Data Science

5.00 credits

30.0 h

> Schedule

Teacher(s)

Ait El Cadi Abdessamad;

Language

French
> English-friendly

Prerequisites

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

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

Title of the programme

Sigle

Credits

Prerequisites

Learning outcomes

Master [120] : Business Engineering

INGE2M

Master [120] : Business Engineering

INGM2M

Master [120] in Management (with work-linked-training)

GESA2M