國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Data science and analytics with Python

Rogel-Salazar, Jesús.

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 PythonJesús 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$q(electronic bk. ;$qPDF)

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: 9781315151670$q(electronic bk. ;$qPDF)Subjects--Topical Terms:

184440
Data mining.

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

Dewey Class. No.: 006.3/12

Data science and analytics with Python
LDR:02441cmm a2200289 a 4500 001 582647
003 FlBoTFG
005 20181122172004.0
006 m o d
007 cr cnu---unuuu
008 210122s2017 flua ob 001 0 eng d
020 $a 9781315151670$q(electronic bk. ;$qPDF)
020 $z 9781498742092$q(Hardback)
035 $a 9781315151670
040 $a FlBoTFG $b eng $c FlBoTFG
041 0 $a eng
050 4 $a QA76.9 $b .D343 2017
082 0 4 $a 006.3/12 $2 23
100 1 $a Rogel-Salazar, Jesús. $3 873190
245 1 0 $a Data science and analytics with Python $h [electronic resource] / $c Jesús Rogel-Salazar.
250 $a 1st ed.
260 $a Boca Raton, FL : $b CRC Press, $c c2017.
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.
588 $a Description based on print version record.
650 0 $a Data mining. $3 184440
650 0 $a Python (Computer program language) $3 215247
650 0 $a Databases. $3 218042
830 0 $a Chapman & Hall/CRC data mining and knowledge discovery series. $3 359990
856 4 0 $u https://www.taylorfrancis.com/books/9781315151670