Study programme 2020-2021Français
Big Data Analytics
Programme component of Master's in Mathematics à la Faculty of Science

Students are asked to consult the ECTS course descriptions for each learning activity (AA) to know what special Covid-19 assessment methods are possibly planned for the end of Q3

CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-M1-SCMATH-055-MOptional UEBEN TAIEB SouhaibS861 - Big Data and Machine Learning
  • BEN TAIEB Souhaib

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Anglais, Français
Anglais, Français303000066.002nd term

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-INFO-075Big Data Analytics3030000Q2100.00%
Programme component

Objectives of Programme's Learning Outcomes

  • Carry out major projects.
    • Work in teams and, in particular, communicate effectively and with respect for others.
    • Present the objectives and results of a project orally and in writing.
  • Communicate clearly.
    • Communicate the results of mathematical or related fields, both orally and in writing, by adapting to the public.
  • 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.

Learning Outcomes of UE

See single learning activity.

Content of UE

This unit is a follow-up to the "Machine Learning" unit. It covers other topics related to big data analysis which will allow to deepen the knowledge about the previously covered topics.

Prior Experience

"Machine learning" is a pre-requisite for this unit.

Type of Assessment for UE in Q2

  • Presentation and/or works
  • Written examination
  • Graded tests

Q2 UE Assessment Comments

Written exam (60% of total score)
Project (20% of total score)
Assignments (20% of total score)
There is a hurdle of 50% for each of the previous evaluations

Type of Assessment for UE in Q3

  • Presentation and/or works
  • Oral examination
  • Graded tests

Q3 UE Assessment Comments

Oral exam (60% of total score)
Project (20% of total score)
Assignments (20% of total score)
There is a hurdle of 50% for each of the previous evaluations

Type of Teaching Activity/Activities

AAType of Teaching Activity/Activities
S-INFO-075
  • Cours magistraux
  • Travaux pratiques

Mode of delivery

AAMode of delivery
S-INFO-075
  • Mixed

Required Reading

AA
S-INFO-075

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-INFO-075Not applicable

Recommended Reading

AA
S-INFO-075

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-INFO-075Not applicable

Other Recommended Reading

AAOther Recommended Reading
S-INFO-075Not applicable

Grade Deferrals of AAs from one year to the next

AAGrade Deferrals of AAs from one year to the next
S-INFO-075Unauthorized
(*) 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 : 09/07/2021
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be