Lingua: Inglese
Editore: Chapman and Hall/CRC (edition 1), 2023
ISBN 10: 103234170X ISBN 13: 9781032341705
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Lingua: Inglese
Editore: Chapman and Hall/CRC (edition 1), 2023
ISBN 10: 103234170X ISBN 13: 9781032341705
Da: BooksRun, Philadelphia, PA, U.S.A.
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Lingua: Inglese
Editore: TAYLOR & FRANCIS NP EXCLUSIVE(CBS), 2024
ISBN 10: 103234170X ISBN 13: 9781032341705
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Aggiungi al carrelloCondizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Jerry Bonnell is a Ph.D. candidate in Computer Science at the University of Miami, and a University of Miami Fellow. His research areas include Machine Learning and Natural Language Processing. His research targets domain experts in Digital Humanities (D.
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. Exploring Data Science with R and the Tidyverse | A Concise Introduction | Jerry Bonnell (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2023 | Chapman and Hall/CRC | EAN 9781032341705 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
Da: AHA-BUCH GmbH, Einbeck, Germania
EUR 189,57
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Aggiungi al carrelloTaschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book introduces the reader to data science using R and the tidyverse. No prerequisite knowledge is needed in college-level programming or mathematics (e.g., calculus or statistics). The book is self-contained so readers can immediately begin building data science workflows without needing to reference extensive amounts of external resources for onboarding. The contents are targeted for undergraduate students but are equally applicable to students at the graduate level and beyond. The book develops concepts using many real-world examples to motivate the reader.Upon completion of the text, the reader will be able to:Gain proficiency in R programmingLoad and manipulate data frames, and 'tidy' them using tidyverse toolsConduct statistical analyses and draw meaningful inferences from themPerform modeling from numerical and textual dataGenerate data visualizations (numerical and spatial) using ggplot2 and understand what is being representedAn accompanying R package 'edsdata' contains synthetic and real datasets used by the textbook and is meant to be used for further practice. An exercise set is made available and designed for compatibility with automated grading tools for instructor use.