
Essentials of Big Data Analytics
Applications in R and Python
Morgan Kaufmann (Publisher)
Published on 9. January 2026
Book
Paperback/Softback
340 pages
978-0-443-45206-2 (ISBN)
Description
Essentials of Big Data Analytics: Applications in R and Python is a comprehensive guide that demystifies the complex world of big data analytics, blending theoretical concepts with hands-on practices using the Python and R programming languages and MapReduce framework. This book bridges the gap between theory and practical implementation, providing clear and practical understanding of the key principles and techniques essential for harnessing the power of big data. Essentials of Big Data Analytics is designed to provide a comprehensive resource for readers looking to deepen their understanding of Big Data analytics, particularly within a computer science, engineering, and data science context. By bridging theoretical concepts with practical applications, the book emphasizes hands-on learning through exercises and tutorials, specifically utilizing R and Python. Given the growing role of Big Data in industry and scientific research, this book serves as a timely resource to equip professionals with the skills needed to thrive in data-driven environments.
More details
Language
English
Place of publication
San Francisco
United States
Publishing group
Elsevier Science & Technology
Target group
Professional and scholarly
Dimensions
Height: 276 mm
Width: 216 mm
Weight
450 gr
ISBN-13
978-0-443-45206-2 (9780443452062)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Pallavi Chavan | Kalyani Pampattiwar | Ramchandra Mangrulkar
Essentials of Big Data Analytics
Applications in R and Python
E-Book
01/2026
Elsevier
€166.99
Available for download
Persons
Dr. Pallavi Vijay Chavan is Professor and head-IT at Ramrao Adik Institute of Technology, D. Y. Patil Deemed to be University, Navi Mumbai, MH, India. She has been in academia for the past 20 years working in the area of computing theory, data science, and network security. In her academic journey, she has published research work in the data science and security domain with reputed publishers including Springer, Elsevier, CRC Press, and Inderscience. Dr. Kalyani Pampattiwar is an Associate Professor at SIES Graduate School of Technology, Navi Mumbai, MH, India, with 21 years of experience in academia, specializing in blockchain, information security, and network security. She earned her doctoral degree in 2023 from D. Y. Patil Deemed to be University, Navi Mumbai, MH, India. Her research contributions include publications in prestigious international journals, conferences by Inderscience, Springer, and IEEE, as well as book chapters with reputed publishers such as Springer, Elsevier, etc. She has received Swayam's "NPTEL Discipline Star? award. Dr. Ramchandra Mangrulkar is a Professor of Information Technology department in Dwarkadas Sanghvi College of Engineering and has 24 years of teaching experience in the field of intelligent systems and security. He completed his M.Tech. in Computer Science and Engineering from NIT Rourkela. He completed his Ph.D. in Information Security at SGBAU, Amravati. He is the recipient of grants from UGC as well as AICTE
Author
Ramrao Adik Institute of Technology, India
Department of Computer Engineering, SIES Graduate School of Technology, India
Assistant Professor - Computer Engineering Dwarkadas Sanghvi College of Engineering, India
Content
1. Introduction to Big Data Analytics
2. Mathematical Foundations
3. Big Data Technologies and Programming
4. Data Ingestion and Preprocessing
5. Big Data Storage and Management
6. Advanced MapReduce for Big Data Processing
7. Machine Learning Techniques for Big Data Processing
8. Mining Data Streams
9. Case Studies and Practical Applications
10. Hands-on Exercises and Tutorials with R, MapReduce, and Data Streams
11. Emerging Trends and Future Directions
2. Mathematical Foundations
3. Big Data Technologies and Programming
4. Data Ingestion and Preprocessing
5. Big Data Storage and Management
6. Advanced MapReduce for Big Data Processing
7. Machine Learning Techniques for Big Data Processing
8. Mining Data Streams
9. Case Studies and Practical Applications
10. Hands-on Exercises and Tutorials with R, MapReduce, and Data Streams
11. Emerging Trends and Future Directions