國立虎尾科技大學 |

Practitioner's guide to data science

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Practitioner's guide to data science/ Hui Lin, Ming Li.
Author:	Lin, Hui.
other author:	Li, Ming.
Published:	Boca Raton, FL :Chapman & Hall/CRC Press, : 2023.,
Description:	1 online resource.
Subject:	Big data. -
Online resource:	https://www.taylorfrancis.com/books/9781351132916
ISBN:	9781351132916

Practitioner's guide to data science
Lin, Hui.

Practitioner's guide to data science[electronic resource] /Hui Lin, Ming Li. - 1st ed. - Boca Raton, FL :Chapman & Hall/CRC Press,2023. - 1 online resource. - Chapman & Hall/CRC data science series. - Chapman & Hall/CRC data science series..

Includes bibliographical references and index.

Soft skills for data scientists -- Introduction to the data -- Big data cloud platform -- Data pre-processing -- Data wrangling -- Model tuning strategy -- Measuring performance -- Regression models -- Regularization methods -- Tree-based methods -- Deep learning.

"This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes"--

ISBN: 9781351132916Subjects--Topical Terms:

981821
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Practitioner's guide to data science
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245 1 0 $a Practitioner's guide to data science $h [electronic resource] / $c Hui Lin, Ming Li.
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504 $a Includes bibliographical references and index.
505 0 $a Soft skills for data scientists -- Introduction to the data -- Big data cloud platform -- Data pre-processing -- Data wrangling -- Model tuning strategy -- Measuring performance -- Regression models -- Regularization methods -- Tree-based methods -- Deep learning.
520 $a "This book aims to increase the visibility of data science in real-world, which differs from what you learn from a typical textbook. Many aspects of day-to-day data science work are almost absent from conventional statistics, machine learning, and data science curriculum. Yet these activities account for a considerable share of the time and effort for data professionals in the industry. Based on industry experience, this book outlines real-world scenarios and discusses pitfalls that data science practitioners should avoid. It also covers the big data cloud platform and the art of data science, such as soft skills. The authors use R as the primary tool and provide code for both R and Python. This book is for readers who want to explore possible career paths and eventually become data scientists. This book comprehensively introduces various data science fields, soft and programming skills in data science projects, and potential career paths. Traditional data-related practitioners such as statisticians, business analysts, and data analysts will find this book helpful in expanding their skills for future data science careers. Undergraduate and graduate students from analytics-related areas will find this book beneficial to learn real-world data science applications. Non-mathematical readers will appreciate the reproducibility of the companion R and python codes"-- $c Provided by publisher.
588 $a OCLC-licensed vendor bibliographic record.
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