{ "item_title" : "Data Science Under Uncertainty", "item_author" : [" José Unpingco "], "item_description" : "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. Bridges statistical foundations and modern machine learning with calibration, conformal prediction, and robustness Integrates Python-based uncertainty quantification with actionable decision analysis and communication Provides a practical introduction to data science as decision-making under uncertainty ", "item_img_path" : "https://covers2.booksamillion.com/covers/bam/3/03/243/013/3032430135_b.jpg", "price_data" : { "retail_price" : "109.99", "online_price" : "109.99", "our_price" : "109.99", "club_price" : "109.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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