Big Data Analytics: Systems, Algorithms, Applications

Big Data Analytics: Systems, Algorithms, Applications is a wide-ranging textbook on the tools, methods, and real-world uses of big data. It brings together the infrastructure side of analytics with the algorithmic side, then shows how both come together in practical settings such as banking, advertising, IoT, transportation, and social platforms.

What the book covers

The scope is broad but purposeful. Readers move through big data ecosystems, storage and processing approaches, analytics models, and machine learning methods, including supervised, unsupervised, semi-supervised, and reinforcement learning. The book also touches on data mining tasks such as clustering, classification, regression, forecasting, optimization, and visualization.

Why this edition stands out

One of the book’s strengths is its balance between technical foundations and application-driven examples. It discusses open-source technologies such as Hadoop, Spark, and R, while also examining topics like NoSQL databases, feature engineering, model evaluation, large-scale pattern recognition, and privacy concerns. That combination makes the material useful for readers who want both the “how” and the “where it is used.”

Case studies with practical context

Real-life examples appear throughout the book, including references to companies and domains such as Google, Facebook, Microsoft, Deutsche Bank, Opower, Delta Airlines, and city transportation systems. These case studies help connect abstract analytics concepts to deployment in actual organizations.

Who it suits

• Undergraduate and postgraduate students in computer science, IT, and related disciplines
• Researchers looking for a structured survey of big data topics
• Professionals who want a practical overview of analytics platforms and applications

If you want a single volume that bridges systems, algorithms, and applications without losing sight of the bigger analytics landscape, this is a strong and well-focused choice.