Code
MGYF STR 5941
Level
M2
Field
Stratégie
Language
Français/French
ECTS Credits
2
Class hours
28
Total student load
40
Program Manager(s)
Department
- Management, Marketing et Stratégie
Educational team
Introduction to the module
This intensive course introduces management students to the design of digital solutions through “vibe coding”: navigating an AI-assisted development environment, organizing data and tools, verifying results, and transforming a management question into operational deliverables. An overarching project analyzes the transformation of the case company and the strategic responses of the comparison company, based on sourced financial documents. Students progressively develop analyses, an animated narrative, an investment report, and a website with authenticated access. The focus is on building a coherent sequence of prompts, breaking down tasks, and producing an operational outcome, rather than solely on code generation.
Learning goals
- 1. S’approprier les usages avancés et spécialisés des outils de l’intelligence digitale en s’assurant de leur impact durable et responsable
Learning objectives
- 1.1 - Develop digital citizenship and prosperity
- 1.2 - Develop digital creativity for the individual and the organizational
- 1.3 - Develop competitiveness in business, and digital sovereignty
Course Learning objectives
A L'ISSU DE CE COURS, CHAQUE ÉTUDIANT SERA CAPABLE DE :
Bloc de compétences 1 : « Être capable d’étendre sa propre intelligence digitale à travers ses différentes dimensions (de manière responsable et durable) ». Compétences associées : 1.1 Développer la citoyenneté numérique et la prospérité sociétale ; 1.2 Développer la créativité numérique pour l’individu et l’organisation avec sobriété ; 1.3 Développer la compétitivité des entreprises et la souveraineté numérique.
Expliquer le fonctionnement d’un LLM, d’un harnais IA et les limites de la fenêtre de contexte (Bloom 2 — Comprendre).
Schématiser et comparer les composants d’une architecture web (front-end, back-end, base de données, API, WebSocket) afin de justifier un choix technique (Bloom 4 — Analyser ; 5 — Évaluer).
Utiliser des sources financières identifiables, contrôler les chiffres et organiser un tableau de données traçables (Bloom 3 — Appliquer).
Analyser et comparer les transformations stratégiques de deux organisations confrontées à une rupture numérique, en reliant événements, indicateurs et états financiers (Bloom 4 — Analyser).
Concevoir et produire un récit animé, un rapport d’analyse stratégique et financière et une application web avec espace authentifié (Bloom 6 — Créer).
Concevoir et justifier un enchaînement structuré de prompts permettant de décomposer un objectif, exploiter les résultats intermédiaires et obtenir un livrable final (Bloom 6 — Créer ; 5 — Évaluer).
[EN]: Competency Block 1: “Be able to expand one’s own digital intelligence across its various dimensions (in a responsible and sustainable manner).”
Related competencies: 1.1 Foster digital citizenship and societal prosperity; 1.2 Develop digital creativity for individuals and organizations in a measured way; 1.3 Enhance business competitiveness and digital sovereignty.
Explain how an LLM and an AI framework work, as well as the limitations of the context window (Bloom Level 2 — Understand).
Diagram and compare the components of a web architecture (front-end, back-end, database, API, WebSocket) to justify a technical choice (Bloom Level 4 — Analyze; Level 5 — Evaluate).
Use identifiable financial sources, verify figures, and organize a table of traceable data (Bloom Level 3 — Apply).
Analyze and compare the strategic transformations of two organizations facing digital disruption, linking events, indicators, and financial statements (Bloom Level 4 — Analyze).
Design and produce an animated narrative, a strategic and financial analysis report, and a web application with a secure login area (Bloom 6 — Create).
Design and justify a structured sequence of prompts that breaks down an objective, leverages intermediate results, and produces a final deliverable (Bloom 6 — Create; 5 — Evaluate).
Content : structure and schedule
Tuesday — Technical Foundations & First Interface
Morning (First Code & LLMs): Launch of the main project thread focused on sector transformation. Hands-on setup with the working environment (editor, terminal, AI harness). Discovery of LLM core concepts (context, inference, limitations, hallucinations) and role distinctions (model, agent, tool, harness). Workshop: creation, local execution, and debugging of a minimal web page, with awareness of common errors and data privacy.
Afternoon (Architectures & Data): Data flow modeling (front-end, back-end, database) and software architecture comparisons (HTML/JS, React, Node/Python, SQLite vs. server databases). Utilization of MCP (Model Context Protocol) and management of permissions/secrets. Workshop: integration of a public dataset (CSV, API) to display it on an interface. (Assessment: Individual Assignment #1 — 10%).
Wednesday — Context Management & Animated Financial Analysis
Morning (Context Window & Data Collection): Handling context saturation, prompt framing files, and cost management (tokens). Work organization methodology broken into manageable tasks. Workshop: extraction of key financial data (revenue, net income, cash flow) from official annual reports and structuring into a traceable, sourced table.
