This volume builds upon the statistical and computational foundations established in Volume 1, advancing readers into modern machine learning, high-dimensional inference, and specialized data analysis domains. This volume focuses on scalable modeling techniques, algorithmic learning, and contemporary applications that define today’s data science practice.

Beginning with classification methods and model selection techniques, the volume systematically introduces tree-based algorithms, ensemble learning, and penalized regression approaches for high-dimensional data. Both supervised and unsupervised learning paradigms are covered, including clustering, factor analysis, survival analysis, and image data analysis, providing breadth across methodological domains.

A distinguishing feature of this volume is its integration of statistical principles with modern machine learning, offering readers a unified perspective on inference, regularization, generalization, and interpretability. Advanced chapters explore text analytics, deep learning from a statistical viewpoint, spatial data analysis, and portfolio risk modeling, highlighting real-world relevance across finance, public policy, and urban analytics.

Throughout the volume, theory is reinforced with practical implementations, case studies, and data-driven illustrations, enabling readers to transition from classical analytics to advanced data science workflows. This volume is intended for students, researchers, and practitioners who have mastered foundational concepts and seek to apply cutting-edge analytical techniques in complex, real-world settings.

Bhargab Chattopadhyay is Associate Professor at the School of Management & Entrepreneurship, Indian Institute of Technology Jodhpur in the DTBI/Operations area. His research interests lie in the area of core set construction, sequential analysis, and inference. He has worked on several projects and is serving as the Associate Editor of the journal Sequential Analysis.

Gaurangadeb Chattopadhyay is Professor in the Department of Statistics at the University of Calcutta, India. He has several publications in journals like Statistics in Medicine, Scandinavian Journal of Statistics, Statistical Papers, and others.

Tapabrata Maiti is Professor and Graduate Director in the Department of Statistics & Probability at Michigan State University, USA. He has secondary appointment with the Department of Marketing, Eli Broad School of Business, and is co-director in the Center for Business and Social Analytics. He has several publications in journals like Annals of Statistics, Journal of Royal Statistical Society Series B, Journal of the American Statistical Association to name some, and is working on several projects funded by federal USA agencies like National Science Foundation (NSF) and National Institutes of Health (NIH).

Prasenjit Majumder is Visiting Faculty at The Chatterjee Group's Centres for Research and Education in Science and Technology (TCG CREST), Kolkata, India; and Professor at Dhirubhai Ambani University, Gandhinagar, India. He has worked on several projects funded by different government agencies. He has authored a monograph and edited two volumes.