1 American University in Bulgaria (BULGARIA)
2 University of Library Studies and Information Technologies (BULGARIA)
About this paper:
Appears in: ICERI2018 Proceedings
Publication year: 2018
Pages: 7182-7191
ISBN: 978-84-09-05948-5
ISSN: 2340-1095
doi: 10.21125/iceri.2018.2731
Conference name: 11th annual International Conference of Education, Research and Innovation
Dates: 12-14 November, 2018
Location: Seville, Spain
Recent advancement of computer technologies, namely Big Data Era, created a huge opportunity in rising human performance, but added a new round in so-called “digital divide” challenge. Exploring Big Data is a complex and challenging task, because of not only its 3 Vs (Volume, Velocity and Variety), but more important because the current state of publicly available sources is flooded with unreliable, difficult to verify information, in addition to free use of languages and jargons, and missing contexts. In this regard, skills to explore and benefit of data availability, and especially competences for performing data analysis are becoming essential for success nowadays. The question motivated our work is: What represents “data analytics competences” in Big Data Era? In previous studies, we have shown that analytical competences represents the cross-point of all other hard and soft skills in data processing, especially in the Big Data context. In this paper, analytical competences are considered in a way to emphasize their importance in almost all stages of data life cycle.

The principal objective of this paper is to develop a classification (taxonomy) of key competences required for data analysis under challenges of the day – today’s way of creating, disseminating, and accessing data, as well as data processing, assessing, interpreting, and inferring. The taxonomy particularly concerns areas of competences such as identifying problems and what means relevant information; how to locate, access and verify credibility of sources; ability to analyze consistency of obtained data; to interpret aggregated measures used to represent population of researched objects by clear understanding of their limits, constraints, and how they are applied; and last, but not least, synthesizing solutions in consent with data, preserving professional ethical and moral standards.

As this work explores the essential skills and competences needed to succeed in the area of data analytics, it may serve a large and diverse group of professionals and can help in researching analytical competences within groups. The developed and proposed here analytical competences taxonomy is essential in curriculum design. Such classification can be used as benchmark for designing and executing different kinds of training, qualification, and degree programs in the area of Data Science.
Big Data, Data analytics, taxonomy, competences.