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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. When you click on any of the 40 links below, you will find a selection of articles related to the entry in question. Most of these articles are hard to find with a Google search, so in some ways this gives you access to the hidden literature on data science, machine learning, and statistical science. Many of these articles are fundamental to understanding the technique in question, and come with further references and source code.

Starred techniques (marked with a *) belong to what I call deep data science, a branch of data science that has little if any overlap with closely related fields such as machine learning, computer science, operations research, mathematics, or statistics. Even classical machine learning and statistical techniques such as clustering, density estimation,  or tests of hypotheses, have model-free, data-driven, robust versions designed for automated processing (as in machine-to-machine communications), and thus also belong to deep data science. However, these techniques are not starred here, as the standard versions of these techniques are more well known (and unfortunately more used) than the deep data science equivalent.

To learn more about deep data science,  click here. Note that unlike deep learning, deep data science is not the intersection of data science and artificial intelligence; however, the analogy between deep data science and deep learning is not completely meaningless, in the sense that both deal with automation.

Also, to discover in which contexts and applications the 40 techniques below are used, I invite you to read the following articles:

Finally, when using a technique, you need to test its performance. Read this article about 11 Important Model Evaluation Techniques Everyone Should Know.

The 40 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

The number of techniques is higher than 40 because we updated the article, and added additional ones.

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Comment by Vinay MH on July 8, 2017 at 10:20am

Thanks for the summary, helpful for me in getting started. 

Comment by Amitabh Das on July 7, 2016 at 2:25am
Nicely summarized in this article the entire world of Applied Statistics. Great work

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