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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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.

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

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, USA

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, India

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, India