Special issue on “Learning in data science: theory, methods and applications”—preface by the guest editors

Recently, the interplay of disciplines involved in data science, most notably statistics and computer science has intensified. Impressive advances in statistical, deep, and machine learning (both supervised and unsupervised) have been achieved by developing and applying more and more complex methods for data, data stream, text, or image processing. They are now further developed and used in many fields of applications like, e.g., engineering, finance, genomics, industrial automation, industry 4.0, marketing, personalised medicine or health care, systems biology.