Data Science

mlsmm2151  2026-2027  Mons

Data Science
5.00 credits
30.0 h
Q1
> Schedule
Teacher(s)
Vande Kerckhove Corentin;
Language
French
> English-friendly
Prerequisites
  • Programming in Python
  • Linear algebra
  • Elementary probability and statistics
Main themes
  • Paradigms and concepts in Data Science.
  • Data cleansing: management of missing values, data transformation and normalisation, exploratory data analysis, feature engineering;
  • Dimensionality reduction and feature selection;
  • Supervised learning: linear and non-linear regressions/classifiers (decision trees, neural networks, SVMs, etc.), evaluation methods and metrics;
  • Unsupervised learning: (k-means, DBSCAN, hierarchical methods, etc.).
Learning outcomes

At the end of this learning unit, the student is able to :

1 At the end of this learning unit, the student is able to:
  • Design a complete workflow for a Data Science project, from initial data exploration to communicating results;
  • Develop and write professional-quality code to manipulate data and train models;
  • Use versioning and collaboration tools (e.g., Git, GitHub) to manage team projects;
  • Understand and explain the fundamental concepts of Data Science.
  • Apply the techniques learned in class to real datasets to solve practical problems;
  • Criticise the results obtained and adjust approaches according to performance and constraints;
  • Produce convincing visualisations of data and results;
  • Evaluate models according to appropriate metrics and interpret their relevance in different contexts.
 
Teaching methods
  • Lectures
  • Course-related exercises
Evaluation methods
  • Completion of an individual project;
  • Oral defense of the project and oral examination during the examination session.
Important notes:
  • This course is subject to a single assessment (i.e., once a grade has been awarded for the course, it is final for the entire academic year and therefore cannot be improved at a later stage)!!!
  • By submitting work for assessment, you affirm that: (i) it accurately reflects the phenomenon being studied; to this end, you must have verified the facts, particularly when they are claimed or provided by generative AI tools (the use of which must be explicitly acknowledged as a tool supporting the completion of your work); and (ii) you have complied with all the specific requirements of the assignment, including requirements regarding transparency and documentation of the scientific approach used. If either of these statements is untrue, whether intentionally or through negligence, you are in breach of your ethical commitment regarding the knowledge produced as part of your work, and potentially of other aspects of academic integrity. This constitutes academic misconduct and will be treated accordingly.
Bibliography
  • HAN J., KAMBER M. (2006), Data mining: concepts and techniques, 2nd ed. Morgan Kaufmann.
  • TUFFERY S. (2007), Data Mining et statistique décisionnelle : l'intelligence dans les bases de données, Technip.
Faculty or entity
> CLSM


Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Sigle
Credits
Prerequisites
Learning outcomes
Master [120] : Business Engineering
INGE2M
5
Master [120] : Business Engineering
INGM2M
5
Master [120] in Management (with work-linked-training)
GESA2M
5