Name: Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (Adaptive Computation and Machine Learning Series) (en Inglés)
SKU: 62986342
Price: 63810.00 CLP
Availability: InStock
Author: Bin Yu; Rebecca L. Barter
ISBN: 9780262049191

portada Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (Adaptive Computation and Machine Learning Series) (en Inglés)

Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (Adaptive Computation and Machine Learning Series) (en Inglés)

Formato

Libro Físico

Editorial

The Mit Press

Autor

Bin Yu; Rebecca L. Barter

Idioma

Inglés

N° páginas

526

Encuadernación

Tapa Dura

ISBN13

9780262049191

Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (Adaptive Computation and Machine Learning Series) (en Inglés)

Bin Yu; Rebecca L. Barter (Autor) · The Mit Press · Tapa Dura

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Reseña del libro "Veridical Data Science: The Practice of Responsible Data Analysis and Decision Making (Adaptive Computation and Machine Learning Series) (en Inglés)"

Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Veridical Data Science, by contrast, embraces the reality that most projects begin with an ambiguous domain question and messy data; it acknowledges that datasets are mere approximations of reality while analyses are mental constructs. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, Veridical Data Science offers a clear and actionable guide for conducting responsible data science. Requiring little background knowledge, this lucid, self-contained textbook provides a solid foundation and principled framework for future study of advanced methods in machine learning, statistics, and data science. Presents the Predictability, Computability, and Stability (PCS) methodology for producing trustworthy data-driven resultsTeaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making processCultivates critical thinking throughout the entire data science life cycleProvides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutionsSuitable for advanced undergraduate and graduate students, domain scientists, and practitioners