Data Science

This edited book brings together international contributors to show how data analytics and AI can be used to solve real problems and how to run the research and innovation process behind them. It moves from the practical foundations of successful, data-driven work, including funding, project management, strategic and tactical planning, and protecting and commercializing results to the communication and impact side of the lifecycle, including publishing, web presence, targeted dissemination, and preserving knowledge. Alongside these "how-to" perspectives, the chapters offer concrete examples across science, engineering, and society. Data-driven healthcare and precision neuromodulation, application of machine learning in engineering practice, and emerging questions around AI's influence on the future of the web and digital knowledge systems. Written for researchers, advanced students, and practitioners, it supports readers who want both an accessible overview of modern data-science thinking and actionable guidance for turning data into credible results and measurable societal value.