Slides from the Upgrad workshop conducted by Ganes Kesari on 10th Dec'16 at Hyderabad.

1. 1
UPGRAD WORKSHOP
10TH DEC’16 @HYD
ENTERING THE
DATA ANALYTICS INDUSTRY
B GANES KESARI
VP, GRAMENER

2. 2
DATA ANALYTICS ?
WHAT’S THE BUZZ AROUND ANALYTICS

3. We have internal
information. Getting
information from outside is
our challenge. There’s no
way of doing that.
– Senior Editor
Leading Media Company
“

4. INDIA’S RELIGIONS
4

5. AUSTRALIA’S RELIGIONS
5

6. 6

7. WHAT ARE PEOPLE LOOKING FOR IN DATA ANALYTICS?
7
USA India
data analytics jobs
data analytics tools
data analytics salary
data analytics training
Jobs & Salary Tools Companies
Training &
Courses
data analytics courses
data analytics tools
data analytics jobs
data analytics companies
Source:https://google.com, https://google.co.in

8. WHAT’S THE POPULARITY OVER TIME?
8
“Data Analytics”
Source:https://trends.google.com/

9. WHICH CITIES HAVE INTEREST IN DATA ANALYTICS?
9Source:https://trends.google.com/
0 20 40 60 80 100 120
Gurgaon
Pimpri-Chinchwad
Noida
Bengaluru
Hyderabad
Chennai
Singapore
Mumbai
San Francisco
Dublin
Boston
Washington
Pune
Howrah
Toronto
New York
Sydney
New Delhi
Chicago
Melbourne

10. 10
WHAT’S THE STATE OF THE
DATA ANALYTICS JOBS

11. WHO’S RECRUITING THE TEAMS?
11
0 50 100 150 200 250 300 350 400 450
IBM India
Accenture
JPMorgan
KPMG
Concentrix Daksh
Microsoft India
Ernst & Young
UnitedHealth Group
Shell India Markets
Amazon Dev Centre
GE India Technology
Hewlett-Packard
Deloitte
Cisco Systems
WNS
Xerox
eClerx Services
Mphasis
AIG Analytics
Sapient Consulting
#Jobs
Source:https://www.naukri.com

12. WHAT INDUSTRIES USE DATA ANALYTICS?
12
0% 10% 20% 30% 40% 50% 60%
Software
Banking, Financial Services
Internet, Ecommerce
KPO, Research, Analytics
BPO, Call Centre, ITES
Recruitment, Staffing
Strategy Mgmt Consulting
Media & Entertainment
Advertising & PR
Accounting & Finance
Telcom, ISP
Education, Teaching & Training
Pharma, Biotech & Clinical Research
Insurance
FMCG, Foods & Beverage
Source:https://www.naukri.com

13. WHAT DO THEY PAY?
13
0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0%
0-3 Lakhs
3-6 Lakhs
6-10 Lakhs
10-15 Lakhs
15-25 Lakhs
25-50 Lakhs
50-75 Lakhs
75-100 Lakhs
100+ Lakhs
Source:https://www.naukri.com

14. WHERE ARE THE DATA ANALYTICS JOBS?
14Source:https://www.naukri.com
0% 5% 10% 15% 20% 25%
Bengaluru
Delhi NCR
Mumbai
Gurgaon
Hyderabad
Others
Pune
Noida
Chennai
Delhi

15. WHO ARE THE BIG PLAYERS IN THIS SPACE?
15Source:Gartner BI Magic Quadrant

16. WHICH STARTUPS OFFER DATA ANALYTICS IN INDIA?
16Source: https://angel.co/
... and more

17. 17
WHY DATA ANALYTICS?
WHAT’S CAUSING ALL THIS BUZZ

18. CLASSES OF ANALYTICAL SOLUTIONS
18
Proactive ActionWhat should I do to achieve mygoal?
Data products, data validated actions,
increased success rate of strategic
initiatives
ModeApproach to data Benefits
Proactive DecisionsWhat is likely to happen?
Support for strategic initiatives,
forward looking decision making
Proactive Consumption
ActiveWhat happened ? Marginal business benefits
, process gap identification
Why did it happen?
Significant improvements
from status quo, data backed
management

