Code
MXUE QUA 9001
Discipline
Techniques quantitatives
Langue
Anglais/English
Crédits ECTS
3
Heures programmées
25
Charge totale étudiant
60
Coordonnateur(s)
Département
- Data analytics, Économie et Finances
Equipe pédagogique
Introduction au module
This class deepens students’ technical understanding of evidence-informed decision making and the evaluation of digital and AI-enabled initiatives in organizational and societal contexts. Students are introduced to practical approaches for measuring impact as well as techniques for communicating results to decision-makers. They learn how business intelligence tools and evaluation frameworks can be combined to guide strategic and operational choices. The week emphasizes translating data and evidence into actionable insights, enabling students to assess whether digital innovations deliver their intended economic, social, and environmental outcomes.
Objectifs d'apprentissage
- 1 - Être capable d'étendre sa propre intelligence digitale à travers ses différentes dimensions (de manière responsable et durable)
- 2 - Produire et mobiliser des savoirs hautement spécialisés, issus d'une réflexion critique, et dans un champ d'expertise
- 2.1 - Développer une conscience critique des savoirs hautement spécialisés, dont certains sont à l'avant garde du savoir, en vue de formuler des contributions novatrices à des problématiques complexes, en cohérence avec le plan stratégique des organisatio
- 2.3 - Conduire une analyse réflexive et distanciée prenant en compte les enjeux, les problématiques et la complexité d'une demande ou d'une situation afin de proposer des solutions adaptées et/ou innovantes en respect des évolutions de la règlementation.
- 3 - Communiquer stratégiquement dans des environnements globaux et multiculturels
- 4 - Conduire des projets de transformation sociétale, numérique, énergétique et/ou environnementale des organisations
- 4.2 - Conduire un projet complexe en responsabilité, dont l'objectif est d'accompagner le transformation des organisations (conception, pilotage, coordination d'équipe, mise en oeuvre et gestion, contrôle, dissémination), en mobilisant des compétences plu
- 5 - Élaborer une vision stratégique et innovante, en s'appuyant sur les potentiels de l'intelligence digitale et sur un écosystème favorable
- 6 - Concevoir et/ou piloter des solutions de gestion innovantes en veillant à garantir une création de valeur soutenable pour toutes les parties prenantes
- 6.1 - Concevoir, développer et appliquer des politiques et pratiques propices au dynamisme de l'organisation, pour résoudre des problématiques repérées, en intégrant les spécificités du contexte métier.
- 6.2 - Optimiser l'usage d'outils adaptés aux différents domaines de gestion, et définir et interpréter les KPI pertinents, afin de mesurer et garantir une création de valeur durable et soutenable pour toutes les parties prenantes.
Contenu : structure du module et agenda
5 Days of the second week of summer school.
Day 1: Morning Lecture Intro to Evidence-Informed Decision Making. Afternoon Company Visit
Day 2: Morning Lecture Experiments & AB Testing. Afternoon Student project session Hands-on lab and Project work
Day 3: Morning Lecture Impact Measurement & Communication. Afternoon: Student project session: Presentations
Day 4 and Day 5: Entrepreneurship Challenge
Contribution à l'atteinte des ODD (Objets du Développement Durable)
ODD 4 — Quality Education: The week builds rigorous analytical skills — causal inference, experimental design, impact measurement — that are directly applicable across professional contexts and sectors.
ODD 11 — Sustainable Cities and Communities: Two of the three case study examples (city energy consumption, carbon tax) directly address urban and environmental sustainability challenges.
ODD — Climate Action: Impact evaluation frameworks are applied to environmental initiatives, equipping students to assess whether sustainability policies actually work.
Nombre d'ODD abordés parmi les 17
3
Apprentissage
synchrone
Méthode pédagogique
Interactive lectures with case illustrations, Hands-on lab (A/B testing in Python/Excel), Company visit, Group project (evaluation framework), Group presentations, Entrepreneurship challenge (problem framing, pitching)
Système de notation et modalités de rattrapage
Class activities (40%)
Applied exercises / short tests (20%)
Student Presentations (40%)
Catch up exam: individual written assignment +online oral evaluation
Règlement du module
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.
Références obligatoires et lectures suggérées
Cunningham, S. (2021). Causal Inference: The Mixtape. Yale University Press. Chapters 1-2, 6, 8-9. Available online at: mixtape.scunning.com
Angrist, J., & Pischke, J.S. (2014). Mastering Metrics. Princeton University Press. Chapter 1 (overview only).
Huntington-Klein, N. (2021). The Effect: An Introduction to Research Design and Causality. Available online at: theeffectbook.net
Kohavi, R., & Thomke, S. (2017). The surprising power of online experiments. Harvard Business Review, September-October 2017.
Kohavi, R., Tang, D., & Xu, Y. (2020). Trustworthy Online Controlled Experiments. Cambridge University Press. Chapters 1-3.
Bojinov, I., & Gupta, S. (2022). Online experimentation: Benefits, operational and methodological challenges, and scaling guide. Harvard Data Science Review. Available at: hdsr.mitpress.mit.edu
Gertler, P. et al. (2016). Impact Evaluation in Practice (2nd ed.). World Bank. Available at: worldbank.org
Mots-clés
Causal analysis, entrepreneurial challenge
Prérequis
INF 9001