Study programme 2021-2022Français
Statistical Data Analysis
Programme component of Master's 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
  • SIEBERT Xavier

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français181800044.001st term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
I-MARO-014Data Mining1818000Q1100.00%

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, practial exam on the computer

Type of Assessment for UE in Q3

  • Written examination
  • Practical Test

Q3 UE Assessment Comments

idem Q1

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
  • Mixed

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.

C.M. Bishop Pattern recognition and machine learning. springer, 2006.

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

K P Murphy, Machine learning: a probabilistic perspective. MIT press, 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 dernière mise à jour de la fiche ECTS par l'enseignant : 16/05/2021
Date de dernière génération automatique de la page : 06/05/2022
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Courriel: info.mons@umons.ac.be