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    1. Do you have some or the other reason to not being able to do something? Or to keep postponing? Check yourself of excusitis which the successful people refrain themselves from!!

       

    2. What is variant data type? What is the difference between a variant array and an array of variants ?

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      These techniques cover most of what data scientists and related practitioners are using in their daily activities, whether they use solutions offered by a vendor, or whether they design proprietary tools

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      The 45 data science techniques

       

      1. Linear Regression 
      2. Logistic Regression 
      3. Jackknife Regression *
      4. Density Estimation 
      5. Confidence Interval 
      6. Test of Hypotheses 
      7. Pattern Recognition 
      8. Clustering - (aka Unsupervised Learning)
      9. Supervised Learning 
      10. Time Series 
      11. Decision Trees 
      12. Random Numbers 
      13. Monte-Carlo Simulation 
      14. Bayesian Statistics 
      15. Naive Bayes 
      16. Principal Component Analysis - (PCA)
      17. Ensembles 
      18. Neural Networks 
      19. Support Vector Machine - (SVM)
      20. Nearest Neighbors - (k-NN)
      21. Feature Selection - (aka Variable Reduction)
      22. Indexation / Cataloguing *
      23. (Geo-) Spatial Modeling 
      24. Recommendation Engine *
      25. Search Engine *
      26. Attribution Modeling *
      27. Collaborative Filtering *
      28. Rule System 
      29. Linkage Analysis 
      30. Association Rules 
      31. Scoring Engine 
      32. Segmentation 
      33. Predictive Modeling 
      34. Graphs 
      35. Deep Learning 
      36. Game Theory 
      37. Imputation 
      38. Survival Analysis 
      39. Arbitrage 
      40. Lift Modeling 
      41. Yield Optimization
      42. Cross-Validation
      43. Model Fitting
      44. Relevancy Algorithm *
      45. Experimental Design
    3. What is IOT concept & how do you relate it with business intelligence & analytics.

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