Veridical Data Science | The Practice of Responsible Data Analysis and Decision Making
Lingua: inglese
Editore: MIT Press Ltd, 2024
- Rilegato
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Veridical Data Science | The Practice of Responsible Data Analysis and Decision Making | Bin Yu (u. a.) | Buch | Einband - fest (Hardcover) | Englisch | 2024 | MIT Press Ltd | EAN 9780262049191 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu.
Codice articolo 129350230
- Titolo
- Veridical Data Science | The Practice of Responsible Data Analysis and Decision Making
- Autore
- Bin Yu (u. a.)
- Editore
- MIT Press Ltd
- Anno di pubblicazione
- 2024
- Condizione
- Neu
- Rilegatura
- Buch
- Lingua
- inglese
- ISBN 10
- 0262049198
- ISBN 13
- 9780262049191
- Peso dell'articolo
- 962 grammi
- Dimensioni
- 232 x 157 x 37 mm
- Cataloghi dei venditori
- Bücher
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 results
- Teaches how a data science project should be conducted from beginning to end, including extensive discussion of the data scientist's decision-making process
- Cultivates critical thinking throughout the entire data science life cycle
- Provides practical examples and illuminating case studies of real-world data analysis problems with associated code, exercises, and solutions
- Suitable for advanced undergraduate and graduate students, domain scientists, and practitioners
"Riassunto" può appartenere a un’altra edizione di questo titolo.
Informazioni sull’autore
Rebecca L. Barter is Research Assistant Professor in Epidemiology at the University of Utah.
"Descrizione articolo" può appartenere a un’altra edizione di questo titolo.
preigu
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Venditore AbeBooks dal 5 agosto 2024
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| Articolo | Da 60 a 60 giorni lavorativi | Da 60 a 60 giorni lavorativi |
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