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

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Sebastopol, CA : O'Reilly Media, Inc., 2020.Edition: First edition.Description: xvi, 342 pages : illustrations, charts, tables (black and white) ; 24 cm.ISBN:
  • 9781492072942
Subject(s): LOC classification:
  • QA276.4 .B78 2020
Contents:
Exploratory data analysis — Data and sampling distributions — Statistical experiments and significance testing — Regression and prediction — Classification — Statistical machine learning — Unsupervised learning.
Courses that have reserved this title:
  • B3 Functional Competences: Data Management and Visualization
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 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.
Holdings
Item type Current library Collection Call number Status Date due Barcode
Book TBS Barcelona Libre acceso Core Textbooks QA276.4 BRU (Browse shelf(Opens below)) Available B04191

Includes bibliographical references (pages 327-342) and index.

Exploratory data analysis — Data and sampling distributions — Statistical experiments and significance testing — Regression and prediction — Classification — Statistical machine learning — Unsupervised learning.

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.

B3 Functional Competences: Data Management and Visualization

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