1 University of Library Studies and Information Technologies (BULGARIA)
2 American University in Bulgaria (BULGARIA)
About this paper:
Appears in: INTED2019 Proceedings
Publication year: 2019
Pages: 4248-4256
ISBN: 978-84-09-08619-1
ISSN: 2340-1079
doi: 10.21125/inted.2019.1066
Conference name: 13th International Technology, Education and Development Conference
Dates: 11-13 March, 2019
Location: Valencia, Spain
The past two decades are characterized by a tremendous growth of the amount of data generated and recorded in computer repositories. To learn and benefit of accumulated data, people need to use Information Technologies to retrieve, process, analyze and explore huge amount of data. Consequently, the terms such as Big Data, data analytics, machine learning, deep learning, etc. appeared to mark the dependence of practically all aspects of human life on data and on instruments to explore data. The term Data Science represents in the best possible way the complexity and comprehension of expertise needed nowadays. Developing competences and training professionals in this field represents a significant challenge to educational institutions. Professionals in the field of Data Science, known as Data Scientist, need to possess competences in various areas such statistics, informatics, computing, communication, management, sociology, economics, etc. Data Science has thus emerged as an inter, multi and even transdisciplinary area of knowledge.

Many authors investigate the range of competences, knowledge and skills a Data Scientist need to master. Although in many cases the focus is on technical skills, working with data requires mastery of a huge variety of skills and abilities. In fact, a combination of analytical, statistical, algorithmic, engineering, and technical skills have to be possessed to mine relevant data by involving contextual domain information. In previous studies, we have shown that analytical competences represents the cross-point of all other hard (technical) and soft (non-technical e.g. communication, collaboration, curiosity etc.) skills, especially in the Big Data context. For building such competences a certain level of maturity and experience is essential and graduate level is the natural choice in building educational programs to train Data Scientists. Many factors may influence success of a graduate program in such complicated field. Among the rest, we consider assessment of students’ entry background as essential. The analytical thinking expertise may serve as the key for students, coming from different Bachelor degree programs, to succeed in a Data Science Master program.

The paper shares development of a questionnaire to assess the analytical thinking among the current students in IT-related bachelor degree programs. Motivated by the aims listed above, the research addresses the following questions:
1. Do prospective students have substantial analytical skills to study Data Science Master program?
2. Can we reveal the potential success that students could achieve as analysts when they graduate?
3. Can we improve the Data Science master program to achieve shifting educational patterns and analytical thinking development?

We have examined principles of analytical thinking, many relevant tests such as existing assessments of analytical thinking, critical thinking, problem solving skills tests, etc. The main part of the questionnaire constructed is consisted from logical problems in three different formats: math questions, text assignment and figures pattern recognition. It also includes two questions which measure how students themselves rate their analytical thinking skills and dispositions.
Analytic thinking, analytic literacy, data science competence.