Fundamentals of Data Science - Edition 1 - By Jugal K. Kalita, Dhruba K. Bhattacharyya and Swarup Roy Elsevier Health Inspection Copies

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Fundamentals of Data Science,

Edition 1 Theory and Practice

By Jugal K. Kalita, Dhruba K. Bhattacharyya and Swarup Roy

Publication Date: 22 Nov 2023

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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.

Key Features

Presents the foundational concepts of data science along with advanced concepts and real-life applications for applied learning
Includes coverage of a number of key topics such as data quality and pre-processing, proximity and validation, predictive data science, descriptive data science, ensemble learning, association rule mining, Big Data analytics, as well as incremental and distributed learning
Provides updates on key applications of data science techniques in areas such as Computational Biology, Network Intrusion Detection, Natural Language Processing, Software Clone Detection, Financial Data Analysis, and Scientific Time Series Data Analysis
Covers computer program code for implementing descriptive and predictive algorithms

About the author

By Jugal K. Kalita, Professor of Computer Science, University of Colorado, Colorado Springs, CO, USA; Dhruba K. Bhattacharyya, Senior Professor, Department of Computer Science and Engineering, Dean of Academic Affairs, Tezpur University, Assam, India and Swarup Roy, Associate Professor in Computer Applications, Sikkim (Central) University, India

1. Introduction
2. Data, sources, and generation
3. Data preparation
4. Machine learning
5. Regression
6. Classification
7. Artificial neural networks
8. Feature selection and extraction
9. Cluster analysis
10. Ensemble learning
11. Association-rule mining
12. Big-Data analysis
13. Data Science in practice
14. Conclusion

ISBN: 9780323917780

Page Count: 334

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Researchers, developers, and industry professionals in Information Technology and Computer Science, such as developers of AI, Machine Learning, and Deep Learning, as well as other research fields, including Biomedical

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