References

Online Books & Educational Tools

Category	Resource	Description
Modeling	An Introduction to Statistical Learning	Statistical and machine learning approaches to learning from data. Includes companion website for Python examples.
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: R, Python	Comprehensive introduction to forecasting methods. Includes like exponential smoothing and ARIMA. Versions for R and Python available.
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.
Statistics & Probability	OpenIntro Statistics	Introduction to statistics and probability. Also includes links to YouTube videos explaining the concepts.
Statistics & Probability	SeeingTheory	Visual introduction to statistics and probability.

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.