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

� propos des auteurs:

Peter Bruce is the Founder and Chief Academic Officer of the Institute for Statistics Education at Statistics.com, which offers about 80 courses in statistics and analytics, roughly half of which are aimed at data scientists. He has authored or co-authored several books in statistics and analytics, and he earned his Bachelor's degree at Princeton, and Masters degrees at Harvard and the University of Maryland.

Andrew Bruce, Principal Research Scientist at Amazon, has over 30 years of experience in statistics and data science in academia, government and business. The co-author of Applied Wavelet Analysis with S-PLUS, he earned his bachelor's degree at Princeton, and PhD in statistics at the University of Washington

Peter Gedeck, Senior Data Scientist at Collaborative Drug Discovery, specializes in the development of machine learning algorithms to predict biological and physicochemical properties of drug candidates. Co-author of Data Mining for Business Analytics, he earned PhD's in Chemistry from the University of Erlangen-N�rnberg in Germany and Mathematics from Fernuniversit�t Hagen, Germany

Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

Description de la librairie

Visitez la page d�accueil du vendeur

Informations commerciales relatives au vendeur

Edward Bowditch Ltd
Exstowe, Exton, Exeter, EX3 0PP, United Kingdom

Conditions de vente :

Legal entity name: Edward Bowditch Ltd
Legal entity form: Limited company
Business correspondence address: Exstowe, Exton, Exeter, EX3 0PP
Company registration number: 04916632
VAT registration: GB834241546
Authorised representative: Mr. E. Bowditch

Conditions de livraison :

Orders usually dispatched within two working days.

Modes de paiement
accept�s par le vendeur

Ajouter aux demandes de livres

Articles li�s � Practical Statistics for Data Scientists: 50+ Essential...

Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Bruce, Peter/ Bruce, Andrew/ Gedeck, Peter

Neuf(s) - Couverture souple

Informations commerciales relatives au vendeur