Study programme 2021-2022Français
Data Structures II
Programme component of Master's in Computer Science à la Faculty of Science

CodeTypeHead of UE Department’s
contact details
Teacher(s)
US-MC-SCINFO-047-MCompulsory UEBRUYERE VéroniqueS829 - Informatique théorique
  • BRUYERE Véronique

Language
of instruction
Language
of assessment
HT(*) HTPE(*) HTPS(*) HR(*) HD(*) CreditsWeighting Term
  • Français
Français300300066.00Année

AA CodeTeaching Activity (AA) HT(*) HTPE(*) HTPS(*) HR(*) HD(*) Term Weighting
S-INFO-020Data Structures II300000Q1
S-INFO-820Data Structures II Project003000A
Programme component

Objectives of Programme's Learning Outcomes

  • Have acquired highly specialised and integrated knowledge and broad skills in the various disciplines of computer science, which come after those within the Bachelor's in computer science.
  • Manage large-scale software development projects.
    • Apply, mobilise, articulate and promote the knowledge and skills acquired in order to help lead and complete a project.
    • Lead a project by mastering its complexity and taking into account the objectives, allocated resources and constraints that characterise it.
    • Demonstrate independence and their ability to work alone or in teams.
  • Manage research, development and innovation.
    • Understand unprecedented problems in computer science and its applications.
  • Master communication techniques.
    • Communicate, both orally and in writing, their findings, original proposals, knowledge and underlying principles, in a clear, structured and justified manner.
  • Develop and integrate a high degree of autonomy.
    • Aquire new knowledge independently.
    • Pursue further training and develop new skills independently.
  • Apply scientific methodology.
    • Demonstrate thoroughness, independence, creativity, intellectual honesty, and ethical values.

Learning Outcomes of UE

To understand how to efficiently solve problems of sorting and dictionnary management, thanks to adapted data structures. To be able to use them to solve a given problem.

Content of UE

Advanced algorithms and data structures for the search, insertion and deletion of a data inside a set of data, as well as the sort of a set of data. Study of binary search trees, AVL trees, B-trees, hash tables, quicksort, optimal sorts. Study of the correctness of the algorithms, and of their complexity in the worst case and the average case.

Project by group of two or three students on a problem to solve thanks to efficient algortihms and adequate data structures. Implementation of the proposed algorithms

Prior Experience

Basic algorithmics and data structures

Type of Assessment for UE in Q1

  • Oral examination

Q1 UE Assessment Comments

Oral examination (2/3)
A failure in one of the AAs involves the failure for the whole learning unit

Type of Assessment for UE in Q2

  • Presentation and/or works

Q2 UE Assessment Comments

Presentation and works (1/3)
A failure in one of the AAs involves the failure for the whole learning unit

Type of Assessment for UE in Q3

  • Presentation and/or works
  • Oral examination

Q3 UE Assessment Comments

Oral examination (2/3)
Presentation and works (1/3)
A failure in one of the AAs involves the failure for the whole learning unit

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
S-INFO-020
  • Cours magistraux
S-INFO-820
  • Préparations, travaux, recherches d'information

Mode of delivery

AAMode of delivery
S-INFO-020
  • Face to face
S-INFO-820
  • Mixed

Required Reading

AA
S-INFO-020
S-INFO-820

Required Learning Resources/Tools

AARequired Learning Resources/Tools
S-INFO-020Not applicable
S-INFO-820Not applicable

Recommended Reading

AA
S-INFO-020
S-INFO-820

Recommended Learning Resources/Tools

AARecommended Learning Resources/Tools
S-INFO-020Not applicable
S-INFO-820Not applicable

Other Recommended Reading

AAOther Recommended Reading
S-INFO-020Introduction to algorithms, by Thomas H. Cormen, Charles E. Leiserson, Ronald L.Rivest (1991). The MIT Press, Mc Graw-Hill.
S-INFO-820Not applicable
(*) 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 : 17/09/2021
20, place du Parc, B7000 Mons - Belgique
Tél: +32 (0)65 373111
Courriel: info.mons@umons.ac.be