Afternoon (Vibe Coding & Strategy): Analysis of strategic disruptions and modeling their impact on financial metrics. Workshop: generation of an animated timeline (web video/vibe coding) combining charts, key events, and quotes from reports. (Assessment: Individual Assignment #2 — 10%).
Thursday — Comparative Analysis & Secure Web Application
Morning (Comparative Study & Time Series): Analysis of before/after disruption financial series using a comparative matrix (distribution, recurring revenue, profitability). Workshop: harmonizing data series, generating charts/ratios, and drafting findings alongside methodological assumptions.
Afternoon (Analyst Report & Authentication): Structured drafting of an analyst note. Workshop: building and deploying a full website featuring the report, data, and animations, while securing access to private pages via an authentication service. (Assessment: Individual Assignment #3 — 10%).
Friday — Deep Dive & Practical Exam
Morning (Automation & Retrospective): Review of the week's assignments. Introduction to process automation (cron jobs, triggers) combining deterministic code and AI harness calls. (Deep-dive session not evaluated in the final exam).
Afternoon (Individual Final Exam): Assessment based on a new case study. Independent work relying solely on skills acquired from Tuesday to Thursday: prompt engineering, strategic/financial analysis, and a standalone digital deliverable accompanied by the chronological prompt log.
Sustainable Development Goals
Le cours aborde la confidentialité des documents, les droits d’utilisation, les biais et hallucinations, la sécurité des accès, la sobriété en tokens et le choix proportionné des outils. Contributions pédagogiques proposées : ODD 8 (productivité et compétences), ODD 9 (innovation et infrastructures numériques) et ODD 12 (usage raisonné des ressources). Ces contributions restent à valider selon les règles de reporting IMT-BS.
[EN]: The course covers document confidentiality, usage rights, biases and hallucinations, access security, token efficiency, and the proportionate selection of tools. Proposed educational contributions: SDG 8 (productivity and skills), SDG 9 (innovation and digital infrastructure), and SDG 12 (responsible use of resources). These contributions are subject to validation in accordance with IMT-BS reporting rules.
Number of SDG's addressed among the 17
3
Learning delivery
synchrone
Pedagogical methods
Alternance de démonstrations courtes, ateliers guidés, analyses de sources, exercices individuels et corrections collectives. Un seul travail individuel est remis et évalué en fin de journée le mardi, le mercredi et le jeudi. Les activités du matin préparent la production du soir. Ces trois jalons permettent de mesurer la progression avant l’examen final du vendredi après-midi.
[EN]: A mix of short demonstrations, guided workshops, source analysis, individual exercises, and group feedback sessions. One individual assignment is submitted and graded at the end of the day on Tuesday, Wednesday, and Thursday. The morning activities prepare students for the evening’s work. These three milestones allow us to assess progress before the final exam on Friday afternoon.
Evaluation and grading system and catch up exams
Continuous assessment: three individual assignments due at the end of each day (Tuesday, Wednesday, Thursday), each accounting for 10%, for a total of 30% of the final grade. Punctuality, participation, and attitude: 10%. Final individual practical exam on Friday afternoon: 60%. The exam covers a simpler case than the main theme and does not include the new features covered on Friday morning. Daily assignments are used to measure progress in skill development.
Assessment rubric for practical assignments and the exam: sequence of prompts (50%)—breakdown, contextualization, sequencing, use of intermediate results, and iterations; strategic and financial analysis (20%); production of the final deliverable (30%). These internal criteria are applied to each practical assignment within the overall weightings of 35% / 10% / 55%.
The final grade may be adjusted based on individual participation (lateness, unexcused absences, etc.).
Resit (CF2): Theory questions / Mini case study. The grade is capped at 10/20.
In the event of failure to pass the resit (CF2), an extension of studies may be granted by the academic board.
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.
Learners are expected to strictly adhere to the class schedule provided in their timetable.
● 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.
Each half-day of unexcused absence or lateness will result in a penalty of -1 point on the final course grade.
● 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
Provost, F., & Fawcett, T. (2013). Data science for business: What you need to know about data mining and data-analytic thinking. O'Reilly Media.
Davenport, T. H., & Harris, J. G. (2007). Competing on analytics: The new science of winning. Harvard Business School Press.
Keywords
Data-Driven Strategy , Strategic Analytics , Business Intelligence , Disruption, Forecasting , Competitive Data Analysis
Prerequisites
Connaissances de base en stratégie et lecture d’états financiers ; maîtrise courante d’un navigateur et de fichiers. Aucune expérience avancée en programmation n’est exigée. Un ordinateur portable et les accès logiciels requis seront nécessaires ; les outils précis et modalités d’accès seront communiqués avant le cours. [EN]: Basic knowledge of strategy and reading financial statements; basic proficiency with a web browser and files. No advanced