Abstract

This book provides an in-depth exploration of computational data analytics within the broader context of data science. It covers the fundamental concepts, methodologies, and tools that define the field, while also delving into advanced statistical and machine learning techniques tailored for large datasets. The text is structured to facilitate understanding of both theoretical principles and practical applications, bridging the gap between data analysis and real-world challenges. Ethical considerations, privacy, and data governance are emphasized to ensure readers are aware of the responsibilities that come with handling data. Each chapter is enriched with case studies that illustrate the application of computational data analytics in various domains such as healthcare, finance, and environmental studies. The book concludes with a forward-looking discussion on the future of data analytics, highlighting emerging trends and technologies.

Keywords: computational data analytics, data science, machine learning, statistical methods, big data, ethical considerations, real-world applications