Study programme 2018-2019Français
Statistical Data Analysis
Programme component of Master's Degree in Mathematics à la Faculty of Science
CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-M1-SCMATH-042-MOptional UESIEBERT XavierF151 - Mathématique et Recherche opérationnelle
  • PIRLOT Marc
  • SIEBERT Xavier

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français30600044.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-014Data Mining306000Q1100.00%
Programme component

Objectives of Programme's Learning Outcomes

  • Have integrated and elaborate mathematical knowledge.
    • Mobilise the Bachelor's course in mathematics to address complex issues and have profound mathematical expertise to complement the knowledge developed in the Bachelor's course.
    • Use prior knowledge to independently learn high-level mathematics.
    • Read research articles in at least one discipline of mathematics.
  • Carry out major projects.
    • Appropriately use bibliographic resources for the intended purpose.
  • Apply innovative methods to solve an unprecedented problem in mathematics or within its applications.
    • Mobilise knowledge, and research and analyse various information sources to propose innovative solutions targeted unprecedented issues.
  • Communicate clearly.
    • Communicate the results of mathematical or related fields, both orally and in writing, by adapting to the public.
    • make a structured and reasoned presentation of the content and principles underlying a piece of work, mobilised skills and the conclusions it leads to.
  • Adapt to different contexts.
    • Have developed a high degree of independence to acquire additional knowledge and new skills to evolve in different contexts.
    • Critically reflect on the impact of mathematics and the implications of projects to which they contribute.
    • Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

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

  • Written examination
  • Practical test

Q1 UE Assessment Comments

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

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, with oral interrogation

Type of Resit Assessment for UE in Q1 (BAB1)

  • N/A

Q1 UE Resit Assessment Comments (BAB1)

Not applicable

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
I-MARO-014
  • Cours magistraux
  • Travaux pratiques
  • Projet sur ordinateur
  • Etudes de cas

Mode of delivery

AAMode of delivery
I-MARO-014
  • Face to face

Required Reading

AA
I-MARO-014

Required Learning Resources/Tools

AARequired Learning Resources/Tools
I-MARO-014- slides of oral presentations (theory and examples) - problem sets

 

Recommended Reading

AA
I-MARO-014

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
I-MARO-014Sans objet

Other Recommended Reading

AAOther Recommended Reading
I-MARO-014R.O.Duda, P.E.Hart, D.G.Stork. "Pattern Classification". John Wiley and Sons, 2000.

I. 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
I-MARO-014Authorized
(*) 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 : 02/05/2019
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