Practical statistics for data scientists : 50 essential concepts / Peter Bruce and Andrew Bruce.

By:

Bruce, Peter C, 1953- [author.]

Contributor(s):

Bruce, Andrew, 1958- [author.]

Material type: Text

TextLanguage: English Publisher: Sebastopol, CA : O'Reilly, 2017Edition: First edition.Description: xvi, 298 pages : illustrations ; 24 cmISBN:

9781492072942

Subject(s):

DDC classification:

001.4/22 23

LOC classification:

QA276.4 .B78 2017

Contents:

Exploratory Data Analysis Data and Sampling Distributions--Statistical Experiments and Significance Testing — Regression and Prediction — Classification — Statistical Machine Learning — Unsupervised Learning.

Summary: 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.

Holdings ( 1 )
Title notes ( 3 )

Holdings
Item type	Current library	Call number	Status	Date due	Barcode
Recommended bibliography book	TBS Barcelona Libre acceso	QA276.4 BRU (Browse shelf(Opens below))	Available		B04191

Includes bibliographical references (pages 285-286) and index.

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.