$8.98
Embark on a data-driven journey into the realm of big data analytics and data science with this comprehensive guide. Explore the principles of data analysis, machine learning, and predictive modeling in the context of big data. Gain insights into the tools and techniques used to extract valuable insights from large datasets. Discover how data scientists leverage statistical methods and algorithms to uncover hidden patterns and trends. This book offers a deep dive into the world of data science, providing a solid foundation for mastering big data analytics.
$8.98
Embark on a data-driven journey into the realm of big data analytics and data science with this comprehensive guide. Explore the principles of data analysis, machine learning, and predictive modeling in the context of big data. Gain insights into the tools and techniques used to extract valuable insights from large datasets. Discover how data scientists leverage statistical methods and algorithms to uncover hidden patterns and trends. This book offers a deep dive into the world of data science, providing a solid foundation for mastering big data analytics.
Embark on a data-driven journey into the realm of big data analytics and data science with this comprehensive guide. Explore the principles of data analysis, machine learning, and predictive modeling in the context of big data. Gain insights into the tools and techniques used to extract valuable insights from large datasets. Discover how data scientists leverage statistical methods and algorithms to uncover hidden patterns and trends. This book offers a deep dive into the world of data science, providing a solid foundation for mastering big data analytics.Big Data Analytics and Data ScienceHello
Format: Paperback
Publication Year: 2023
Language: English
Edition: First Edition
Publisher: PS Publishing
Topics Covered:
big data analytics
data analysis
data science
Machine Learning
predictive modeling
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Japan: 3-5 days
Australia: 2-6 days
Rest of the world: 5-14 days
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30 Days free return policy if item is the wrong item or damaged.