Future Skills
Veranstaltungen

Data Literacy: Digitale Kompetenzen in der Hochschule
●  Daniel Krupka, Geschäftsführer, Gesellschaft für Informatik e.V.
●  Dr. Jens Heidrich, Hauptabteilungsleiter Process Management, Fraunhofer-Institut für Experimentelles Software Engineering
●  Pascal Bauer, Forscher, Fraunhofer-Institut für Experimentelles Software Engineering 

Data Literacy ist die Fähigkeit des planvollen Umgangs mit Daten. In Ergänzung zu spezialisierten Fachkräften – den Data Scientists – liegt der Fokus auf der bedarfsgerechten, Disziplinen übergreifenden Vermittlung von Wissen, um datengestützt arbeiten und entscheiden können. Die Fähigkeit, planvoll mit Daten umzugehen und sie im jeweiligen Kontext bewusst einsetzen und hinterfragen zu können, wird über alle Studienrichtungen hinweg immer wichtiger. In einer Studie für das Hochschulforum Digitalisierung stellen die Gesellschaft für Informatik und das Fraunhofer-Institut für Experimentelles Software-Engineering IESE Best Practices und umsetzbares Wissen für Hochschulen und Fächer für die Curriculumentwicklung im Hinblick auf übergreifende Kompetenzen und Inhalte in der digitalen Welt anhand der Kompetenz der Data Literacy zusammen. Insbesondere soll die Studie beleuchten, wie die Kompetenz der Data Literacy von allen Studierenden immersiv erworben werden kann.
 

Bildung für das digitale Zeitalter: Studienangebote im Bereich Data Science
●  Dr. Klaus Wannemacher, HIS-Institut für Hochschulentwicklung
●  Dr. Maren Lübcke, HIS-Institut für Hochschulentwicklung

Laut Stifterverband und McKinsey werden aktuell bis zu 95.000 Personen mit fortgeschrittenen Datenkenntnissen in Deutschland benötigt. Die vorliegende Studie gibt Aufschluss darüber, welche Rolle Hochschulen bei der Deckung der Nachfrage nach Data-Science-Experten am Arbeitsmarkt zukommt. Dabei wurden eine Bestandsaufnahme der Studiengänge und weiteren Studienangebote sowie eine Expertenbefragung durchgeführt. Es zeigt sich, dass es in Deutschland derzeit 25 Studiengänge überwiegend an staatlichen Hochschulen auf Master-Ebene gibt. Niedrigschwelligere Angebote in Form von Zertifikaten, die auch berufsbegleitend beispielsweise online durchgeführt werden, existieren bislang kaum. Es besteht ein signifikantes, anhaltendes Missverhältnis zwischen der Arbeitsmarktnachfrage und dem Angebot an Absolventen. Auch mittelfristig dürften die Hochschulen nicht in der Lage sein, die Bedürfnisse des Arbeitsmarktes zu befriedigen. Verschiedene Maßnahmen könnten kurz- bzw. mittelfristig helfen, die Situation etwas zu entspannen.
 

EDISON Data Science Framework (EDSF): Facilitating Data Science Curricula Development and Interoperability
●  Dr. Yuri V. Demchenko, Senior Researcher, University of Amsterdam

Data Science is an emerging field of science which requires a multi-disciplinary approach and is based on the Big Data and Data Analytics technologies. Effective use of modern data technologies cannot be achieved without necessary competences and skills and general data literacy. Modern data driven research and industry require new type of specialists that are capable to support all stages of the data lifecycle from data production and input to data processing and actionable results delivery, visualisation and reporting which are jointly defined as the Data Science professions family. The education and training of data scientists currently lacks a commonly accepted, harmonized instructional model that reflects all multi-disciplinary knowledge and competences that are required from the Data Science practitioners in modern, data driven research and the digital economy. The poster illustrates the EDSF structure and data model that includes four main components: Data Science Competence Framework (CF-DS), Data Science Body of Knowledge (DS-BoK), Data Science Model Curriculum (MC-DS), Data Science Professional profiles and occupations taxonomy (DSPP).
 

Towards an Empirically Founded Data Literacy Competence Model
●  Prof. Dr. Ralf Romeike, Gesellschaft für Informatik e.V.
●  Andreas Grillenberger, Gesellschaft für Informatik e.V.

Managing and processing data has always been important in computer science (CS). In recent years, big data, data science and the new opportunities they enable have broken the boundaries of CS: Managing, processing and analyzing large amounts of data becomes a new research paradigm and gains in importance in various scientific disciplines, but also in daily life. Hence, different target groups require skills and competencies related to data. These are are often summarized as data literacy. For elaborating and characterizing these competencies, we develop a competency model of data literacy from a CS perspective. To emphasize both practical and content-related competencies, the model is divided into closely-linked process and content areas. These are derived from existing empirical work on the key concepts of data management, and the contents of various data science study programs. In the poster we present a prototype of this model which is underpinned with several exemplary competency descriptions.
 

ECDL: Supporting Students' Digital Skills
●  Frank Mockler, Head of Programme Standards, ECDL Foundation

ECDL is the world's leading computer skills certification. More than 15 million people have engaged with the ECDL programme, in over 100 countries. The ECDL programme is made up of a wide range of modules, each of which constitutes a standard in a specific area of competence relating to the use of common technologies. Individuals can show their mastery of these competences by becoming certified via high-quality invigilated tests. Our modules have been mapped against a range of national and international frameworks, including Europe's DigComp, so that their relevance and focus can be demonstrated to candidates and policy makers. ECDL modules are particularly useful for young people who may be seen to be digital natives but may lack the skills that they need. Our modules do this by developing both commonly required digital literacy skills, such as working with document and numbers and thinking critically, and more specialised skills, such as working with images or databases. The ECDL programmes are implemented in a range of third-level institutions around the world to support students in their educational experience and future working life.
 

Master's program Data Science & Entrepreneurship (joint degree)
●  Carlien Geelkerken, Senior Policy Officer, Tilburg University

The two-year MSc Data Science & Entrepreneurship is an interdisciplinary business-technology-analytics program hosted by Eindhoven University of Technology and Tilburg University at the JADS campus. The program is built on three components: the academic curriculum, a skills journey, and an individual coaching and mentoring program. During the course students are encouraged to become data literate professionals in the broadest sense of the word. We aim for graduates with a strong ethical and legal compass regarding the collection, management and application of data, although one of the main learning outcomes is the ability to make business out of data.
 

Data literacy in a digital humanities curriculum
●  Prof. Eetu Mäkelä, Assistant Professor in Digital Humanities, Aalto University Helsinki

Inside the digital humanities minor at the University of Helsinki, multiple viewpoints to data literacy coexist. First, in the introductory courses, everyone is given the broad basics of different types and origins of data, and taught the fundamental concepts needed to evaluate how usable a dataset is for a particular purpose. After this, elective courses allow students to delve deeper into e.g. the management, curation, modelling, processing, publication or research use of data. Finally, an interdisciplinary project course pools the diverse knowledge gained by the students back together, and applies it into practice.