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8 minute intro to data science

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8 minute intro to data science

  1. 1. 8min intro to Data Science
  2. 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. 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. 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. 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. 6. What's the secret sauce ?
  7. 7. Ability to “see” patterns FASTER than competition is key to SURVIVAL !!!
  8. 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. 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. 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. 11. Why is Data Science Hot ?
  12. 12. Data Science jobs are H O T !
  13. 13. Data Science Jobs hot In India too !
  14. 14. BIG DATA HAS ENTERED BOARD ROOM GLOBALLY
  15. 15. “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
  16. 16. DATA SCIENCE = PASSPORT TO GLOBAL MARKET !
  17. 17. 1 2 3 To summarize 3 key takeaways …
  18. 18. 6 key points regarding our UNIQUE LEARNING MODEL
  19. 19. Principle-1 : Humanize Machine Learning
  20. 20. Principle-2 : 60 % Doing + 40 % Listening
  21. 21. Principle-3 : Biz Backward , instead of Technology forward !
  22. 22. Principle-4 : Playbooks + Checklists + Worksheets
  23. 23. Principle-5 : Outcome triumphs Output , ROI is key ! SegmentationROI from customers Moving to high value segments
  24. 24. 6. Repeat top 10 R commands 5 times
  25. 25. 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
  26. 26. 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 )
  27. 27. Good luck in hunting for patterns using Data Science 

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