Essentials of Big Data Analytics : Applications in R and Python

著者名：Chavan Ph.D., Pallavi/Pampattiwar Ph.D., Kalyani/Mangrulkar Ph.D., Ramchandra

Morgan Kaufmann（2026/01/06発売）

• 言語：ENG
• ISBN：9780443452062
• eISBN：9780443452079

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.- Includes hands-on Tutorials and Case Studies: Structured exercises and real-world examples reinforce learning and skill-building- Focuses on Python and R for Big Data: Detailed lessons in Python and R programming cater to the increasing demand for data science expertise- Balanced Theory and Practice: Comprehensive coverage ensures a strong theoretical foundation paired with actionable insights for real-world application

Table of Contents

1. Introduction to Big Data Analytics2. Mathematical Foundations3. Big Data Technologies and Programming4. Data Ingestion and Preprocessing5. Big Data Storage and Management6. Advanced MapReduce for Big Data Processing7. Machine Learning Techniques for Big Data Processing8. Mining Data Streams9. Case Studies and Practical Applications10. Hands-on Exercises and Tutorials with R, MapReduce, and Data Streams11. Emerging Trends and Future Directions