Frontiers in Data Science deals with philosophical and practical results in Data Science. A broad definition of Data Science describes the process of analyzing data to transform data into insights. This also involves asking philosophical, legal and social questions in the context of data generation and analysis. In fact, Big Data also belongs to this universe as it comprises data gathering, data fusion and analysis when it comes to manage big data sets. A major goal of this book is to understand data science as a new scientific discipline rather than the practical aspects of data analysis alone.
Preface. Generalized optimal wavelet decomposing algorithm for big financial data. How ’big data’ can make big impact: Findings from a systematic review and a longitudinal case study. Data Science and Big Data: current state and future opportunities. Legal Aspects of Information Science, Data Science and Big Data. Between two hypes: FORZA Digital forensics investigation framework that incorporate legal issues. Application of big data in Intelligent Transportation. Legal and policy aspects of space situational awareness. Causation, probability and all that - Data Science as a novel kind of inductive methodology. Preprocessing in big data: new challenges for discretization and feature selection. The impact of Big Data on making evidence-based decisions. Living in a big data world: Predicting mobile commerce activity through privacy concerns. Privacy as virtue: The negative and the positive obligations of the state. Beyond the hype: Big data concepts, methods, and analytics. Recommendation System for Designing Education Courses: A Data Science Perspective. Between two hypes: Will big data help unravel blind spots in understanding the global land rush?