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.
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
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