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 scienceequivalent.

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:

21 data science systems used by Amazon to operate its business

24 Uses of Statistical Modeling

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

Linear Regression

Logistic Regression

Jackknife Regression *

Density Estimation

Confidence Interval

Test of Hypotheses

Pattern Recognition

Clustering - (aka Unsupervised Learning)

Supervised Learning

Time Series

Decision Trees

Random Numbers

Monte-Carlo Simulation

Bayesian Statistics

Naive Bayes

Principal Component Analysis - (PCA)

Ensembles

Neural Networks

Support Vector Machine - (SVM)

Nearest Neighbors - (k-NN)

Feature Selection - (aka Variable Reduction)

Indexation / Cataloguing *

(Geo-) Spatial Modeling

Recommendation Engine *

Search Engine *

Attribution Modeling *

Collaborative Filtering *

Rule System

Linkage Analysis

Association Rules

Scoring Engine

Segmentation

Predictive Modeling

Graphs

Deep Learning

Game Theory

Imputation

Survival Analysis

Arbitrage

Lift Modeling

Yield Optimization

Cross-Validation

Model Fitting

Relevancy Algorithm *

Experimental Design

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

Some opinions expressed in this article may be those of a guest author and not necessarily Analytikus. Staff authors are listed in https://www.datasciencecentral.com/profiles/blogs/40-techniques-used-by-data-scientists