Data Science

"In [van der Aalst, W.M.P.: Process Mining: Data Science in Action. Springer-Verlag, Berlin (2016)], data science is defined as follows: “Data science is an interdisciplinary field aiming to turn data into real value. Data may be structured or unstructured, big or small, static or streaming. Value may be provided in the form of predictions, automated decisions, models learned from data, or any type of data visualization delivering insights. Data science includes data extraction, data preparation, data exploration, data transformation, storage and retrieval, computing infrastructures, various types of mining and learning, presentation of explanations and predictions, and the exploitation of results taking into account ethical, social, legal, and business aspects.” Data science can be seen as an umbrella term for machine learning, artificial intelligence, mining, Big data, visual analytics, etc. The term is not new. Turing award winner Peter Naur (1928–2016) first used the term ‘data science’ long before it was in vogue. In 1974, Naur wrote [15]: “A basic principle of data science, perhaps the most fundamental that may be formulated, can now be stated: The data representation must be chosen with due regard to the transformation to be achieved and the data processing tools available”. In [15], Naur discusses ‘Large Data Systems’ referring to data sets stored on magnetic disks having a maximum capacity of few megabytes. Clearly, the notion of what is large has changed dramatically since the early seventies, and will continue to change" (p. 6).

pp. 5-19

"The Data Science Revolution How Learning Machines Changed the Way We Work and Do Business"

Wil M.P. van der Aalst in Strous, L., Johnson, R., Grier, D. A., & Swade, D. (2020). Unimagined Futures - ICT Opportunities and Challenges (Vol. 555). Springer International Publishing AG. https://doi.org/10.1007/978-3-030-64246-4