Advanced data science and analytics with Python / (Record no. 2740)

MARC details
000 -LEADER
fixed length control field 02383cam a22002898i 4500
003 - CONTROL NUMBER IDENTIFIER
control field OCoLC
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field ta
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 210519s2020 flua b 001 0 eng
010 ## - LIBRARY OF CONGRESS CONTROL NUMBER
LC control number 2019055620
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781138315068 (pbk.)
International Standard Book Number 1138315060
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)1129775792
Canceled/invalid control number (OCoLC)1129923395
050 ## - LIBRARY OF CONGRESS CALL NUMBER
Classification number QA76.9.D343
Item number R64 2020
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Rogel-Salazar, Jesus.
245 10 - TITLE STATEMENT
Title Advanced data science and analytics with Python /
Statement of responsibility, etc. Jesus Rogel-Salazar.
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Boca Raton, FL :
Name of publisher, distributor, etc. CRC Press,
Date of publication, distribution, etc. 2020.
300 ## - PHYSICAL DESCRIPTION
Extent xxxv, 383 p. :
Other physical details ill.
490 1# - SERIES STATEMENT
Series statement Chapman & Hall/CRC data mining & knowledge discovery series
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references and index.
520 ## - SUMMARY, ETC.
Summary, etc. "Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow up from the topics discuss in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered in the book. The book discusses the need to develop data products and tackles the subject of bringing models to their intended audiences. In this case literally to the users's fingertips in the form of an iPhone app"-- Provided by publisher.
650 #4 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Data mining.
Topical term or geographic name entry element Python (Computer program language)
Topical term or geographic name entry element Databases.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Chapman & Hall/CRC data mining and knowledge discovery series.
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme Library of Congress Classification
Koha item type Books
Holdings
Withdrawn status Lost status Damaged status Not for loan Permanent Location Current Location Shelving location Date acquired Full call number Barcode Date last seen Price effective from Koha item type
        Punsarn Library Punsarn Library General Stacks 17/06/2021 QA76.9.D343 R64 2020 PNLIB21062553 17/06/2021 17/06/2021 Books