cost(Thelessthebetter  )
▪ Constraints(PublicCloud)
ArchitectureDrivers:
▪ V
olume(45TB)
▪ Sources(Semi-structured- JSON)
▪ Throughput(> 20K/sec)
▪ Latency(1hour)
▪ Extensibility(Customtags)
▪ DataQuality(Notcritical)
Trade-off:
 Non‐RelationalArchitecture
 Reporting via MaterializedView
pattern

127. SolutionArchitecture
Technologies:
• AmazonS3
• Flume
• Hadoop/HDFS,MapReduce
• HBase
• Oozie
• Hive
Node1
Node 2
NodeN

128. Tips for Designing Big Data Solutions
 Understand data users and sources
 Discover architecture drivers
 Select proper reference architecture
 Do trade-off analysis, address cons
 Map reference architecture to technology
stack
 Prototype, re-evaluate architecture
 Estimate implementation efforts
 Set up devops practices from the very
beginning
 Advance in solution development through
“small wins”
 Be ready for changes, big data
technologies are evolving rapidly

129. The Data Science behind IPL
How should they judge in detail:
“Which player should they buy and which one they shouldn’t
it?”, “How much money should be spent on which
player?” or “What are the values of the different players?”.
Case Study #3: Data Science in IPL

131. Case Study #4: Data Science in Base Ball
Billy Beane, a baseball general
manager, and Peter Brand, an
economics graduate, challenge
convention as they try to form a
competitive sports team using
computer-based methods.

132. • Cancer is an incredibly complex disease; a single tumor can have
more than 100 billion cells, and each cell can acquire mutations
individually. The disease is always changing, evolving, and adapting.
• Employ the power of big data analytics and high-performance
computing.
• Leverage sophisticated pattern and machine learning algorithms to
identify patterns that are potentially linked to cancer
• Huge amount of data processing and recognition
13
2
Case Study #5: Data Science Cancer Research

133. • Stanford Medicine, Google
team up to harness power of
data science for health care
• Stanford Medicine will use the
power, security and scale of
Google Cloud Platform to
support precision health and
more efficient patient care.
• Analyzing genetic data
• Focusing on precision health
• Data as the engine that
drives research
13
3
Source: http://med.stanford.edu/news/all-news/2016/08/stanford-medicine-google-team-up-to-harness-power-of-data-science.html
Case Study #6: Data Science: Health Care

134. Data Science:
• The Obama campaigns in 2008 and 2012 are credited for their
successful use of social media and data mining.
• Micro-targeting in 2012
– http://www.theatlantic.com/politics/archive/2012/04/the-
creepiness-factor-how-obama-and-romney-are-getting-to-know-
you/255499/
– http://www.mediabizbloggers.com/group-m/How-Data-and-Micro-
Targeting-Won-the-2012-Election-for-Obama---Antony-Young-
Mindshare-North-America.html
• Micro-profiles built from multiple sources accessed by aps, real-
time updating data based on door-to-door visits, focused media
buys, e-mails and Facebook messages highly targeted.
• 1 million people installed the Obama Facebook app that gave
access to info on “friends”.
22
Case Study #7: Data Science: Elections

135. The Science of Election Forecasting
 Opinion Polls
 Poll of Polls
 Economic & Political Drivers
 Challenges in the Indian Context


136. Data Science: Case Study
13
6
Case Study #8: Data Science: Customer
Analytics

137. RPA - Evolution of Era,Industry4.0
The4 Industrial Revolution by Christopher Roser

138. 1700’sFirstIndustrial
Revolution
Mechanical
Technology equipped steam
and water to power the first
factories
1800’sSecondIndustrial
Revolution
Electrical:
Electricity made possiblethe
division of labor & mass
production
1900’sThirdIndustrial
Revolution
Automation:
ITenabled programmable
work &limited the reliance on
manual labor
Today
Fourth Industrial Revolution
Connected
Cyber-physical systems,
powered by IoTand fuelled by
data, create afully
interconnected society
INDUSTRIAL REVOLUTION
Asthe new job roles emanating out of upcoming disruptions areentrepreneurial,
scientific, creative, and disruptive in nature, it is necessary for usto reform the
education and skilling ecosystem

139. At the sametime:
GenNext & their Preferences also EvolvingFaster

140. Gen‘Z’Learners
• Highly connected,tech-savvy
• Amind-set that technology cansolve everyproblem
• Seekinvolvement in learning processbut at their ownpace
• Welcome challenges, enjoy groupworking
• Time & place agnostic learning, LoveFreedom
• Engagingthem in atraditional way is highlychallenging

141. 18
The Road
Ahead forYou

142. “The greatest value of a picture is
when it forces us to notice what we
never expected to see.”
John Tukey
24

143. 143

144. 144

145. Conclusion
• IT is now called industry 4.0 which means artificial
intelligence, smart devices, big data, social media.
• IT and computer network technology is continuing to
develop in new and interesting ways.
• Some key developments that have transpired over the past
several years include:
• The growing popularity of cloud computing and cloud
storage
• An array of new devices targeted at the Internet of Things
(IoT) market will undoubtedly compete for our attention.
• The field of AI is growing very fast and develops computers
and machines with human-like intelligence which is
dangerous for the humanity.

146. 146
Q&A

147. 147