Practical statistics for data scientists : 50+ essential concepts using R and Python / Peter Bruce, Andrew Bruce, and Peter Gedeck.
Material type:
TextLanguage: English Publication details: Sebastopol, CA ; Mumbai : 2020 [Indian reprint 2021]Edition: Second editionDescription: xvi, 342 pages : illustrations ; 24 cmISBN: 9788194435006Subject(s): Mathematical analysis -- Statistical methods | Quantitative research -- Statistical methods | R (Computer program language) | Python (Computer program language) | Statistics -- Data processing | Python (Computer program language) | R (Computer program language) | Statistics -- Data processingDDC classification: 001.422 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.--
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Central Library, University of Rajshahi Reading Room | Non-fiction | 001.422 BRP 2021 (Browse shelf(Opens below)) | C-1 | Not For Loan | BDT | B58416 | |
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Central Library, University of Rajshahi General Stacks | Non-fiction | 001.422 BRP 2021 (Browse shelf(Opens below)) | C-2 | Available | BDT | B58417 |
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| 001.42 ZAM 1973 Metatheory and consumer research | 001.422 BRP 2021 Practical statistics for data scientists : 50+ essential concepts using R and Python / | 004.165 LIM 1996 Microcomputer systems : the 8086/8088 family : architecture, programming, and design / | 004.65 PEC 2022 Computer networks : a systems approach / | 005.1 CLE 2008 Clean code : a handbook of agile software craftsmanship / |
Includes bibliographical references (pages 327-328) 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 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.-- Source other than the Library of Congress.
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