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|a 9784873119267
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| 050 |
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4 |
|a QA276.4
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| 100 |
1 |
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|a Bruce, Peter C.
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| 130 |
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|a Practical statistics for data scientists
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| 245 |
0 |
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|a Dēta saiensu no tame no tōkeigaku nyūmon
|b yosoku, bunrui, tōkei moderingu, tōkeiteki kikai gakushū to R/Python puroguramingu
|c Peter Bruce, Andrew Bruce, Peter Gedeck cho ; Kurokawa Toshiaki ; Ōhashi Shin'ya gijutsu kanshū = Practical statistics for data scientists : 50+ essential concepts using R and Python / Peter Bruce, Andrew Bruce, and Peter Gedeck
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| 246 |
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1 |
|a Practical statistics for data scientists
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| 250 |
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|a Dai 2-han
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| 260 |
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|a Tōkyō-to Shinjuku-ku
|b Orairī Japan
|c 2020
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| 300 |
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|a 396 pages
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| 505 |
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|a Includes bibliographical references (pages 345-349) and index
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| 653 |
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|a R (Langage de programmation)
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| 653 |
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|a Python (Computer program language) / fast
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| 653 |
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|a Python (Computer program language) / http://id.loc.gov/authorities/subjects/sh96008834
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| 653 |
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|a Mathematical analysis / Statistical methods / fast
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| 653 |
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|a R (Computer program language) / fast
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| 653 |
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|a Recherche quantitative / Méthodes statistiques
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| 653 |
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|a Quantitative research / Statistical methods
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| 653 |
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|a Quantitative research / Statistical methods / fast
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| 653 |
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|a Statistique / Informatique
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| 653 |
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|a Analyse mathématique / Méthodes statistiques
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| 653 |
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|a Statistics / Data processing / fast
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| 653 |
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|a R (Computer program language) / http://id.loc.gov/authorities/subjects/sh2002004407
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| 653 |
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|a Python (Langage de programmation)
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| 653 |
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|a Statistics / Data processing / http://id.loc.gov/authorities/subjects/sh85127583
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| 653 |
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|a Mathematical analysis / Statistical methods
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| 700 |
1 |
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|a Bruce, Andrew
|e author
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| 700 |
1 |
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|a Gedeck, Peter
|e author
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| 700 |
1 |
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|a Kurokawa, Toshiaki
|e translator
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| 041 |
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|a jpn
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 9784873119267
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| 776 |
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|z 487311926X
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| 856 |
4 |
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|u https://learning.oreilly.com/library/view/~/9784873119267/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 001.42
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| 082 |
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|a 519.5
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| 082 |
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|a 001.4/22
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| 520 |
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|a "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." --
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