SPECIAL TOPICS IN ECONOMICS AND FINANCE

Course Information
TitleΕΙΔΙΚΑ ΘΕΜΑΤΑ ΣΤΑ ΟΙΚΟΝΟΜΙΚΑ ΚΑΙ ΣΤΑ ΧΡΗΜΑΤΟΟΙΚΟΝΟΜΙΚΑ / SPECIAL TOPICS IN ECONOMICS AND FINANCE
CodeΕΔΕ12
FacultySocial and Economic Sciences
SchoolEconomics
Cycle / Level2nd / Postgraduate
Teaching PeriodWinter/Spring
CoordinatorDimitrios Ntantakas
CommonNo
StatusActive
Course ID600026526

Programme of Study: Data Science in Economics and Finance

Registered students: 0
OrientationAttendance TypeSemesterYearECTS
CoreElective Courses116
Class Information
Academic Year2025 – 2026
Class PeriodWinter
Faculty Instructors
Class ID
600273580
Course Type 2021
Specialization / Direction
Mode of Delivery
  • Face to face
  • Distance learning
Digital Course Content
Learning Outcomes
Upon successful completion of the course “Special Topics”, students will be able to: Identify and Analyze Current Economic and Financial Issues Identify contemporary problems in the economy, financial markets, or business sector. Describe the main causes, dynamics, and socio-economic consequences of these issues. Apply Data Science Tools to Solve Problems Use statistical methods, machine learning techniques, and big data analysis to process economic and business data. Develop predictive models, detect patterns, and draw practical conclusions from complex datasets. Evaluate and Select Appropriate Models and Strategies Compare different analytical approaches and select the most suitable one for each problem. Make evidence-based decisions, assessing the strengths, limitations, and risks of the models used. Develop Strategies to Address Challenges in the Modern Economic and Business Environment Apply analytical tools to propose strategies in real-world scenarios of uncertainty or crisis. Assess socio-economic impacts of decisions and suggest measures to mitigate negative outcomes. Present and Communicate Data Analysis Results Prepare clear analytical reports and presentations that convey findings and recommendations. Collaborate effectively in teams, integrating theory, data analysis, and practical application to solve complex problems.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Work in an international context
  • Work in an interdisciplinary team
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
This course examines current issues related to economics, finance, and/or business. The need to prepare students to face the challenges of the modern economic and business environment requires the study of contemporary problems using Data Science tools, enabling them to effectively manage potential socio-economic impacts.
Educational Material Types
  • Notes
  • Slide presentations
  • Interactive excersises
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Laboratory Teaching
  • Use of ICT in Communication with Students
  • Use of ICT in Student Assessment
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures39✓
Reading Assigment60✓
Written assigments81✓✓
Total180
Student Assessment
Description
Exercises, class presentations and Class Assignments Exercises: During laboratory sessions and seminars, students will complete hands-on exercises in R to analyze international economic data, create visualizations, implement econometric models, and apply forecasting techniques. These exercises aim to reinforce theoretical knowledge through practical experience. Class Presentations: Students will prepare short presentations (5–10 minutes) on selected topics from the four course modules, analyzing both theoretical concepts and practical applications, and fostering discussion and critical thinking. Assignments: Students are expected to complete a seminar project or written assignment that applies course concepts to real-world datasets. The project may include data analysis, econometric modeling, visualization, and policy evaluation, culminating in a written report and/or presentation.
Student Assessment methods
  • Written Assignment (Formative)
  • Oral Exams (Formative)
  • Labortatory Assignment (Formative)
Bibliography
Course Bibliography (Eudoxus)
Data Science for Economics and Finance: Methodologies and Applications - DOI 10.1007/978-3-030-66891-4 Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking DOI 10.5555/9781449361327 Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python DOI 10.5555/9781492072942 Machine Learning for Finance: Principles and Practice for Financial Analysts, Quantitative Analysts, and Financial Engineers DOI 10.5555/9781789136369
Additional bibliography for study
Data Science for Economics and Finance: Methodologies and Applications - DOI 10.1007/978-3-030-66891-4 Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking DOI 10.5555/9781449361327 Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python DOI 10.5555/9781492072942 Machine Learning for Finance: Principles and Practice for Financial Analysts, Quantitative Analysts, and Financial Engineers DOI 10.5555/9781789136369
Last Update
03-09-2025