Code
MGFE MIS 5209
Level
M2
Field
Systèmes d’information
Language
Anglais/English
ECTS Credits
4
Class hours
30
Total student load
60
Program Manager(s)
Department
- Data analytics, Économie et Finances
- Technologies, Information et Management
Educational team
Introduction to the module
This module offers an introduction to data analytics, artificial intelligence (AI), and machine learning (ML). The first part, dedicated to Data Analytics, enables students to master industry-standard tools (Python, Jupyter, Google Colaboratory) to conduct exploratory data analysis (EDA) and data visualization, statistical testing, and modeling (regressions, PCA, Clustering). The course then extends to Machine Learning and AI, where students explore various predictive models and learning algorithms to address diverse challenges, while also discussing the broader implications of artificial intelligence. This comprehensive approach transforms complex data into strategic decision-making levers.
Learning goals
- 6. Concevoir et/ou piloter des solutions de gestion innovantes en veillant à garantir une création de valeur soutenable pour toutes les parties prenantes
Learning objectives
- 6.2 - Optimise the use of tools adapted to different areas of management, and define and interpret relevant KPIs in order to measure and guarantee sustainable value creation for all stakeholders.
Course Learning objectives
At the end of this course, the students will be able to:
- Analyze economic data using exploratory data analysis, regression models, multivariate techniques, and machine learning methods.
- Interpret the results of statistical and machine learning analyses.
- Communicate analytical findings effectively to technical and non-technical audiences.
Content : structure and schedule
1. Tools for Data Science
- Python, Jupyter, and Google Colab
2. Data Analysis
- Exploratory Data Analysis (EDA)
3. Statistical Foundations
- Statistical hypotheses and tests
4. Introduction to AI and Machine Learning
- Core concepts and applications
- Social and economic impact of AI
5. Supervised Learning
- Linear regression
- Logistic regression
6. Unsupervised Learning
- Cluster analysis
- Principal Component Analysis
Sustainable Development Goals
SDG 4: Quality Education
The module contributes to this goal by providing students with essential technical and analytical skills in the field of Artificial Intelligence and Data Science. By mastering tools such as Python and machine learning, students develop high-level digital literacy that is indispensable for their professional integration and for meeting the challenges of the modern economy, thus promoting high-quality, relevant education.
Number of SDG's addressed among the 17
1
Learning delivery
synchrone
Pedagogical methods
Lectures followed by in-class practical sessions
Evaluation and grading system and catch up exams
Final exam: 50% (closed-book, written examination)
Project: 50% (group component: 80%, individual contribution: 20%)
Second chance exam (CF2): 100% (closed-book, written examination)
Module Policies
Professor-Student Communication
● The professor will contact the students through their school email address (IMT-BS/TSP) and the Moodle portal. No communication via personal email addresses will take place. It is the student responsibility to regularly check their IMT-BS/TSP mailbox.
● Students can communicate with the professor by emailing him/her to his institutional address. If necessary, it is possible to meet the professor in his office during office-hours or by appointment.
Students with accommodation needs
If a student has a disability that will prevent from completing the described work or require any kind of accommodation, he may inform the program director (with supporting documents) as soon as possible. Also, students are encouraged to discuss it with the professor.
Class behavior
● Out of courtesy for the professor and classmates, all mobile phones, electronic games or other devices that generate sound should be turned off during class.
● Students should avoid disruptive and disrespectful behavior such as: arriving late, leaving early, careless behavior (e.g. sleeping, reading a non-course material, using vulgar language, over-speaking, eating, drinking, etc.). A warning may be given on the first infraction of these rules. Repeated violators will be penalized and may face expulsion from the class and/or other disciplinary proceedings.
● The tolerated delay is 5 minutes. Attendance will be declared via Edusign during these 5 minutes via a QR code provided by the teacher at each course start.
● Student should 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 is absolutely no exception to this rule. No student can continue to take an exam once the time is up. No student may leave the room during an examination unless he / she has finished and handed over all the documents.
● In the case of remote learning, the student must keep his camera on unless instructed otherwise by the professor.
Honor code
IMT-BS is committed to a policy of honesty in the academic community. Conduct that compromises this policy may result in academic and / or disciplinary sanctions. Students must refrain from cheating, lying, plagiarizing and stealing. This includes completing your own original work and giving credit to any other person whose ideas and printed materials (including those from the Internet) are paraphrased or quoted directly. Any student who violates or helps another student violate academic behavior standards will be penalized according to IMT-BS rules.
Textbook Required and Suggested Readings
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2021). An Introduction to Statistical Learning (2nd ed.). Springer.
Keywords
Data science, Digital workplace, AI, Machine learning
Prerequisites
Basic knowledge of Python