19. 19
Proactive Action
ModeApproach to data Benefits
Proactive Decisions
Proactive Consumption
Active
Operational Reporting
for measurement of
efficiency & compliance
Marginal business benefits
, process gap identification
CLASSES OF ANALYTICAL SOLUTIONS

20. TIMES NOW COVERAGE HAD
80%+ VIEWERSHIP 20

21. 21
Proactive Action
ModeApproach to data Benefits
Proactive Decisions
Proactive ConsumptionRoot Cause Analysis ,
Benchmarking and multi-
dimensional analysis
Significant improvements
from status quo, data backed
management
Active
CLASSES OF ANALYTICAL SOLUTIONS

22. DETECTING FRAUD
“
We know meter readings are
incorrect, for various reasons.
We don’t, however, have the
concrete proof we need to start
the process of meter reading
automation.
Part of our problem is the
volume of data that needs to be
analysed. The other is the
inexperience in tools or
analyses to identify such
patterns.
ENERGY UTILITY

23. This plot shows the frequency of all meter readings from Apr-
2010 to Mar-2011. An unusually large number of readings are
aligned with the tariff slab boundaries.
This clearly shows collusion
of some form with the
customers.
Apr-10 May-10Jun-10Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11
217 219 200 200 200 200 200 200 200 350 200 200
250 200 200 200 201 200 200 200 250 200 200 150
250 150 150 200 200 200 200 200 200 200 200 150
150 200 200 200 200 200 200 200 200 200 200 50
200 200 200 150 180 150 50 100 50 70 100 100
100 100 100 100 100 100 100 100 100 100 110 100
100 150 123 123 50 100 50 100 100 100 100 100
0 111 100 100 100 100 100 100 100 100 50 50
0 100 27 100 50 100 100 100 100 100 70 100
1 1 1 100 99 50 100 100 100 100 100 100
This happens with specific
customers, not randomly.
Here are such customers’
meter readings.
Section Apr-10 May-10 Jun-10 Jul-10 Aug-10 Sep-10 Oct-10 Nov-10 Dec-10 Jan-11 Feb-11 Mar-11
Section 1 70% 97% 136% 65% 110% 116% 121% 107% 114% 88% 74% 109%
Section 2 66% 92% 66% 87% 70% 64% 63% 50% 58% 38% 41% 54%
Section 3 90% 46% 47% 43% 28% 31% 50% 32% 19% 38% 8% 34%
Section 4 44% 24% 36% 39% 21% 18% 24% 49% 56% 44% 31% 14%
Section 5 4% 63% -27% 20% 41% 82% 26% 34% 43% 2% 37% 15%
Section 6 18% 23% 30% 21% 28% 33% 39% 41% 39% 18% 0% 33%
Section 7 36% 51% 33% 33% 27% 35% 10% 39% 12% 5% 15% 14%
Section 8 22% 21% 28% 12% 24% 27% 10% 31% 13% 11% 22% 17%
Section 9 19% 35% 14% 9% 16% 32% 37% 12% 9% 5% -3% 11%
If we define the “extent of
fraud” as the percentage
excess of the 100 unit
meter reading,the
value varies
considerably
across sections,
and time
New section
manager arrives
… and is
transferred out
… with some
explainable
anomalies.
Why would
these happen?
Simple histograms have been applied to manage ALM compliance,
fraud in corporate directorships, and collusion in schools

24. What do the children in schools know and can do at
different stages of elementary education?
Have the inputs made into the elementary education
system had a beneficial effect or not?
24

