Essentials of Big Data Analytics

Applications in R and Python
Erscheinungsjahr 2026
ISBN: 978-0-443-45206-2
Verlag: Elsevier Science

Buch, Englisch, 340 Seiten, Format (B × H): 216 mm x 274 mm, Gewicht: 907 g

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.

Autoren/Hrsg.

Chavan, Pallavi

Pampattiwar, Kalyani

Mangrulkar, Ramchandra

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Wirtschaftswissenschaften Betriebswirtschaft Wirtschaftsinformatik, SAP, IT-Management
• Mathematik | Informatik EDV | Informatik Angewandte Informatik Wirtschaftsinformatik
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data

Inhaltsverzeichnis

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

Über Autor(innen)

Chavan, Pallavi
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.

Mangrulkar, Ramchandra
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