Course Structure

The program consists of ECTS 126 credits distributed over two year full-time.

Classes are expected to be held four times a week in the morning and afternoon. Please note that the schedule is subject to change.

Note: The structure of the course is subject to change.

Program degree requirements

To graduate from the Master of Science in Economics and Data Science program, students must meet the following academic, research and administrative requirements

• Academic coursework
  Completion of required credits: successfully complete a total of 120 ECTS credits as defined by the curriculum.
• Capstone project: Master’s thesis
  Conduct original research in economics and data science under faculty supervision. Submit a written thesis that meets academic standards and defend it successfully before a committee. The thesis should demonstrate the ability to apply data science methods to economic problems and contribute new insights to the field.
• Internship (Optional for Applied economics track)
  Applied Economics track requires an internship at a relevant role.
• Attendance and participation
  Meet the program’s attendance requirements, including participation in lectures, seminars, workshops, or other required academic activities.

Program learning outcomes

Graduates of the M.Sc. in Economics and Data Science will possess:

• Advanced Economic Knowledge: Master economic theories, models, and methodologies to analyze complex issues.
• Proficiency in Data Science Tools and Techniques: Evaluate policies and market trends, applying evidence-based solutions using Python, R, or similar for data analysis and computational modeling.
• Research Competence: Conduct independent research and communicate findings effectively. Develop and apply advanced mathematical and statistical methods to analyze economic phenomena and large datasets. Conduct independent and collaborative research using cutting-edge methods in economics and data science.
• Global Perspective: Analyze economic issues from a global and multicultural perspective.
• Ethical Decision-Making: Address ethical considerations in economic analysis and policy.
• Global Perspective: Analyze economic issues from a global and multicultural viewpoint.
• Leadership and Communication: Demonstrate leadership, teamwork, and clear communication of economic concepts.
• Lifelong Learning: Commit to continuous learning and adaptability in evolving economic contexts.

Entry requirements

• Bachelor degree from an accredited institution (minimum 180 ECTS credits) or a higher academic qualification (degree course format should be full-time or part-time)
• A pass in Entrance Exam.

Applicants with a GRE/GMAT (10th or Focus edition) certificate may be exempt from the entrance exam. Their exam results will be based on their score in the GRE/GMAT Math (Quantitative) section.

To check how GRE/GMAT Math (Quantitative) score compares to the exam score, please use the equivalency table below.

If applicants prefer, they may still take the exam. In case of a difference between the score granted from the GRE/GMAT Math (Quantitative) section and the exam score, the higher score will be considered.

Equivalency table

English language requirements

Proficiency in the English language as evidenced by one of the below:

• IELTS 6.0 or higher
• TOEFL iBT 60 or higher

Note: We accept only the TOEFL iBT taken at approved test centers. We do not accept the TOEFL iBT Home Edition.

• CEFR B2 (56-60)

Applicants who have completed their bachelor’s degree entirely in English do not need to provide any additional proof of language proficiency.

Exam Format

Total Duration: 90 minutes

Total Number of Questions: 8

Total Marks: 70

Mark Per Correct Answer:
Section A - 8
Section B - 10

The following topics will be covered:

1. Linear algebra

• Vectors, matrices, and operations with them. Linear dependence of a system of vectors. Basis of a linear space. Scalar product.
• Determinant of a square matrix. Calculation of determinants. Expansion of a determinant along a row and a column.
• Transposed matrix. Inverse matrix. Rank of a matrix. Special types of matrices.
• Systems of linear equations. Cramer's method. Gaussian elimination method. Fundamental system of solutions.
• Eigenvalues and eigenvectors of a matrix. Diagonalization and computing the powers of a matrix.

2. Mathematical analysis

• Functions of one variable. Limit of a function. Derivatives. Taylor series expansion. Function analysis and graphing.
• Functions of multiple variables. Partial derivatives. Total differential.
  Gradient of a function. Directional derivative. Hessian matrix. Unconstrained extrema of multivariable functions. Necessary and sufficient conditions for extrema.
• Basic integration. Definite and indefinite integrals.

3. Differential Equations

• Equations with separable variables. Equations in total differentials. Change of variables method. Bernoulli equation.
• First-order linear differential equations. Method of variation of constants.
• Homogeneous linear differential equations with constant coefficients. Characteristic equation. Solution stability.

4. Probability Theory

• Basic concepts of probability theory. Random events and random variables. Probability density function. Joint distribution of multiple random variables. Conditional distributions.
• Characteristics of random variable distributions (expectation, variance, covariance). Properties of expectation, variance, and covariance. Conditional expectation.

Recommended Literature:

1. Gilbert Strang - Introduction to Linear Algebra
2. James Stewart - Calculus: Early Transcendentals
3. Dennis G. Zill - A First Course in Differential Equations with Modeling Applications
4. Sheldon Ross - A First Course in Probability

Exam Date and Deadline for Registration

Fees and Funding

Career Perspectives

M.Sc. in Economics and Data Science program emphasizes integrating quantitative methods, programming skills, and analytical tools to address real-world economic and policy challenges. Graduates will be prepared for careers in academia, industry, or government, where they can leverage data-driven insights to inform decision-making and innovate in the digital economy.

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