This document provides an overview of data science and its applications. It discusses: 1) Industries that are being disrupted by data science like telecom, banking, retail, and healthcare. 2) How companies like Amazon, Netflix, and Google were able to disrupt their industries through their ability to analyze patterns in data faster than competitors. 3) The factors driving more companies to adopt data science including competitive advantages, revenue growth, and cost optimization.

1. 8min intro
to
Data Science

2. Key takeaways of Data Science
• An overview of the shift to Data Science Platforms
• The 2 critical components of a Data Science platform
• Industries that are most likely to get disrupted and shift to Data Science
• Characteristics of firms that get left behind the Data Science wave
• Factors that push an industry towards Data Science
• A brief overview of aspects of platform architecture beyond Hadoop
• What's in it for you ? How can an individual intercept this massive new trend ?

3. What's common to the following ?
1
2
3
4 5
Japanese dating app
Sensored cows in Netherland Googles autonomous car
MOOC
Heart implants

4. The world around is changing …
• How our health gets cared for ?
• How we learn ?
• How we fall in love ?
• How we do farming ?
• How we drive ?
They all have Data Science embedded in them
(an intimate fabric of our lives)

5. How did the following players disrupt the Marketplace ?
• Amazon Defeated Borders ( Books )
• Netflix Defeated Blockbuster ( Video )
• iTunes Defeated Tower records ( Music )
• Google defeated Yahoo ( Search ) – Page rank
algorithm

6. What's the secret sauce ?

7. Ability to “see” patterns FASTER than competition is key to SURVIVAL !!!

8. Industries disrupted by Data Science
• Telecom ( Infrastructure optimisation, Network security )
• Banking ( Customer sentiment, Multi channel analysis )
• Digital channel ( Consumer engagement, Recommendation engines )
• Automotive ( Autonomous cards, Fords OnStar )
• Health care (Wearables )
• Oil n Gas ( Operations optimisation )
• Retail ( Digitisation )

9. What factors are driving companies towards data science ?
• Competitive advantage in the market place ( get ahead fast using unique insights )
• Existential threat ( others are moving ahead fast and I need to catch up )
• Revenue enhancement ( Cross sell models, recommenders )
• Cost optimisation ( Operational efficiency )

10. Overview of Data Science Platform ?
• Store massive torrents of data
• Billions of events
• Petabytes of data
• Emphasis on how to handle data
• Hadoop
• Cloudera
• Infobright
• Splunk
• Sense patterns from data for competitive advantage
• Emphasis on seeing patterns
• Algorithms
• Clustering
• Advanced visualisation
• Text mining

11. Why is Data Science Hot ?

12. Data Science jobs are H O T !

13. Data Science Jobs hot In India too !

18. BIG DATA HAS ENTERED BOARD ROOM GLOBALLY

19. “By 2018, the United States alone could
face a shortage of 140,000 to 190,000
people with deep analytical skills as well
as 1.5 million managers and analytics
with the know-how to use the analysis
of big data to make effective decisions”
McKinsey & Company: Big Data: The next frontier for competition

20. DATA SCIENCE = PASSPORT TO GLOBAL MARKET !

24. 1
2
3
To summarize
3 key takeaways …

25. 6
key points regarding our UNIQUE LEARNING MODEL

26. Principle-1 : Humanize Machine Learning

27. Principle-2 : 60 % Doing + 40 % Listening

28. Principle-3 : Biz Backward , instead of Technology
forward !

29. Principle-4 : Playbooks + Checklists +
Worksheets

30. Principle-5 : Outcome triumphs Output , ROI is key !
SegmentationROI from customers
Moving to high value
segments

31. 6. Repeat top 10 R commands 5 times

32. What you would learn at the end of 4 weeks ?
15 Core Foundational Building Blocks for next generation job market
PREDICTIVE
SCORING
MODELING
DEMYSTIFYING
MACHINE
LEARNING
CORRELATIO
N DETECTION
ADVANCED
VISUALISATION
VOLATILITY
ANALYTICS
CLUSTERING
FEATURE
EXTRACTION
OUTLIER
EXPLORATION
BOX PLOTS
SCATTER
PLOTS
UNIVARIATE
ANALYSIS
EXPLORATORY
DATA ANALYSIS
REGRESSION
MODELING
BUSINESS USE
CASES OF ML
REFRERENCE
ARCHITECTURE

33. 4 Week Data Science Boot camp
Week by week plan
Week-1
Week-2
Week-3
Week-4
 Demystifying Data Science
 Introduction to Machine learning techniques
 Step by Step methodology for converting noise to signal
 12 tools of a Data Scientist
 Descriptive vs Prescriptive statistics
 How to do EDA ( Exploratory Data Analysis ) –Univariate / Bivariate / Corrrelations
 Advanced Visualisation techniques
 Data Science Lab Session-2 : Hands on Univariate + Bivariate + Correlation Analytics
 Data Science Lab Session-1 : Getting feet wet in Data Science tools
 Introduction to segmentation and clustering techniques
 Segmentation in Retail Industry
 Segmentation in Telecom industry
 Segmentation in Healthcare industry
 How to present for maximising Segmentation Business Impact
 Data Science Lab Session-3 : Hands on SEGMENTATION on live data
 Demystifying Predictive Analytical Models ( PAM )
 Predictive Analytical Models in Retail Industry
 Predictive Analytical Models in Telecom industry
 Predictive Analytical Models in Healthcare industry
 Mapping Impact of Predictive models on Business Outcomes
 Summary of Key Data Science concepts
 Data Science Lab Session-4 : Hands on PREDICTIVE ANALYTICS on live data
 END 2 END MACHINE LEARNING PROJECT on
Live data ( Telecom or Retail or Banking )

38. Good luck in hunting for patterns using Data Science 