ADSEE project partners have created educational modules that show how to apply data science in specific occupations and sectors in order to improve or simplify business process. Four educational modules which have been created were tested by students and lecturers in Croatia, Netherlands, Germany and Slovenia.
ADSEE is an Erasmus+ project which aims to encourage the application of data science in non-technical occupations. This way, project partners try to point out the benefits of data science in those fields in which data science is not used so often. To achieve this, educational modules have been created and tested in order to popularize implementation in data science among students of different professions.
Educational modules have been tested during the pilot in 4 countries, with the students and lecturers from the following higher educational institutions: Algebra University College (Croatia), University of Amsterdam (Netherlands), University of Siegen (Germany) and Faculty of Information Studies (Slovenia). ADSEE educational modules have been implemented into the courses that are take place on these HEIs regularly, which illustrates practicality of the modules as well as benefits of implementing data science into many fields and industries. The pilot was conducted in four educational institutions and the following modules were tested during the pilot: Retail, Culture and Tourism, Privacy and Job Market Signalling.
More than 200 students participated in the pilot, and results of the survey conducted before the pilot showed that majority of the students (50%) could not describe in detail what the data science is about, meaning that they were not introduced with data science before the pilot. However, majority of the students expressed their interest to learn about useful implementation of data science approach. Results of the survey conducted after the pilot showed that 63% of the students pointed out that applying data science in their field of expertise would definitely be very useful. It is very important to emphasize that students’ job positions or fields of expertise belong to non-technical fields, which means that students do not have technical background or significant previous knowledge about data science approach and methods.
You can find detailed pilot analysis with recommendations for implementing data science into non-technical fields here.
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