University of Cebu-Banilad Library

Fundamentals of data science : theory and practice / Jugal K. Kalita, Dhruba K. Bhattacharyya, and Swarup Roy.

By: Kalita, Jugal K [author. ]Contributor(s): Bhattacharyya, Dhruba K [author.] | Roy, Swarup [author.]Material type: TextTextPublisher: United Kingdom : Academic Press/Elsevier, c2024Description: xxvi, 307 pages : illustrations (black and white) ; 23 cmContent type: text Media type: unmediated Carrier type: volume ISBN: 9780323917780 [paperback]Subject(s): Data science | Machine learning | Data mining | Big data | Mathematical statistics
Contents:
Contents: Preface — Acknowledgement — Foreword — Foreword — 1. Introduction — 2. Data, sources, and generation — 3. Data preparation — 4. Machine learning — 5. Regression — 6. Classification — 7. Artificial neural networks — 8. Feature selection — 9. Cluster analysis — 10. Ensemble learning — 11. Association-rule mining — 12. Big data analysis — 13. Data science in practice — 14. Conclusion — Index.
Summary: "Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics. Users will find the authors’ research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy. In addition, a number of descriptive learning tasks are included." --Provided by the publisher
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Subject Reference Subject Reference College Library
Subject Reference 		005.7 K12 2024 (Browse shelf) Available 3UCBL000029697

Includes index.

Contents: Preface — Acknowledgement — Foreword — Foreword — 1. Introduction — 2. Data, sources, and generation — 3. Data preparation — 4. Machine learning — 5. Regression — 6. Classification — 7. Artificial neural networks — 8. Feature selection — 9. Cluster analysis — 10. Ensemble learning — 11. Association-rule mining — 12. Big data analysis — 13. Data science in practice — 14. Conclusion — Index.

"Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications.

The book provides students, researchers and professionals at different levels a good understanding of the concepts of data science, machine learning, data mining and analytics.

Users will find the authors’ research experiences and achievements in data science applications, along with in-depth discussions on topics that are essential for data science projects, including pre-processing, that is carried out before applying predictive and descriptive data analysis tasks and proximity measures for numeric, categorical and mixed-type data. The book's authors include a systematic presentation of many predictive and descriptive learning algorithms, including recent developments that have successfully handled large datasets with high accuracy.

In addition, a number of descriptive learning tasks are included." --Provided by the publisher

Adult

Purchased Ortega, Eric College of Computer Studies Computer Science

Text in English

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