Code
MGFE INF 4402
Level
MS
Field
Informatique
Language
Anglais/English
ECTS Credits
1
Class hours
18
Total student load
20
Program Manager(s)
Department
- Data analytics, Économie et Finances
Educational team
Introduction to the module
This course introduces students to programming in Python as a practical tool for business decision-making. Through a hands-on, project-based format, students learn to manipulate, analyse, and visualize real business data — developing the technical fluency needed to work alongside data professionals, interpret analytical outputs critically, and contribute meaningfully to data-driven organisations. No prior coding experience is required.
This course requires a personal computer (tablets are not recommended), active participation throughout, and a willingness to engage with code and data from the very first session. Students who arrive prepared and engaged will leave with the foundational technical skills and analytical confidence to work with data in their professional lives.
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 PGE2(M1) course, each student will be able to:
1- Explain the fundamental building blocks of Python programming — variables, data types, lists, conditionals, loops, and functions — and give examples of how each is used to automate data operations.
2- Identify and distinguish between core Pandas operations — loading, selecting, filtering, groupby, merge/join, and basic data cleaning — selecting the appropriate operation for a given data task.
3- Use Python in a Colab environment — with AI tools as support — to load, clean, merge, and explore a structured dataset; produce summary statistics, grouped aggregations, and cleaned datasets that a non-technical reader can act on; and identify where AI-generated code requires correction or adaptation before use.
4- Detect and articulate patterns in a real business dataset and link those patterns explicitly to a specific business question using descriptive statistics and visualization.
5- Justify analytical choices made during the project and present data-driven findings to a non-technical audience in a structured format, defending the approach taken when questioned.
Content : structure and schedule
This course corresponds to the first module of the DATA CAMP
**Day 1**: Block 1 — Colab intro, variables, data types (content + group work). Block 2 — lists, indexing/selecting (content + group work). Block 3 — conditionals, loops, brief functions intro (content + group work + in class MCQ).
**Day 2**: Block 1 — Pandas/dataframes basics: loading, selecting (content + group work). Block 2 — filtering, groupby, light data cleaning (content + group work). Block 3 — merge/join, brief reshape mention (content + group work + in class MCQ).
**Day 3**: Block 1 — descriptive statistics (content + group work). Block 2 — visualization (content + group work). Block 3 — group presentations and individual exam.
Sustainable Development Goals
This course contributes to ODD 4 by developing digital literacy and data skills in students from non-technical backgrounds, supporting inclusive access to technical competencies that are increasingly essential in today's professional environment. It contributes to ODD 8 by equipping future managers with hands-on Python and data manipulation skills that directly strengthen their employability and their capacity to contribute to data-driven organisations and economic growth.
Number of SDG's addressed among the 17
2 ODD
Learning delivery
synchrone
Pedagogical methods
This course is based on a learning-by-doing approach. The classes will include a theoretical component, which will cover the essential concepts, as well as a practical component where the theory will be applied through programming exercises.
Evaluation and grading system and catch up exams
Continuous assessment (MCQ 2×10% + group project report and presentation 40%): MCQs evaluate students' knowledge of content presented each day. The group project report and presentation are the outcomes of the group challenge students work on during class, applying course concepts to their case study. Individual contributions to the group project are tracked via shared Colab notebook history.
Final exam (40%, paper-based): Students demonstrate their understanding of a Python script by interpreting its logic, identifying issues, and proposing corrections. The exam covers the core concepts from the course.
Rattrapage: Only the failed component(s) need to be retaken: a new MCQ, a new individual written exam on the same learning objectives, or a new project based on a different dataset with the same deliverable structure. All rattrapage work is followed by an individual oral exam.
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 on Moodle 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
Severance, C. (2016). Python for everybody: Exploring Data using python 3. Charles Severance.
Keywords
Python, Jupyter notebook, data science
Prerequisites
Students are expected to have basic familiarity with data concepts covered in an introductory data science or statistics course, including descriptive statistics, data types, and simple data exploration. No prior programming experience is required.