Data Science for all
DS4all
Methods of Data Science are now used in all areas of business, science and society, due to the rapidly growing number of ways in which data can be processed mechanically and the increasing connection of all areas through the Internet and the resulting easy availability of large and informative data. Applications include engineering technologies such as Autonomous Driving, which has become the most important topic in the automotive industry alongside electromobility, and applications in the Internet economy such as web search and recommendation systems, which in the last 20 years lead to giants such as Google and Amazon emerging out of nothing. In line with the enormous economic and social potential, there is a very high demand on the part of companies and other organisations for specialists in the field of data science, but also for graduates from other disciplines who can safely bridge the gap between their field of application and data science. Doctors are receiving more and more support in evidence-based diagnosis and personalized medicine, and teachers can use computer-assisted analyses of students' learning behavior and progress to design individualized tasks and offer adaptive support. In the wake of international large-scale research, the demand for evidence-based practice has also gained acceptance in the education sector, resulting in growing demands for data science skills among people working in education administration and for data literacy among teachers.
The applications of data science are now so omnipresent that data science, the ability to draw conclusions about the underlying process from data, is now often referred to as the fifth elemental cultural technology in the field of knowledge and communication alongside reading, writing, arithmetic and programming.
But as important as such a basic competence in the field of data science, a data literacy, is, so little do most employees currently feel prepared. In a study by Qlik2 among 5500 employees and managers of various organizations from five European countries, only 17% consider themselves data competent, in Germany only 14%.
The University of Hildesheim would like to combine its expertise in the field of Data Science, which has found expression in the international Master's course Data Analytics, currently the largest Data Science course in Germany, and in the establishment of a Research and Innovation Center Data Science, with the expertise in the programs located at the university in order to offer all students an educational opportunity in the field of Data Science that takes into account the different requirements and focal points of the subjects without sacrificing the teaching of the underlying methods. Its core consists of a set of two introductory courses in data science, which are structured micromodularly, i.e. in units of a maximum of 10 minutes: methodological modules, which are the same for all subjects, and example modules, which illustrate a method using an example from the respective field. We would like to offer Data Science for all in a way, that neither all students are treated identical and ignore their subject-specific needs and factors, nor do we offer completely different, consequently difficult to maintain and (unnecessarily) expensive courses for each individual subject.
Partner:
Prof. Ulrich Heid, Institut für Informationswissenschaft und Sprachtechnologie
Prof. Renate Soellner, Institut für Psychologie
Prof. Christof Wecker, Institut für Erziehungswissenschaft
Prof. Wolf Schünemann, Institut für Sozialwissenschaften
Prof. Martin Sauerwein, Institut für Geographie
Contact:
Prof. Schmidt-Thieme
Lukas Brinkmeyer