25. HAVING BOOKS IMPROVES READING ABILITY
Having more books at home improves the performance of children when it
comes to reading. (But children typically only have only 1-10 books at home)
Number of students sampled
What is the impact? How many more marks
can having more books fetch?
Circle size indicates number of students with
this response. Few students have no books.
Is this response (“25+ books”) good or bad?
Small red bars indicate low marks. Large
green bars indicate high marks. Students
having 25+ bookstend to score high marks.
The most common response is marked in
blue. This is also the circle.
The graphic is summarized in words
Indicates whether the best response is the
most popular. Blue means that it is not.
Green means that it is. Red means that the
worst level is the most popular response.
25

26. HAVING MORE SIBLINGS DOESN’T HELP READING
Children with 1 sibling do much better than children with many siblings
26

27. … BUT HELPS A LOT IN MATHEMATICS
Children with 4+ siblings do very well, children with 1 sibling fare poorly
27

28. TUITIONS HELP A LITTLE
… BUT NOT CHILDREN WITH 4+ SIBLINGS
28

29. TUITIONS HELP A LITTLE
… BUT NOT CHILDREN OF ILLITERATE PARENTS
29

30. CHILDREN LIKE GAMES, AND THEY’RE GOOD
… but playing daily hurts reading ability 30

31. 31
Proactive Action
ModeApproach to data Benefits
Proactive Decisions
Proactive Consumption
Active
Statistical Analysis thru
Segmentation, Decision Trees and
Cause-effect Modelling
Support for strategic initiatives,
forward looking decision making
CLASSES OF ANALYTICAL SOLUTIONS

32. 32
Telecommunication
“ How to predict
customer churn,
atleast a month
ahead”

33. 33
Background & Objective
Gramener Approach
Customer churn is a well noted problemin telecom industry today. One of the leading telecom
operator in the country wanted to predict the churn rate 2/4 week before using an analytical
model.
Exploratory
Analysis &
influencers
Predictive
Intervention
Linear
Discriminant
Parameters
Exploratory business analysis
performed to identify
influencers & create additional
derived metrics & derived
dimensions
Using selective metrics,
models were built on Linear
Classification like Decision
trees, Linear Discriminant
Parameters
Non – Linear
Models
Using selective metrics
non-linear families of
models were built: Neural
Networks, Random Forests
& Support Vector
Machines
• The best model was
implemented & compared
with a control set
• Targeted promotions for
predicted set yielded ~60%
reduction in churn
CLASSES OF ANALYTICAL SOLUTIONS

34. MODEL BUILDING & FINE-TUNING
Models Deployed
üPair-wise correlation
üMulti-linear regression
üLinear Discriminant Analysis
üDecision Tree
üSupport Vector Machines
üNeural Networks
üRandom Forest
Other Variability
üPredict Duration
üAgeing of model
Input Metrics - Customer
ü Incoming & Outgoing Minutes
ü Incoming & Outgoing Calls
ü Daily Mobile Usage
ü Closing Balance
ü Customer activation date
Input - Derived & Growth Metrics
ü Last/Average Closing balance in a month
ü Days since the last Outgoing Call
ü Days since the last Recharge
ü Total Decrement
ü Monthly Refill Amount
ü Total Minutes incl Incoming & Outgoing
ü Percentage of Incoming Minutes
ü Recharge Values

35. 8.3% 0.0%
MISSED WASTED
6.61
COST PER CUST.
0.0%
IMPROVEMENT
Base
MODEL
OK
WASTED
Marketing cost
Rs 40
MISSED
Acquisition cost
Rs 80
OK
No churn Churn
NochurnChurn
Prediction
Actual

36. ~1-2% ~2-3%
MISSED WASTED
~2.0-3.0
COST PER CUST.
~40-50%
IMPROVEMENT
Random
Forest/SVM/etc
MODELS

37. 37
Proactive Action
ModeApproach to data Benefits
Proactive Decisions
Proactive Consumption
Active
Data driven decision making, thru
advanced mathematical models and
scenario planning
Data products, data validated actions,
increased success rate of strategic
initiatives
CLASSES OF ANALYTICAL SOLUTIONS

38. HEURISTICS
EMERGENCY
“
A man is rushed to a hospital in
the throes of a heart attack.
The nurse needs to decide
whether the victim should be
admitted into emergency care.
Although this decision can save
or cost a life, the nurse must
decide using only the available
cues, and within a few seconds
– preferably using some fancy
statistical software package.

