8 minute intro to data science

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 