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).
• Identify complex problems to be solved and develop the specifications with the client by integrating 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.
• Evaluate the approach and results for their adaptation (modularity, optimisation, quality, robustness, reliability, upgradeability, etc.).
• 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.
• Analyse and model an innovative IT solution or a business strategy by critically selecting theories and methodological approaches (modelling, optimisation, algorithms, calculations), and taking into account multidisciplinary aspects.
• Identify and discuss possible applications of new and emerging technologies in the field of information technology and sciences and quantifying and qualifying business management.
• Assess the validity of models and results in view of the state of science and characteristics of the problem.

Learning Outcomes of UE

perform a multivariate statistical analysis on data files;
interpret the results of such an analysis;
understand and apply classification and clustering techniques;
understand the principles of experimental design

UE Content: description and pedagogical relevance

data representation and analysis; factorial methods (principal components analysis, multiple correspondance methods, rank analysis, ...); regression, analysis of variance; classification, clustering; experimental design. Software : R, Weka
 

Prior Experience

one-dimensional descriptive statistics
algebra, calculus