Eligibility and admission requirements

1. Diploma requirements

The faculty's committee will assess whether the undergraduate degree is equivalent to the required degree, the bachelor of science in business engineering organised at Ghent University, which gives direct admission to the master of science in business engineering.

Ghent University's business engineering bachelor programme consists of six learning paths. These paths should all be present in the candidate's educational background in order to be eligible for the master's programme. Moreover, we require a certain amount of credits, study hours and/or specific courses in order to make sure students have obtained the required final competences. In order to give international applicants a better idea of these diploma requirements, we have added the number of credits, study hours, courses and the final competences per learning path of the bachelor in business engineering organised at Ghent University.

Business economical & economical learning path

• ECTS credits: 63
• Study hours:  1890
• Courses: 13 (economics A, economics B, accounting A, accounting B, business administration, microeconomics, macroeconomics, financial statement analysis, banking and finance, analytical accounting and cost accounting, managerial leadership, marketing, corporate finance)
• Final competences: students must know and use theoretical concepts, models, quantities and analysis of the economics domain and be able to understand the essence of macro- and micro-economic analysis. In addition, they should also understand the basic concepts, theories and tools associated with market analysis, marketing strategy, marketing implementation, and marketing evaluation. Finally, they should understand the domain of corporate finance with ability to execute a complex capital budgeting analysis. 

Quantitative learning path

• ECTS credits: 15
• Study hours: 450
• Courses: 4 (mathematics IA, mathematics IB, mathematics IIA, mathematics IIB)
• Final competences: students can translate an economics problem into a mathematical problem, can approach this quantitatively and/or graphically and solve it. They can work with mathematical techniques, have understanding of mathematical concepts and proofs, can represent functional relations graphically, analyse and interpret them.  Students should have a thorough theoretical and practical knowledge of linear algebra and real analysis, in particular of: function analysis, optimisation problems, integrals, infinite series and their applications. Students should also be able to determine partial derivatives of functions of multiple variables and differentiate such functions implicitly., compute the total differential and a directional derivative of functions of multiple variables, determine whether an optimization problem is an unconstrained or constrained problem. 
  

Methodological learning path

• ECTS credits: 22
• Study hours: 660
• Courses: 5 (statistics IA, statistics IB, data mining, econometrics, business research methods)
• Final competences:  students should be able to describe and explain elementary statistical concepts and understand the underlying assumptions of common statistical distributions, use techniques from descriptive statistics, inferential statistics and be able to apply probability theory, have insight into hypothesis testing and describe and construct parametric tests and construct and interpret estimators and confidence intervals. Students should understand datamining methods (e.g. supervised learning, unsupervised learning). In addition, students need a thorough knowledge of the classical linear regression model and assumptions. Finally, students should beable to use the most appropriate research methods for data collection and data analysis.

Technical & technological learning path

• ECTS credits: 21
• Study hours: 630
• Courses: 6 (physics, chemistry, electrical and electronics engineering, materials science, mechanical engineering, civil engineering)
• Final competences:  students need insight in some of the fundamental concepts in the domains of physics (mechanics, thermodynamics, waves and oscillations, electricity, magnetism etc) and chemistry (chemical reactions and processes).. In addition, students also need insight in engineering concepts (possibly related to domains as electrical and electronics engineering, materials science, mechanical engineering, civil engineering).

Operations & information management learning path

• ECTS credits: 18
• Study hours:  540
• Courses: 3 (production technology, operations management, operations research)
• Final competences: students need knowledge of the general structure,  organisation of a production process and specific processes in the main industrial sectors. They should be able to situate industrial production within its broader socio-economical context, with attention for sustainability aspects. In addition, they should understand the strategic importance and complexity of operations management and understand theoretical concepts of operations management (e.g. inventory management, production management, facility layout, etc). Students should have insight in the concepts of modelling, optimisation, duality, shadow prices, reduced cost, algorithms, linear and non-linear optimisation, heuristics, decision trees, deterministic and stochastic processes, etc. 

Informatics learning path

• ECTS credits: 18
• Study hours: 540
• Courses: 4 (informatics, object-oriented programming, algorithms and data structures, database systems)
• Final competences: student should understand the fundamentals of Computer Science and information and communication technologies and apply abstraction and algorithmic thinking when solving data processing problems. They should know the principles of object orientation, setting up data structures and algorithms in programming and be able to, given a program design,  develop a correct programming implementation in the programming language of choice (Java, Python, C, C++, C#, Julia, etc). Finally, they have to be able to analyse and develop conceptual models (business process models, data models) and database models and understand SQL statements for defining databases and updating/querying data and NoSQL databases

In case these learning paths all not all present in the candidate's educational background, the committee will decide if admission to the preparatory programme for international students is possible. 

2. Language requirements

English level B2 is required upon enrolment: check the accepted language proofs.

3. Additional faculty requirements

he Faculty of Economics and Business Administration requires all non-EEA applicants to add a GMAT or GRE test score to the application file. Non-EEA applicants who fail to submit a GMAT or GRE test score, will not be considered for academic admission. EEA students are strongly advised to add a GMAT or GRE test score to their application file since the faculty will be granting 8 scholarships to students with outstanding scores on the GMAT or GRE test.

The faculty determined the minimum scores an applicant should obtain. Provided the applicants adopt an appropriate study attitude, this score gives a positive indication of their chances of success in the study programme. A lower score is insufficient and implies the applicants would have considerable difficulties in successfully completing the study programme, therefore, these applications will be refused.