University of Library Studies and Information Technologies (BULGARIA)
About this paper:
Appears in: ICERI2020 Proceedings
Publication year: 2020
Pages: 9138-9148
ISBN: 978-84-09-24232-0
ISSN: 2340-1095
doi: 10.21125/iceri.2020.2026
Conference name: 13th annual International Conference of Education, Research and Innovation
Dates: 9-10 November, 2020
Location: Online Conference
Big Data promises to revolutionise the production of knowledge within and beyond science, by enabling novel, highly efficient ways to plan, conduct, disseminate and assess research. Big data and data science are widely viewed as implementing a new way of performing research and challenging existing understandings of what counts as scientific knowledge.

There is evidence of significant integration of digital technologies and Big data-driven experimental research in IT training and education. The authors use to implement at the university environment four general principles about high quality learning: First - There is a direct relationship between what students learn and how they learn. Second - Developments in personalising learning make it possible for every student to learn. Third - All learning should be student centered. Fourth - Students should work on real projects to improve their motivation. All of these principles can be enhanced with digital technologies. The information technologies make it possible for learners to engage in learning.

How to train new data scientists at the university level, how to develop their thinking and acting like a data scientist?
Data Science Analytics, Data Science Engineering and Data Management and Governance are the identified skills groups required for data analytics jobs because the Data Science is a systematic study of structure and behavior of the data to deduce valuable and actionable insights. A data scientist is a practitioner who has sufficient knowledge in the overlapping regimes of business needs, domain knowledge, analytical skills, and software and systems engineering to manage the end-to-end data processes in the analytics life cycle.

This paper discusses the main characteristics of convergence of experimental research and Big Data for improved learning. The deeper understanding that in recent years technology has changed the learning behaviors and we have to reshape our teaching methods and learning environments. Our vision for quality of education and its sustainability is based on three pillars: University – Business - Research cooperation, Experimental research and big data-driven innovative programme and learning scenarios. To achieve high quality of education in data science our university has promoted the links with business, implement the inquiry-based education and create a research environment.

The university uses to train its young staff and students based on the Enterprise Big Data Framework which provides a structure to achieve long-term success. The main framework components integrated in our initiative are: structure and organizational aspects and measurable capabilities.
First part of the paper presents an overview of trainings in Data Science. The authors identify the gaps and needs for design and development of competence-based approach in MSc programmes in Data Science.

The second part is dedicated to its design which roots are in the Research Project DM 12/4 - Forming of Data Science Competence for Bridging the Digital Divide. The essential experimental research infrastructure is developed under the effort of the Center of Excellence in Informatics and Information and Communication Technologies.

The last part is focused on the piloting of the programme as an experimental research for learning improvement.

With this paper the authors would like to present their approach for competence based learning in Big Data and Data Science.
Experimental Research, Data Science, Learning Improvement.