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The book provides an insight into the practical applications and theoretical foundation of Data Science. The book discusses new ways of embracing agile approaches to various facets of Data Science, including Machine Learning and Artificial Intelligence, data mining, data visualization, and communication. The book includes contributions from academia and industry experts detailing the shortfalls of current tools and techniques used and generating the blueprint of the new technologies. The topics covered in the book range from theoretical and foundational research, platforms, methods, applications, and tools in Data Science. The chapters in the book add a social, geographical, and temporal dimension to Data Science research. The papers included are application-oriented that prepare and use data in discovery research. This book will provide researchers and practitioners with a detailed snapshot of current progress in Data Science. Moreover, it will stimulate new study, research, and the development of new applications.
Data science is an amalgamation of various disciplines of research including the method ranging from empirical methods to current trends of data observation along with the application of rigorous skepticism about observations in data, resulting in interprets that involve formulating hypotheses and deducing inductions. These principles of data science have been distinguished in the form of a series of steps applicable to various enterprises. The processes, algorithms, and systems to extract knowledge and insights from a huge amount of structural and unstructured data produced by these enterprises have made data science the need of the hour. The aim is to use the data as raw inputs and generate valuable data products that can be used by enterprises for their business benefits. Data science in societal applications has been established as a new horizon of the scientific field with the silver lining of research evolution in societal applications using renovations in the existing domains of statistics, computing science, and intelligence science. The application of advancements in these legacy research domains and its practical transformation in engineering, public sector enterprises, social science, and lifestyle spheres is the way forward. This adds to the ever-increasing importance of data science, both big data and small data, which when analyzed scientifically bring with them a wealth of opportunities that combine the new trends to extract, transport, pool, refine, store, analyze, and visualize data which are needed to unleash their power. This also simultaneously can be incorporated into tool-integrated development environments and workflows that are ready to be used by people in general.
To have a successful career in data science emphasizing its societal applications for the benefit of the public at large is a real challenge. The desired knowledge base includes complex topics from statistics, computer science, and mathematics. In addition to that, domain-specific knowledge becomes essential for user-friendly application developments. The real-time applications of data science are added challenge that requires to be mitigated. Thus, there is a need for conjugation of data science with various real-life applications of computing and implementations. The proposed book intends to provide a range of current trends in the theoretical and practical applications of data science that are being developed and implemented by researchers, academicians, and industries