Code
MGSF INF 4603
Level
M1
Field
Informatique
Language
French
ECTS Credits
3
Class hours
21
Total student load
60
Program Manager(s)
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.
Learning goals/Programme objectives
- LG1 Being able to extend digital intelligence through its different dimensions
- LG4 Having access to different cross disciplinary management approaches and tools
Learning objectives/Intended learning outcomes
- 1 - Being able to extend digital intelligence through its different dimensions
- 1.1 - Develop digital citizenship and prosperity
- 1.2 - Develop digital creativity for the individual and the organizational
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
Le cours contribue a réalisé les ODD: 4, 8, 9
Number of SDG's addressed among the 17
3 ODD
Learning delivery
synchrone
Pedagogical methods
Les cours transmettent les connaissances nécessaires pour travailler sur des exercices de programmations notées qui vont permettre aux étudiants d'approfondir et d’appliquer les concepts appris dans les cours.
Evaluation and grading system and catch up exams
Final grade = 50% x Graded assignment + 50% x exam
● There will be one or two graded assignments which will contribute 50% to the final grade.
● The exam will cover all the content seen in class and will be graded individually.
The resit will take into account the continuous assessment grades.
In case of non-justified absence a malus of 0.5 points will be applied to the final grade
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