Madisetti & Bahga Release Textbook on Big Data Science & Analytics

“Big Data Science & Analytics: A Hands-On Approach”

Vijay Madisetti and Arshdeep Bahga, both affiliated with the Georgia Tech School of Electrical and Computer Engineering, released the textbook titled “Big Data Science & Analytics: A Hands-On Approach”. This textbook is a part of the expanding “A Hands-On Approach™” series, from the same authors.

Cloud computing, mobile applications, Internet of Things (IoT) and Analytics-driven Business Intelligence are changing the face of information technology and computing. Recent industry surveys predict that there will be millions of job openings for engineers and scientists trained in these areas. Large technology companies are laying off thousands of engineers in traditional areas of computing (e.g., PC, and non-Cloud enterprise applications) adding a new urgency to the task of education and training (and retraining) of personnel to meet these new demands.

Vijay Madisetti and Arshdeep Bahga, authors of the “A Hands-On Approach™” series of books, released their third book in the series titled – “Big Data Science & Analytics: A Hands-On Approach”. The book is focused on providing a solid foundation in the areas of big data and analytics in a manner that can be taught at universities and colleges. Extensive use of examples and case studies, with full code support, allows students to learn through doing.

This book is organized into three parts. The first part proposes a new methodology for the design of big data analytics applications, where four different patterns/templates are proposed (Alpha, Beta, Gamma, and Delta) that allow the use of big data stacks and frameworks from vendors ranging from Amazon, Microsoft, and Google. This new methodology forms the pedagogical foundation of this book.

The second part describes big data analytics applications as realization of the proposed Alpha, Beta, Gamma and Delta models, that comprise tools and frameworks for collecting and ingesting data from various sources into the big data analytics infrastructure, distributed filesystems and non-relational (NoSQL) databases for data storage, and processing frameworks for batch and real-time analytics. Also described are serving databases, web and visualization frameworks.

The third part uses advanced case studies that introduce the reader to these tools and frameworks for big data analytics (including Hadoop, MapReduce, Pig, Hive, Spark, Storm, Kafka and HBase), and the architectural and programming aspects of these frameworks as used in the proposed design methodology.

Through generous use of hundreds of figures and tested code samples, the authors have attempted to provide a rigorous “no hype” guide to big data science and analytics. The reader is provided the necessary guidance and knowledge to develop working code for real-world big data applications. Concurrent development of practical applications that accompanies traditional instructional material within the book further enhances the learning process.

Furthermore, an accompanying website for this book ( contains additional support for instruction and learning.

The book is available worldwide on Amazon ( and other major online distribution channels. 

The “A Hands-On Approach™” series of books by the same authors include “Cloud Computing: A Hands-On Approach”, and “Internet of Things: A Hands-On Approach”. These books have been widely adopted by universities worldwide for their program offerings. The book on Cloud Computing was recognized by the ACM Computing Reviews’ as part of their 19th Annual Best of Computing in the 2014 list as a “Notable Book”.

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