Data Science Techniques:

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• Exploratory Data Analysis (EDA) - Creating visualizations and statistical summaries to understand data characteristics and distributions. Helps guide modeling approach.

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• Regression - Estimating relationships between a dependent and independent variables. Key techniques are linear, logistic (for binary outcomes), and polynomial regression.

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• Forecasting models - Predicting future values based on historical time series data. Common methods are ARIMA, Exponential Smoothing (ETS), and Holt-Winters seasonal forecasting.

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• Optimization algorithms - Determining optimal parameters or asset allocation to maximize desired outcomes. Includes linear programming, quadratic programming, and genetic algorithms.

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• Simulation modeling - Mimicking real world system dynamics with mathematical models to estimate performance under various scenarios. Used for forecasting and optimization.

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• Decision trees - Non-parametric models that generate rules for classification and prediction based on sequential partitioning of data. Simple interpretation.

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• Text mining/NLP - Extracting insights from unstructured text data through statistical modeling, topic modeling, document classification, and sentiment analysis.

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AI/ML Techniques:

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• Neural networks - Models inspired by biological neurons to identify complex nonlinear relationships. Includes deep learning neural nets.

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• Clustering algorithms - Grouping data points based on similarity. Key methods are K-means, density-based spatial clustering (DBSCAN), and hierarchical clustering.

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• Association rule learning - Identifying relationships between variables in large databases. Market basket analysis is a common application.

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• Ensemble models - Combining multiple models to improve prediction accuracy. Includes random forests and boosting techniques like Adaboost, XGBoost.

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• Dimensionality reduction - Reducing number of variables while retaining information. PCA and linear discriminant analysis (LDA) are main techniques.

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• Anomaly detection - Identifying outlier data points that are significantly different from expected patterns.

 

Data Analytics Techniques:

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• A/B testing - Comparing test version against a baseline by splitting sample. Used for product design and marketing.

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• Cohort analysis - Track group of users who share common characteristic over time. Valuable for customer segmentation.

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• Feature engineering - Creating new model input features by transforming existing data. Helps improve predictive capability.

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• Model validation - Assessing trained model performance on holdout dataset to prevent overfitting and estimate real world performance.

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• Hypothesis testing - Using statistical tests to determine if an effect is significant and not just due to chance. T-tests, ANOVA are examples.

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• Prediction metrics - Evaluating trained models using performance metrics like accuracy, AUC, R-squared, root mean squared error, etc.

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• Multivariate analysis - Simultaneously analysing multiple variables to uncover relationships. Key methods are regression analysis, MANOVA, and factor analysis.

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• Model optimization - Tuning model hyperparameters and architecture to improve performance by reducing bias and variance.