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Book synopsis

This book provides a practical introduction to data science as decision-making under uncertainty. It shows how to move beyond point estimates and accuracy scores toward workflows that quantify, evaluate, and communicate uncertainty in real data problems. Readers learn probability as a language for data, simulation-based thinking, regression and predictive modeling, resampling and regularization, probabilistic classification, calibration, prediction intervals, Bayesian modeling, conformal prediction, causal questions, decision analysis, robustness under distribution shift, and responsible communication. With Python-oriented examples and end-to-end case studies, this book helps readers build models whose limitations are explicit and whose predictions can support better decisions.

Book details

• Full title: Data Science Under Uncertainty
• Subtitle: Inference, Prediction, and Decision-Making with Python.DE
• Author: José Unpingco
• Language: English
• Binding: Hardback
• EAN: 9783032430137
• ID: 54021325
• Publisher: Springer, Berlin
• Dimensions: 235 × 155 mm
• Date of publishing: 18. March 2027

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