MSE Master of Science in Engineering

The Swiss engineering master's degree

Chaque module vaut 3 ECTS. Vous sélectionnez 10 modules/30 ECTS parmi les catégories suivantes:

  • 12-15 crédits ECTS en Modules technico-scientifiques (TSM)
    Les modules TSM vous transmettent une compétence technique spécifique à votre orientation et complètent les modules de spécialisation décentralisés.
  • 9-12 crédits ECTS en Bases théoriques élargies (FTP)
    Les modules FTP traitent de bases théoriques telles que les mathématiques élevées, la physique, la théorie de l’information, la chimie, etc., vous permettant d’étendre votre profondeur scientifique abstraite et de contribuer à créer le lien important entre l’abstraction et l’application dans le domaine de l’innovation.
  • 6-9 crédits ECTS en Modules contextuels (CM)
    Les modules CM vous transmettent des compétences supplémentaires dans des domaines tels que la gestion des technologies, la gestion d’entreprise, la communication, la gestion de projets, le droit des brevets et des contrats, etc.

Le descriptif de module (download pdf) contient le détail des langues pour chaque module selon les catégories suivantes:

  • leçons
  • documentation
  • examen 
Market Analysis and Forecasting (TSM_MarkFor)

A proper understanding of the current state and probable future development of a market is key to any successful business development. The module Market Analysis and Forecasting provides the foundations of analysis of complex socio-economic systems. It puts students in place to autonomously plan, design and execute their own qualitative and quantitative analysis. Development of well-founded forecasts and scenarios completes the understanding of customer data, markets and the socio-economic environment. Tools for the definition and the analysis of company reactions to potential future market scenarios will complete the module, allowing for transformation of market inputs into strategic choices.

Compétences préalables

Good knowledge of English.
Bachelor degree in Business Administration or Engineering.

Objectifs d'apprentissage

Students have the knowledge and the ability to understand and analyze a market as a complex socio-economic system. They are able to identify the most relevant factors determining the market behavior, to identify the causal relation between these factors and to describe socio-economic systems by means of qualitative modelling. Students understand and apply key concepts of the theory of complex systems such as observability, controllability, time variance or invariance, randomness or determinacy of factors, linear or nonlinear, static or dynamic behavior and their impacts on the overall system behavior. Students apply qualitative and quantitative methods for model validation, including basic behavior analysis and statistics. In practical examples they learn to analyze, predict and steer such systems. Finally students are able to present the analysis results in terms of descriptive scenarios using different visualization techniques.

Catégorie de module

The module includes the following topics:

1. Market modelling

    • Understanding the market as a complex, socio-economic system
    • Outlook: system modelling in a broader context
    • Identification of key factors determining the dynamic, time variant and stochastic behavior of a market
    • Systemic market analysis
    • Experiencing complex market behavior, steering complex systems
    • From qualitative to quantitative models
    • Model validation
    • Developing scenarios describing the market future
    • Prospects and limits of modelling

2. Case studies that cover topics in market analysis such as

    • Customer segmentation for marketing campaign planning
    • Customer feedback analysis for service improvement planning
    • Demand prediction for electricity production planning and agricultural planning
    • Credit card default prediction
    • Applicant rating for HR decision making

using basic quantitative methods such as

    • k-Means Clustering
    • k-Means Clustering
    • Linear Regression
    • Logistic Regression
    • Time Series Forecasting

The use and benefits of each discussed topic will be explained, methods for solving the analysis tasks will be presented in an accessible and non-technical manner. The focus will be on the validity and generalizability of the results/conclusions and how they will be included in decision making.

Méthodes d'enseignement et d'apprentissage

The module is taught by theory inputs, case studies and a software tool.


[1] Sterman, J. D. (2000). Business Dynamics. Systems Thinking and Modeling for a Complex World. Boston: McGraw-Hill. ISBN 978-0071241076. (Recommended.)

[2] Rob J. Hyndman, George Athanasopoulos, Forecasting: principles and practice, OTexts, 2013. The book is freely available as an online book at Alternatively, a print version is available: ISBN # 0987507109. (Required.)

Télécharger le descriptif complet