Abstract:

The main task of Data Science is to use modern technologies and tools for analyzing big data, through which useful knowledge is extracted to solve various problems. Therefore, the analytical competencies of data professionals are a key element in their professional profile. In our study, we proved that the analytical competencies of data professionals represent the intersection of the other two skill sets - hard (technical) and soft (non-technical, such as communication, collaboration, curiosity, etc.), especially in the context of big data. The ability to think logically, analytical thinking and critical thinking are very useful in activities such as decision making, analysis of questions, as well as research, problem solving and self-assessment, which are part of the work of data professionals.

The development of analytical and critical thinking is a core mission of all educational institutions. Students are also expected to develop analytical, logical and critical thinking throughout the educational process.

The purpose of the research is to provide an assessment of the analytical skills possessed by potential applicants for an MA program in Data Science. It is addressed to students studying for (or graduating from) a Bachelor of Science in Informatics and Computer Science. The questions in the survey are in 4 groups (analytical skills, abstract and logical thinking; questions for distinguishing facts from opinions, as well as questions exploring quantitative thinking). Data were collected through the survey prepared both online and through field research. 235 respondents took part in the survey. To evaluate the analytical capabilities of potential candidates for the MA program, an algorithm was applied, which includes 5 main steps:
(1) Recoding;
(2) Score formation;
(3) Descriptive statistics;
(4) Scale norming and
(5) Correlation analysis.

The results obtained served to build a curriculum for training in the MA program in Data Analysis and Management, which takes into account the input skills of its potential candidates.

Keywords:

Big data, data science, analytical thinking, empirical study.