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Fundamentals of Data Science
Theory and Practice
- 1st Edition - November 17, 2023
- Authors: Jugal K. Kalita, Dhruba K. Bhattacharyya, Swarup Roy
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 1 7 7 8 - 0
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 7 2 6 3 - 5
Fundamentals of Data Science: Theory and Practice presents basic and advanced concepts in data science along with real-life applications. The book provides students, researche… Read more
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Request a sales quoteFundamentals 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.
- 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
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
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- Preface
- Acknowledgment
- Foreword
- Foreword
- 1: Introduction
- Abstract
- 1.1. Data, information, and knowledge
- 1.2. Data Science: the art of data exploration
- 1.3. What is not Data Science?
- 1.4. Data Science tasks
- 1.5. Data Science objectives
- 1.6. Applications of Data Science
- 1.7. How to read the book?
- References
- 2: Data, sources, and generation
- Abstract
- 2.1. Introduction
- 2.2. Data attributes
- 2.3. Data-storage formats
- 2.4. Data sources
- 2.5. Data generation
- 2.6. Summary
- References
- 3: Data preparation
- Abstract
- 3.1. Introduction
- 3.2. Data cleaning
- 3.3. Data reduction
- 3.4. Data transformation
- 3.5. Data normalization
- 3.6. Data integration
- 3.7. Summary
- References
- 4: Machine learning
- Abstract
- 4.1. Introduction
- 4.2. Machine Learning paradigms
- 4.3. Inductive bias
- 4.4. Evaluating a classifier
- 4.5. Summary
- References
- 5: Regression
- Abstract
- 5.1. Introduction
- 5.2. Regression
- 5.3. Evaluating linear regression
- 5.4. Multidimensional linear regression
- 5.5. Polynomial regression
- 5.6. Overfitting in regression
- 5.7. Reducing overfitting in regression: regularization
- 5.8. Other approaches to regression
- 5.9. Summary
- References
- 6: Classification
- Abstract
- 6.1. Introduction
- 6.2. Nearest-neighbor classifiers
- 6.3. Decision trees
- 6.4. Support-Vector Machines (SVM)
- 6.5. Incremental classification
- 6.6. Summary
- References
- 7: Artificial neural networks
- Abstract
- 7.1. Introduction
- 7.2. From biological to artificial neuron
- 7.3. Multilayer perceptron
- 7.4. Learning by backpropagation
- 7.5. Loss functions
- 7.6. Activation functions
- 7.7. Deep neural networks
- 7.8. Summary
- References
- 8: Feature selection
- Abstract
- 8.1. Introduction
- 8.2. Steps in feature selection
- 8.3. Principal-component analysis for feature reduction
- References
- 9: Cluster analysis
- Abstract
- 9.1. Introduction
- 9.2. What is cluster analysis?
- 9.3. Proximity measures
- 9.4. Exclusive clustering techniques
- 9.5. High-dimensional data clustering
- 9.6. Biclustering
- 9.7. Cluster-validity measures
- 9.8. Summary
- References
- 10: Ensemble learning
- Abstract
- 10.1. Introduction
- 10.2. Ensemble-learning framework
- 10.3. Supervised ensemble learning
- 10.4. Unsupervised ensemble learning
- 10.5. Semisupervised ensemble learning
- 10.6. Issues and challenges
- 10.7. Summary
- References
- 11: Association-rule mining
- Abstract
- Acknowledgement
- 11.1. Introduction
- 11.2. Association analysis: basic concepts
- 11.3. Frequent itemset-mining algorithms
- 11.4. Association mining in quantitative data
- 11.5. Correlation mining
- 11.6. Distributed and parallel association mining
- 11.7. Summary
- References
- 12: Big Data analysis
- Abstract
- 12.1. Introduction
- 12.2. Characteristics of Big Data
- 12.3. Types of Big Data
- 12.4. Big Data analysis problems
- 12.5. Big Data analytics techniques
- 12.6. Big Data analytics platforms
- 12.7. Big Data analytics architecture
- 12.8. Tools and systems for Big Data analytics
- 12.9. Active challenges
- 12.10. Summary
- References
- 13: Data Science in practice
- Abstract
- 13.1. Need of Data Science in the real world
- 13.2. Hands-on Data Science with Python
- 13.3. Dataset preprocessing
- 13.4. Feature selection and normalization
- 13.5. Classification
- 13.6. Clustering
- 13.7. Summary
- References
- 14: Conclusion
- Abstract
- Index
- No. of pages: 334
- Language: English
- Edition: 1
- Published: November 17, 2023
- Imprint: Academic Press
- Paperback ISBN: 9780323917780
- eBook ISBN: 9780323972635
JK
Jugal K. Kalita
