Amazon cover image
Image from Amazon.com

Practical statistics for data scientists : 50+ essential concepts using R and Python / Peter Bruce, Andrew Bruce, and Peter Gedeck.

By: Contributor(s): Publisher: Sebastopol, CA : O'Reilly Media, Inc., 2020Copyright date: ©2020Edition: Second editionDescription: xvi, 342 pages : illustrations ; 24 cmContent type:
  • text
Media type:
  • unmediated
Carrier type:
  • volume
ISBN:
  • 9781492072942
  • 149207294X
Subject(s): DDC classification:
  • 001.4/22 23
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.
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.
List(s) this item appears in: 2020 New Titles | Learning Python | Data Analysis
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
BOOK BOOK NCAR Library Mesa Lab QA276.4 .B78 2020 1 Available 50583020009274
Total holds: 0

Includes bibliographical references 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.

Questions? Email library@ucar.edu.

Not finding what you are looking for? InterLibrary Loan.