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

“AI and Healthcare: Monograph” presents an in-depth analysis of the integration of Artificial Intelligence (AI) into the healthcare sector. This book offers a comprehensive examination of AI’s role in revolutionizing various aspects of healthcare, from diagnostics and treatment to patient care and medical research. It begins by establishing a foundational understanding of AI and healthcare, including historical perspectives and future trends. The book then delves into the technical aspects of AI, such as machine learning and natural language processing, and their applications in healthcare. Special emphasis is given to personalized medicine, showcasing how AI contributes to advancements in genomic research and tailored treatments. Ethical, privacy, and regulatory considerations form a critical part of the discussion, highlighting the challenges and responsibilities of integrating AI in sensitive and impactful healthcare settings. The book also addresses the limitations and challenges of AI in healthcare, providing a balanced view of its potential and pitfalls. Finally, it looks ahead to the future of AI in healthcare, considering its role in upcoming health crises and its impact on healthcare providers, patients, and policy.

Keywords: AI, Healthcare, Machine Learning, Personalized Medicine, Ethical Considerations, Privacy, Future Trends, Diagnostics, Treatment, Medical Research