Fundamentals of data science : theory and practice / Jugal K. Kalita, Dhruba K. Bhattacharyya, and Swarup Roy.
Material type:
TextPublisher: 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| Item type | Current location | Call number | Status | Date due | Barcode |
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Subject Reference
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College Library Subject Reference | 005.7 K12 2024 (Browse shelf) | Available | 3UCBL000029697 |
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| 005.43 H73 2022 Operating systems : design and implementation / | 005.43 T41 2022 Operating systems / | 005.43 T41 2022 Operating systems / | 005.7 K12 2024 Fundamentals of data science : theory and practice / | 005.7 Sn61 2025 Big data analytics : tools and technology / | 005.7 Su81 2019 The emerging technology of big data : its impact as a tool of ICT development / | 005.73 G62 2001 Data structures and algorithms in Java / |
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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