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Practical statistics for data scientists : 50 essential concepts /
"Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Sebastopol, CA :
O'Reilly Media, Inc.,
[2017]
|
| Edición: | First edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 100 | 1 | |a Bruce, Peter C., |d 1953- |e author. | |
| 245 | 1 | 0 | |a Practical statistics for data scientists : |b 50 essential concepts / |c Peter Bruce and Andrew Bruce. |
| 246 | 3 | 0 | |a 50 essential concepts |
| 246 | 3 | |a Fifty essential concepts | |
| 250 | |a First edition. | ||
| 264 | 1 | |a Sebastopol, CA : |b O'Reilly Media, Inc., |c [2017] | |
| 264 | 4 | |c ©2017 | |
| 300 | |a 1 online resource (298 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references and index. | ||
| 505 | 0 | |a Exploratory data analysis -- Data and sampling distributions -- Statistical experiments and significance testing -- Regression and prediction -- Classification -- Statistical machine learning -- Unsupervised learning. | |
| 520 | |a "Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide 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 science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, 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"--Provided by publisher. | ||
| 588 | 0 | |a Print version record. | |
| 590 | |a O'Reilly |b O'Reilly Online Learning: Academic/Public Library Edition | ||
| 650 | 0 | |a Mathematical analysis |x Statistical methods. | |
| 650 | 0 | |a Quantitative research |x Statistical methods. | |
| 650 | 0 | |a Big data |x Mathematics. | |
| 650 | 6 | |a Analyse mathématique |x Méthodes statistiques. | |
| 650 | 6 | |a Recherche quantitative |x Méthodes statistiques. | |
| 650 | 6 | |a Données volumineuses |x Mathématiques. | |
| 650 | 7 | |a REFERENCE |x Questions & Answers. |2 bisacsh | |
| 650 | 7 | |a Statistics. |2 fast |0 (OCoLC)fst01132103 | |
| 650 | 7 | |a Statistics |x Data processing. |2 fast |0 (OCoLC)fst01132113 | |
| 650 | 7 | |a Data Mining |2 gnd | |
| 650 | 7 | |a Datenanalyse |2 gnd | |
| 650 | 7 | |a Statistik |2 gnd | |
| 700 | 1 | |a Bruce, Andrew, |d 1958- |e author. | |
| 776 | 0 | 8 | |i Print version: |a Bruce, Peter C., 1953- |t Practical statistics for data scientists. |b First edition. |d Sebastopol, CA : O'Reilly Media, Inc., [2017] |z 9781491952962 |w (OCoLC)936003166 |
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