39. HEURISTICS
EMERGENCY
Pressure < 91
Age > 62
Pulse > 100
No Yes
No Yes
No Yes

40. VISUAL ANALYTICS IS IMPERATIVE FOR
ANALYTICS → INSIGHTS → ACTION
Spot the unusual Communicate patterns Simplify decisions

41. 41
SKILLS & ROLES
THAT YOU SHOULD PICK UP

42. SO, WHAT’S THE SKILL NEEDED TO CREATE THESE?
42
Deep Domain
Expertise
Visual Design &
Presentation
Deep
Programming
Statistics & Machine
Learning
Passion for Numbers
Domain Orientation

43. …AND WHAT ARE THE ROLES AVAILABLE?
43
Deep Domain
Expertise
Visual Design &
Presentation
Deep
Programming
Statistics & Machine
Learning
Passion for Numbers
Domain Orientation
Data Scientist

44. SO, WHAT’S THE SKILL NEEDED TO CREATE THESE?
44
Deep Domain
Expertise
Visual Design &
Presentation
Deep
Programming
Statistics & Machine
Learning
Passion for Numbers
Domain Orientation
Functional Consultant

45. SO, WHAT’S THE SKILL NEEDED TO CREATE THESE?
45
Deep Domain
Expertise
Visual Design &
Presentation
Deep
Programming
Statistics & Machine
Learning
Passion for Numbers
Domain Orientation
Information Designer

46. SO, WHAT’S THE SKILL NEEDED TO CREATE THESE?
46
Deep Domain
Expertise
Visual Design &
Presentation
Deep
Programming
Statistics & Machine
Learning
Passion for Numbers
Domain Orientation
Data Analyst

47. SO, WHAT’S THE SKILL NEEDED TO CREATE THESE?
47
Deep Domain
Expertise
Visual Design &
Presentation
Deep
Programming
Statistics & Machine
Learning
Passion for Numbers
Domain Orientation
Data ScientistFunctional Consultant
Information Designer Data Analyst

48. 48
TOOLS & SOFTWARE
THAT YOU SHOULD BE LOOKING AT

49. THE DATA SCIENCE TOOLKIT
Alteryx
Amazon EC2
Azure ML
BigQuery
Birst
Caffe
Cassandra
Cloud Compute
Cloudera
Cognos
CouchDB
D3
Decision tree
ElasticSearch
Excel
Gephi
ggplot2
Hadoop
HP Vertica
IBM Watson
Impala
Julia
Jupyter Notebook
Kafka
Kibana
Kinesis
Lambda
Leaflet
Logstash
MapR
MapReduce
Matplotlib
Microstrategy
MongoDB
NodeXL
Pandas
Pentaho
Pivotal
PowerPoint
Power BI
Qlikview
R
R Studio
Random Forest
Redis
Redshift
Regression
Revolution R
S3
SAP Hana
SAS
Spark
Spotfire
SPSS
SQL Server
Stanford NLP
Storm
SVM
Tableau
TensorFlow
Teradata
Theano
Thrift
Torch
Weka
Word2Vec
The tool does not matter. A person’s skill with the tool does.
Pick the person. Let them pick the tool.

50. 50
TRAINING & COURSES
THAT WILL HELP YOU ENTER THE INDUSTRY

51. SELF-LEARNING
51
TAILORED COURSES
LEARN ON THE JOB

52. GRAMENER
CONSULTING | SERVICES | PRODUCTS
5252
GANES.KESARI@GRAMENER.COM | @KESARITWEETS