Essentials of Big Data Analytics Applications in R and Python
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Sprache:Englisch
166,99 €
inkl. gesetzl. MwSt.Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Nein
Text-to-Speech
Ja
Erscheinungsdatum
06.01.2026
Verlag
Elsevier Science & Techn.Seitenzahl
250 (Printausgabe)
Dateigröße
40632 KB
Sprache
Englisch
EAN
9780443452079
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
- 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
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