Duration
30h Th
Number of credits
Lecturer
Language(s) of instruction
English language
Organisation and examination
Teaching in the second semester
Schedule
Units courses prerequisite and corequisite
Prerequisite or corequisite units are presented within each program
Learning unit contents
This course aims to equip students with some conceptual and practical tools used to analyze, predict, and simulate the evolution of complex economic or social systems. The integration of forecasting techniques and microsimulation methods allows the course to explore short-term predictive analysis and long-term scenario evaluation, thereby supporting data-based decision-making in business, economics, and public policy.
Learning outcomes of the learning unit
By the end of the course, students will be able to:
- Apply forecasting methods to real-world managerial and economic datasets.
- Critically assess the performance and reliability of different forecasting models.
- Design microsimulation models tailored to specific problems.
- Combine predictive and simulation-based approaches to support strategic decision-making.
Prerequisite knowledge and skills
Basic statistics concepts, basics in excel and in any programming language (loops, variables, etc).
Planned learning activities and teaching methods
The course consists in face to face sessions including theory and applications. The students will need a computer for excel and RStudio.
Mode of delivery (face to face, distance learning, hybrid learning)
Face-to-face course
Course materials and recommended or required readings
Platform(s) used for course materials:
- LOL@
Further information:
Slides will be available on Lola and scientific articles will be recommended during the course.
Exam(s) in session
Any session
- In-person
oral exam
Written work / report
Further information:
During the semester, students will complete a group project to produce a scientific poster on a predetermined topic. The poster will be presented during the exam session.
1. Group poster presentation
The group will present the poster during the exam session. All group members are required to participate actively in the presentation. The presentation will be followed by questions from the examiners.
The use of AI tools is not prohibited. However, any use of AI must comply with the applicable plagiarism rules and with the ULiège Charter on the use of AI in Projects.
Each student is personally responsible for the content of the complete poster, regardless of which group member produced a particular part of it. Students must therefore be able to explain, justify, and accurately define all concepts, methods, and results presented on the poster.
The poster and its oral presentation together account for 50% of the final grade.
2. Individual oral exam
An individual oral exam will be organised for each student. It will consist of theoretical and practical questions related to the course material.
This examination accounts for the remaining 50% of the final grade.
Individual Assessment within the Group Project
Although the poster is a group project, individual participation and contribution to the project will be assessed. Consequently, students belonging to the same group may receive different grades for the project and presentation.
NB: This text was reformulated using ChatGPT, but the teachers validated the content.
Work placement(s)
Organisational remarks and main changes to the course
Contacts
Main teacher : Morgane Dumont : morgane.dumont@uliege.be (office 3/7 in N1a)
Assistants : Emil Geleleens : Emil.Geleleens@uliege.be
Alexandre Kerff : akerff@uliege.be