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

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