References

Online Books

Category	Resource	Description
Explainability	Interpretable Machine Learning	A practical overview of techniques for making ML models more transparent, including SHAP.
Visualization	UW Interactive Data Lab Curriculum	Book on statistical visualization using Vega-Lite and Altair.
Visualization	Fundamentals of Data Visualization	Principles and examples of clear, effective visual communication.
Time Series	Forecasting: Principles and Practice	Covers forecasting techniques like exponential smoothing and ARIMA, with examples in R.
Data Imputation	Flexible Imputation of Missing Data	Methods to handle missing data, with emphasis on multiple imputation.
Fraud Detection	Fraud Detection Handbook	Applied techniques for detecting fraud in highly imbalanced datasets. Includes instructions on using a fraud data simulator.

Tools

• SDV - Python library for creating tabular synthetic data.
• permetrics - Python library for performance metrics of machine learning models. Documentation site includes quick explanations of each metric.

Resources used to make this guide

• Quarto: Extensive publishing system. Supports jupyter notebooks and markdown.
• bootswatch: Collection of free themes for Bootstrap-based sites.