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Erstausgabe
Soft cover. Zustand: As New. 1st Edition. Photo is the book you will receive. In excellent condition with nominal wear, a tight binding and pristine text. Suitable as a gift.
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Sprache: Englisch
Verlag: O'Reilly Media, Incorporated, 2020
ISBN 10: 149207294X ISBN 13: 9781492072942
Anbieter: Better World Books, Mishawaka, IN, USA
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Sprache: Englisch
Verlag: O'Reilly Media, Incorporated, 2020
ISBN 10: 149207294X ISBN 13: 9781492072942
Anbieter: Better World Books, Mishawaka, IN, USA
Zustand: Good. Pages intact with minimal writing/highlighting. The binding may be loose and creased. Dust jackets/supplements are not included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.
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paperback. Zustand: Very Good.
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Anbieter: GoldBooks, Denver, CO, USA
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Anbieter: GreatBookPricesUK, Woodford Green, Vereinigtes Königreich
EUR 38,10
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In den WarenkorbZustand: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.
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In den WarenkorbPaperback. Zustand: USED BOOKS. USED BOOKS ! Fast Delivery This is an International Edition and ship within 24-48 hours. Deliver by FedEx and Dhl, & Aramex, UPS, & USPS and we do accept APO and PO BOX Addresses. Order can be delivered worldwide within 6-10 days and we do have flat rate for up to 2LB. Extra shipping charges will be requested if the Book weight is more than 5 LB. This Item May be shipped from India, United states & United Kingdom. Depending on your location and availability.
Anbieter: medimops, Berlin, Deutschland
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Anbieter: medimops, Berlin, Deutschland
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Anbieter: GoldBooks, Denver, CO, USA
Paperback. Zustand: new. New Copy. Customer Service Guaranteed.
Anbieter: WorldofBooks, Goring-By-Sea, WS, Vereinigtes Königreich
EUR 65,70
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In den WarenkorbPaperback. Zustand: Good. The book has been read but remains in clean condition. All pages are intact and the cover is intact. Some minor wear to the spine.
Anbieter: WorldofBooks, Goring-By-Sea, WS, Vereinigtes Königreich
EUR 65,70
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In den WarenkorbPaperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.
Sprache: Englisch
Verlag: O'Reilly Media, Sebastopol, 2020
ISBN 10: 149207294X ISBN 13: 9781492072942
Anbieter: Grand Eagle Retail, Bensenville, IL, USA
Paperback. Zustand: new. Paperback. Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that ""learn"" from data Unsupervised learning methods for extracting meaning from unlabeled data" Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on whats important and whats not Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 82,09
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 1st edition. 298 pages. 9.00x6.75x0.50 inches. In Stock.
Anbieter: moluna, Greven, Deutschland
Zustand: New. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells .
Anbieter: Revaluation Books, Exeter, Vereinigtes Königreich
EUR 94,43
Anzahl: 1 verfügbar
In den WarenkorbPaperback. Zustand: Brand New. 2nd edition. 342 pages. 9.00x7.00x0.75 inches. In Stock.
Anbieter: preigu, Osnabrück, Deutschland
Taschenbuch. Zustand: Neu. Practical Statistics for Data Scientists, 2e | 50+ Essential Concepts Using R and Python | Peter Bruce (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2020 | O'Reilly Media | EAN 9781492072942 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.
Sprache: Englisch
Verlag: O'Reilly Media, Sebastopol, 2020
ISBN 10: 149207294X ISBN 13: 9781492072942
Anbieter: AussieBookSeller, Truganina, VIC, Australien
Paperback. Zustand: new. Paperback. Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher-quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that ""learn"" from data Unsupervised learning methods for extracting meaning from unlabeled data" Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on whats important and whats not Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.
Taschenbuch. Zustand: Neu. Neuware - Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this practical guide-now including examples in Python as well as R-explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data scientists use statistical methods but lack a deeper statistical perspective. If you're familiar with the R or Python programming languages, and have had some exposure to statistics but want to learn more, this quick reference bridges the gap in an accessible, readable format. With this updated edition, you'll dive into: Exploratory data analysis Data and sampling distributions Statistical experiments and significance testing Regression and prediction Classification Statistical machine learning Unsupervised learning.