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45 Analytic Techniques Used by Data Scientists

Saurabh Jain

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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


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