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.

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

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