Objectives of Programme's Learning Outcomes

• Imagine, design, develop, and implement conceptual models and computer solutions to address complex problems including decision-making, optimisation, management and production as part of a business innovation approach by integrating changing needs, contexts and issues (technical, economic, societal, ethical and environmental).
• On the basis of modelling, design a system or a strategy addressing the problem raised; evaluate them in light of various parameters of the specifications.
• Deliver a solution selected in the form of diagrams, graphs, prototypes, software and/or digital models.
• Mobilise a structured set of scientific knowledge and skills and specialised techniques in order to carry out computer and management engineering missions, using their expertise and adaptability.
• Master and appropriately mobilise knowledge, models, methods and techniques specific to computer management engineering.
• Assess the validity of models and results in view of the state of science and characteristics of the problem.
• Communicate and exchange information in a structured way - orally, graphically and in writing, in French and in one or more other languages - scientifically, culturally, technically and interpersonally, by adapting to the intended purpose and the relevant public.
• Use and produce scientific and technical documents (reports, plans, specifications) adapted to the intended purpose and the relevant public.
• Adopt a professional and responsible approach, showing an open and critical mind in an independent professional development process.
• Exploit the different means available in order to inform and train independently.
• Contribute by researching the innovative solution of a problem in engineering sciences.
• Develop and implement conceptual analysis, numerical modelling, software implementations, experimental studies and behavioural analysis.
• Collect and analyse data rigorously.

Learning Outcomes of UE

- understand and explain the theory, models and techniques used
- identify which model(s) are best suited for a given dataset
- analyse datasets using a software
- interpret the results from the software, showing an understanding of the theory
 

Content of UE

- descriptive techniques such as principal components analysis and discriminant analysis
- classical models of statistical data analysis (analysis of variance, linear regression)
- data mining (classification and clustering)

Prior Experience

Elementary statistics
Algebra and Calculus
 

Type of Assessment for UE in Q1

• N/A

Q1 UE Assessment Comments

Not applicable

Type of Assessment for UE in Q2

• Oral Examination
• Written examination

Q2 UE Assessment Comments

Theoretical and practical questions of various difficulty levels

Type of Assessment for UE in Q3

• Oral examination
• Written examination

Q3 UE Assessment Comments

idem Q2

Type of Resit Assessment for UE in Q1 (BAB1)

• N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable