STATISTICAL METHODS FOR BIG DATA AND MACHINE LEARNING

In recent years, we have witnessed a profound transformation
in the way data is generated, stored, analyzed, and ultimately utilized
to inform decisions across nearly every domain of human activity.
This era of Big Data has catalyzed an unprecedented synergy between
traditional statistical methods and the fast-evolving field of machine
learning. As organizations and researchers navigate massive
datasets—from consumer behavior logs to genomic sequences—the
ability to extract meaningful insights depends increasingly on a robust
statistical foundation interwoven with computational efficiency and
algorithmic sophistication.
This book, Statistical Methods for Big Data and Machine
Learning, is designed to bridge that critical intersection. It provides an
integrated view of the statistical underpinnings essential for modern
data science, while also offering practical insights into how these

concepts manifest within machine learning pipelines, tools, and real-
world applications. Whether applied to model credit risk, optimize

marketing strategies, understand public health trends, or power
recommendation systems, the statistical methods covered here are
fundamental to extracting value from data at scale.
The early chapters establish a foundational understanding of
statistics—both descriptive and inferential—before progressively
moving toward advanced topics such as regularization, resampling,
Bayesian inference, and ensemble learning. The book addresses
essential questions: How do we model uncertainty in large-scale
systems? How do we select features from high-dimensional data?
What are the ethical considerations when deploying models in
sensitive contexts? Each chapter not only introduces theoretical
frameworks but also emphasizes implementation, interpretability, and

critical thinking, ensuring readers are not only capable of using
statistical methods, but also of questioning their limitations and
implications. The data analysis section delves into the intricacies of
working with structured, semi-structured, and unstructured data.
Concepts such as estimates of location and variability, as well as
categorical data analysis, are covered in depth. A dedicated segment
on database systems, including relational databases, NoSQL, and
query optimization, ensures that readers understand the infrastructure
behind data storage and retrieval.
A key strength of this text lies in its holistic structure. We
have organized the material to suit a wide audience: undergraduate and
graduate students in statistics, data science, or computer science;
professionals in industry seeking to deepen their analytical skills; and
educators looking for comprehensive teaching material. From a

thorough discussion of machine learning’s statistical roots to hands-
on case studies rooted in global and Indian contexts, this book aims to

be as accessible as it is rigorous.
The journey from raw data to actionable insight is not merely
a technical one; it is also conceptual, ethical, and deeply human. As
data volumes continue to grow, so too must our responsibility to use
them wisely. Our hope is that this book serves not just as a guide to
methods, but also as a catalyst for responsible, impactful data-driven
discovery.