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Data science and analytics with Python

Rogel-Salazar, Jesús.

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Data science and analytics with Python

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data science and analytics with Python/ Jesús Rogel-Salazar.
作者:	Rogel-Salazar, Jesús.
出版者:	Boca Raton, FL :CRC Press, : c2017.,
面頁冊數:	1 online resource (400 p.) :ill.
附註:	"A Chapman & Hall book"--Title page.
標題:	Data mining. -
電子資源:	https://www.taylorfrancis.com/books/9781315151670
ISBN:	9781315151670

Data science and analytics with Python
Rogel-Salazar, Jesús.

Data science and analytics with Python[electronic resource] /Jesús Rogel-Salazar. - 1st ed. - Boca Raton, FL :CRC Press,c2017. - 1 online resource (400 p.) :ill. - Chapman & Hall/CRC data mining and knowledge discovery series. - Chapman & Hall/CRC data mining and knowledge discovery series..

"A Chapman & Hall book"--Title page.

Includes bibliographical references (p. 361-368) and index.

Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book.

ISBN: 9781315151670Subjects--Topical Terms:

562972
Data mining.

LC Class. No.: QA76.9 / .D343 2017

Dewey Class. No.: 006.3/12

Data science and analytics with Python
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300 $a 1 online resource (400 p.) : $b ill.
490 1 $a Chapman & Hall/CRC data mining and knowledge discovery series
500 $a "A Chapman & Hall book"--Title page.
504 $a Includes bibliographical references (p. 361-368) and index.
520 3 $a Data Science and Analytics with Python is designed for practitioners in data science and data analytics in both academic and business environments. The aim is to present the reader with the main concepts used in data science using tools developed in Python, such as SciKit-learn, Pandas, Numpy, and others. The use of Python is of particular interest, given its recent popularity in the data science community. The book can be used by seasoned programmers and newcomers alike. The book is organized in a way that individual chapters are sufficiently independent from each other so that the reader is comfortable using the contents as a reference. The book discusses what data science and analytics are, from the point of view of the process and results obtained. Important features of Python are also covered, including a Python primer. The basic elements of machine learning, pattern recognition, and artificial intelligence that underpin the algorithms and implementations used in the rest of the book also appear in the first part of the book. Regression analysis using Python, clustering techniques, and classification algorithms are covered in the second part of the book. Hierarchical clustering, decision trees, and ensemble techniques are also explored, along with dimensionality reduction techniques and recommendation systems. The support vector machine algorithm and the Kernel trick are discussed in the last part of the book.
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