Code
MGSF INF 4603
Level
M1
Field
Informatique
Language
French
ECTS Credits
3
Class hours
21
Total student load
60
Program Manager(s)
Department
- Data analytics, Économie et Finances
Educational team
Introduction to the module
This course is designed to provide a comprehensive introduction to data science and AI, with a strong emphasis on practical business applications. Beginning with fundamental statistical concepts, you will progress through more advanced topics, equipping you with the necessary knowledge to understand artificial intelligence. The course will involve hands-on exercises in R and will include take-home exams and a final examination to assess your grasp of important data concepts.
Rubrics
- Concepts statistiques de base
- Visualisation des données
- Régression linéaire et classification
- Régularisation et prédiction hors échantillon
- Algorithmes d'intelligence artificielle
Content : structure and schedule
Bloc 1:
- Introduction
- Méthode d'analyse basique
Bloc 2:
- Régression
- Classification
Bloc 3:
- Régularisation
- Prédiction hors-échantillon
- Regroupement
Bloc 4:
- Algorithmes d'IA : Forêts aléatoires, XGBoost, réseaux de neurones
Sustainable Development Goals
Un cours d’intelligence artificielle et de science des données offre une éducation de qualité en développant des compétences techniques et critiques de haut niveau (ODD 4). Il renforce l’employabilité en préparant les étudiants à des emplois qualifiés et en leur donnant des outils pour améliorer les dynamiques économiques (ODD 8). Il permet également de concevoir des solutions innovantes, d’optimiser des infrastructures et de soutenir des projets technologiques durables (ODD 9).
Number of SDG's addressed among the 17
3 ODD
Learning delivery
synchrone
Pedagogical methods
Ce cours repose sur une approche d'apprentissage par la pratique. Les cours comprendront une partie théorique, qui transmettra les notions essentielles, ainsi qu'une partie pratique où la théorie sera mise en application à l'aide d'exercices de programmation
Evaluation and grading system and catch up exams
La note sera constituée de deux dossiers à rendre, qui représenteront 65 % de la note finale. Les 35 % restants seront évalués par des QCM. Un QCM pourra être réalisé en fin/debut de cours et ne sera pas annoncé à l’avance. Pour repondre a un QCM une exercise de codage sera a completer.
Les dossiers à rendre seront des exercices de codage vous demandant d’implémenter certaines méthodes d’intelligence artificielle. Une presentation finale du dossier peut etre demander.
Pour chaque absence non-justifie un malus de 0.5 point sera appliquee sur la note finale
Modalités de rattrapage :
Un dossier supplémentaire devra être rendu dans un délai d'une semaine.
Module Policies
Teacher-Student Communication
● The teacher will contact students via their academic email address (IMT-BS/TSP) and the Moodle portal. No communication through personal email addresses will occur. It is the student’s responsibility to regularly check their IMT-BS/TSP email inbox.
● Students can communicate with the teacher by sending an email to their institutional address. If needed, it is possible to meet with the teacher in their office during office hours or by appointment.
Students with Accommodation Needs
If a student has a disability that prevents them from performing the described work or requires any kind of accommodation, it is their responsibility to inform the director of studies (with supporting documents) as soon as possible. Also, the student should not hesitate to discuss this with their teacher.
Classroom Behavior
● As a courtesy to the teacher and other students, all mobile phones, electronic games, or other sound-generating devices must be turned off during class.
● The student should avoid any disruptive and disrespectful behavior such as: arriving late to class, leaving early, inconsiderate behavior (e.g., sleeping, reading a document unrelated to the course, using vulgar language, excessive talking, eating, drinking, etc.). A warning may be given for the first offense of these rules. Offenders will be penalized and may be expelled from class and/or subjected to other disciplinary procedures.
● A 5-minute tardiness is tolerated. Attendance will be recorded on Moodle during these 5 minutes via a QR code provided by the teacher at the start of each class.
● The student must arrive on time for exams and other assessments. No one will be allowed to enter the classroom once the first person has finished the exam and left the room. There are absolutely no exceptions to this rule. No student can continue taking an exam once time is up. No student can leave the room during an exam unless they have finished and handed in all documents.
● In the case of remote classes, the student must keep their camera turned on unless instructed otherwise by the teacher.
Ethical Code
IMT-BS is committed to a policy of honesty in the academic environment. Any conduct compromising this policy may result in academic and/or disciplinary sanctions. Students must refrain from cheating, lying, plagiarizing, and stealing. This includes producing original work and recognizing any other person whose ideas and printed materials (including those from the internet) are paraphrased or directly quoted. Any student who breaches or helps another student breach school behavior standards will be sanctioned in accordance with IMT-BS’s rules.
Textbook Required and Suggested Readings
Suggested Reading: Business Data Science, Matt Taddy
Keywords
Intelligence Artificielle, Science des Données
Prerequisites
Comfort en programmation basique, volonté d'apprendre