MARC details
| 000 -LEADER |
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| fixed length control field |
02059 a2200253 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
fcuc |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20250417150519.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
250417b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9781009318259 |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
fcuc |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
005.7 TRI 2024 |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| 9 (RLIN) |
673 |
| Personal name |
Triguero, Isaac |
| Relator term |
author. |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Large-scale data analytics with phyton and spark : |
| Remainder of title |
a hands-on guide to implementing machine learning solutions : |
| Statement of responsibility, etc. |
Isaac Triguero and Mikel Galar. |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Cambridge, United Kingdom ; New York, NY : |
| Name of publisher, distributor, etc. |
Cambridge University Press, |
| Date of publication, distribution, etc. |
2024. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xvi, 378 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
25 cm. |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc. note |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc. |
"Based on the authors' extensive teaching experience, this hands-on graduate-level textbook teaches how to carry out large-scale data analytics and design machine learning solutions for big data. With a focus on fundamentals, this extensively class-tested textbook walks students through key principles and paradigms for working with large-scale data, frameworks for large-scale data analytics (Hadoop, Spark), and explains how to implement machine learning to exploit big data. It is unique in covering the principles that aspiring data scientists need to know, without detail that can overwhelm. Real-world examples, hands-on coding exercises and labs combine with exceptionally clear explanations to maximize student engagement. Well-defined learning objectives, exercises with online solutions for instructors, lecture slides, and an accompanying suite of lab exercises of increasing difficulty in Jupyter Notebooks offer a coherent and convenient teaching package. An ideal teaching resource for courses on large-scale data analytics with machine learning in computer/data science departments."-- Provided by publisher. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Spark |
| General subdivision |
(Electronic resource : Apache Software Foundation) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Big data |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
Machine learning |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Python |
| General subdivision |
(Computer program language) |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
|---|
| Personal name |
Galar, Mikel |
| Relator term |
author. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Koha item type |
Open Collection |