Dr. Jugal Kalita received his BTech degree from the Indian Institute of Technology in Kharagpur, India, his MS degree from the University of Saskatchewan, Canada, and his MS and PhD degrees from the University of Pennsylvania. He is a Professor of Computer Science at the University of Colorado at Colorado Springs. His research interests include machine learning and its applications to areas such as natural language processing, intrusion detection, and bioinformatics. He is the author of more than 250 research articles in reputed conferences and journals and has authored four books, including Network Traffic Anomaly Detection and Prevention from Springer, Gene Expression Data Analysis: A Statistical and Machine Learning Perspective from Chapman and Hall/CRC Press, and Recent Developments in Machine Learning and Data Analytics from Springer. He has received multiple National Science Foundation (NSF) grants
Affiliations and expertise
Professor of Computer Science, University of Colorado, Colorado Springs, CO, USADB
Dhruba K. Bhattacharyya
Dr. Dhruba K. Bhattacharyya received his PhD in Computer Science and Engineering from Tezpur University. Currently, he is a Senior Professor in the Department of Computer Science & Engineering, Tezpur University, and also the Dean of Academic Affairs. Dr. Bhattacharyya’s major research interests are Machine Learning, Cyber Security, and Bioinformatics, and in all these three fields his contributions are significant. Dr. Bhattacharyya has published more than 260 research articles in peer-reviewed international journals and selective conference proceedings. Dr. Bhattacharyya has authored/edited 18 reference books on machine learning and its applications, including Network Traffic Anomaly Detection and Prevention from Springer, Gene Expression Data Analysis: A Statistical and Machine Learning Perspective from Chapman and Hall/CRC Press, Data Mining Techniques and Its Application in Medical Imagery from VDM, and Clustering Techniques in Spatial Data Analysis from Lambert Academic Publishing. Dr. Bhattacharyya is on the review panel for most major research grants reviewed by the Department of Science & Technology, Government of India, and several international funding agencies. Machine learning research at Tezpur University, led by Dr. Bhattacharyya, has been recognized by the Indian Ministry of Education as a Centre of Excellence, one among twenty in India. Dr. Bhattacharyya is a fellow of the Institution of Electronics and Telecommunication Engineers, and Institution of Engineers, and is a Senior Member of IEEE. He is also on the Editorial/Advisory Boards of several international journals, conferences, and workshops
Affiliations and expertise
Senior Professor, Department of Computer Science and Engineering, Dean of Academic Affairs, Tezpur University, Assam, IndiaSR
Swarup Roy
Swarup Roy is a Professor in Computer Science at Sikkim (Central) University, Gangtok. He received his M.Tech. and PhD (Comp. Sc. & Engg.) from Tezpur (Central) University. He worked as a Post-Doctoral Fellow (PDF) at University of Colorado at Colorado Springs, USA and Indian Institute of Technology (IIT), Guwahati. His research interest includes Machine Learning, Data Science, Network
Science, Intrusion Detection and Computational Biology. He has published 80+ research articles in high impact international journals and leading world conferences across the globe in machine
learning and bioinformatics. He authored the book “Biological Network Analysis- Trends, Approaches, Graphical Theory and Algorithms” published by Elsevier, USA . He was a recipient of Best Doctoral Thesis Award from IIT-Roorkee and University Gold Medal. He was selected for Overseas Research Associate Fellowship from DBT, Govt. of India in 2015 to conduct research in the foreign laboratories and funding from DST-SERB to visit SPAIN in 2012 to present his research paper. He taught undergraduate and graduate students of computer science at University of Colorado, USA as visiting professor. He has been listed as a data science subject expert by the Department of Science & Technology-Govt of India. He acted as Track Co-Chair for Biological Modelling at 8th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB) 2017, Boston, USA. He is acting as Guest Editor of International Journals such as MDPI Data, MDPI Life, Frontier in Bioinformatics and in the technical committee of many reputed International Journals.
Affiliations and expertise
Professor of Computer Science, Sikkim (Central) University, Sikkim, IndiaRead Fundamentals of Data Science on ScienceDirect