The subject of the Programme, which operates at the premises of the University of Macedonia, is the study and research of economic analysis in conjunction with data science, with particular emphasis on the application of advanced analytical methods and data-driven approaches to contemporary economic challenges.
The purpose of the Programme is to build a solid scientific foundation for postgraduate students, equipping them with fundamental principles, appropriate methodologies, and analytical tools that enable continuous development and adaptability. It aims to cultivate critical, analytical, and creative skills required for responsible scientific, professional, and research activity. Furthermore, the Programme provides specialized knowledge in the fields of economics and data science—particularly at their intersection—so as to enhance graduates’ ability to initiate or advance successfully in their professional careers. At the same time, the Programme seeks to promote scientific knowledge and research in the interdisciplinary field of economic analysis and data science.
The above purpose is further specified through the following objectives:
To provide high-quality postgraduate education.
To train specialized graduates with a strong theoretical background in economics and data science, as well as the ability to address complex real-world problems through an interdisciplinary approach.
To strengthen students’ confidence through the provision of specialized knowledge and systematic practical training in the integrative application of economic theory and advanced data science methods (including statistical modeling, machine learning, and computational tools), while explicitly cultivating leadership and communication skills.
To develop advanced analytical and computational problem-solving skills, enabling students to formulate, analyze, and solve complex economic problems using mathematical modeling, programming, simulation, and AI-driven techniques.
To enhance data proficiency and quantitative reasoning by training students in the acquisition, management, processing, and analysis of large and complex datasets. Emphasis is placed on the application of modern statistical, econometric, machine learning, and artificial intelligence methods, as well as on reproducibility, transparency, and the interpretation of uncertainty, through laboratory sessions, assignments, and research activities.
To cultivate critical thinking and model evaluation skills, enabling students to rigorously assess economic models, empirical strategies, and AI/ML algorithms by examining assumptions, robustness, trade-offs, and real-world applicability. This is achieved through case studies, research seminars, and advanced methodological courses.
To promote interdisciplinary understanding and ethical awareness, highlighting the interaction between economics, data science, artificial intelligence, and related fields such as computer science, finance, and public policy. Students are encouraged to consider ethical implications of data use, algorithmic decision-making, and policy design through dedicated discussions, coursework, and research projects.
To develop communication, collaboration, and leadership skills, enabling students to effectively present quantitative analyses and AI-enhanced insights through written reports, oral presentations, and data visualization techniques. Team-based assignments, presentations, and project-based learning are used to foster these competencies.
To ensure professional readiness by providing opportunities for the application of acquired knowledge and skills in real-world contexts, such as capstone projects, internships, and case studies, preparing graduates to address data-intensive economic challenges in industry, government, and international organizations.
To support and promote interdisciplinary research through teaching and engagement with complex research topics in economic analysis and data science, as well as through the preparation of high-quality postgraduate theses.
To develop critical and research skills required for doctoral-level studies.
