Study programme 2019-2020Français
Analysis of Experimental Data
Programme component of Master's in Computer Engineering and Management (Charleroi (Hor. décalé)) à la Faculty of Engineering

Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what assessment methods are planned for the end of Q3

CodeTypeHead of UE Department’s
contact details
UI-M1-IRIGIG-830-CCompulsory UESIEBERT XavierF151 - Mathématique et Recherche opérationnelle
  • SIEBERT Xavier

of instruction
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français301200044.002nd term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-156Analysis of Experimental Data3012000Q2100.00%
Programme component

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

Content of UE

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

Type of Assessment for UE in Q2

  • Written examination
  • Practical test

Q2 UE Assessment Comments

written exam for the theory, followed by a practial exam on the computer

Type of Assessment for UE in Q3

  • Written examination
  • Practical Test

Q3 UE Assessment Comments

written exam for the theory, followed by a practial exam on the computer

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
  • Cours magistraux
  • Ateliers et projets encadrés au sein de l'établissement

Mode of delivery

AAMode of delivery
  • Face to face

Required Reading


Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-MARO-156Not applicable

Recommended Reading


Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-MARO-156Not applicable

Other Recommended Reading

AAOther Recommended Reading
I-MARO-156I. H. Witten, E. Frank. Data Mining : "Practical Machine Learning Tools and Techniques with Java Implementations". Morgan Kaufmann, 2010

J-M. Azaïs, J-M. Bardet, "Le Modèle Linéaire par l'exemple : Régression, Analyse de la Variance et Plans d'Expériences. Illustrations numériques avec les logiciels R, SAS et Splus", Dunot, 2006

R.E.Walpole, R.H.Myers, S.L.Myers, K.Ye, "Probability and Statistics for Engineers and Scientists", Prentice Hall, 2012

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
(*) HT : Hours of theory - HTPE : Hours of in-class exercices - HTPS : hours of practical work - HD : HMiscellaneous time - HR : Hours of remedial classes. - Per. (Period), Y=Year, Q1=1st term et Q2=2nd term
Date de génération : 13/07/2020
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