Principles of Healthcare Data Analytics: A Conceptual and Practical Approach guides readers in transforming medical information into actionable insights that enhance patient care and operational efficiency. Readers learn to extract meaningful intelligence from diverse sources such as electronic health records, medical imaging, IoT devices, genomic datasets, and patient-generated health information from wearables and mobile applications. The book explains comprehensive topics covering warehousing and mining, predictive modeling, machine learning techniques, integration and quality oversight, visualization methods, and technological infrastructure. It addresses critical ethical frameworks, privacy safeguards, and regulatory compliance while offering practical guidance for combining multiple information streams throughout clinical and administrative systems. Special coverage encompasses geospatial analytics, population health oversight, interdisciplinary collaboration, strategic decision-making, and emerging technologies like AI governance and precision medicine. Featuring real-world case studies from hospital operations, pharmaceutical research, public health surveillance, and insurance analytics, Principles of Healthcare Data Analytics demonstrates practical implementations from reducing readmissions to optimizing surgical workflows. The book is ideal for courses in health informatics, medical management, information science, and public health requiring comprehensive analytical expertise.