Below are the mostly used techniques in Data Science:
- 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
