RELEVANCE AND SIGNIFICANCE OF THE PROJECT

Critical studying of data, knowledge transfer and building a deep understanding of processes are essential to the formation of rational behavior. Retaining knowledge is another important aspect that affects rational behavior. Today we are faced with a new round of this evolution: literacy is needed to obtain the knowledge of so-called “big data”. The phenomenon of “large data sets” or simply “big data” has become particularly popular over the past few years, opening up a new stage in the digital divide, the challenge faced by human society after the emergence of computer technology. Today we are faced with a new dimension to this divide, which affects both organizations and individuals. Today it is important to understand the available storage, processing and searching capabilities of large datasets, but more importantly, there are skills in how to extract the useful knowledge from the data and how to use that knowledge. More and more tangible becomes the need to carry out adequate training aimed at acquiring the necessary competences for evaluation, verification and correct interpretation of statistical measures. Understanding the capabilities provided by information technology to capture all facts and events occurring within and outside an organization, as well as the use of computer-based dependency and causality-related apps explaining behavior form the needed competencies in the age of big data. The creation of a methodical basis for overcoming the digital divide in the field of Data Science competencies is what is original and innovative in the proposed solutions.

AIM

The main aim of the project is to create a methodological basis for overcoming the digital divide in the field of Data Science.
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APPROACHES

Building DATA SCIENCE Competence for Overcoming the Digital Divide project will implement the following approaches for achieving the research aim:

1.The verification of hypotheses X1 and X2 will be carried out by conducting empirical studies. Processes will be carried out: data collection (through questionnaires and interviews), data processing and analysis of results.

2.Statistical processing and mathematical modeling of the data obtained and comparison of the current state with that of previous studies will be carried out.

3.Studying the world experience based on literary review and participation in scientific conferences and seminars to exchange experience and ideas.

4.Summarizing and adapting good practice to existing needs and building on this training curriculum and methodology for training in Data science competence.
5. Conducting pilot training on Data Science competences.

TASKS

In order to achieve this aim, the following main tasks have been formulated:
  1. To conduct an empirical study of the degree of digital skills and Data Science literacy of the population in order to reveal different categories of social groups.
  2. To identify the competencies, knowledge and skills that make up Data Science literacy.
  3. To develop an educational model, curriculum and learning aids targeted at some social groups on the development of their Data Science skills.

HYPOTHESES

The following working hypotheses have been formulated:
  • Hypothesis 1 (X1): The level of Data Science competence (data manipulation skill) in the different layers of society is low, even among educated population groups.
  • Hypothesis 2 (X2): The target group of people who need Data Science competencies is limited to people with a higher education level or fall into the so-called “Specialists” or even “professionals”.
  • Hypothesis 3 (X3): Online learning on Data Science competency in the context of digital inequality is a less effective way to conduct “face-to-face” training.