Frontiers in Data Science

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.

Matthias Dehmer studied mathematics at the University of Siegen (Germany) and received his Ph.D. in computer science from the Technical University of Darmstadt (Germany). Afterwards, he was a research fellow at Vienna Bio Center (Austria), Vienna University of Technology, and University of Coimbra (Portugal). He obtained his habilitation in applied discrete mathematics from the Vienna University of Technology. Currently, he is Professor at UMIT - The Health and Life Sciences University (Austria) and also has a post at Bundeswehr Universit]at M]unchen (Germany). His research interests are in Data Science, Big Data, Complex Networks, Machine Learning and Information Theory. In particular, he is also working on machine learning-based methods to design new data analysis methods for solving problems in computational biology. He has more than 205 publications in applied mathematics, computer science and related disciplines.

Frank Emmert-Streib studied physics at the University of Siegen, Germany, gaining his PhD in theoretical physics from the University of Bremen. He was a postdoctoral fellow in the USA before becoming a Faculty member at the Center for Cancer Research at the Queen's University Belfast (UK). Currently, he is a Professor at Tampere University Technology, Finland, in the Department of Signal Processing. His research interests are in the field of computational biology, data science and analytics in the development and application of methods from statistics and machine learning for the analysis of big data from genomics, finance and business.