Code
MUSF QUA 2201
Level
L2
Field
Techniques quantitatives
Language
Anglais/English
ECTS Credits
4
Class hours
20
Total student load
80
Program Manager(s)
Department
- Data analytics, Économie et Finances
Educational team
Introduction to the module
This module gives students basic knowledge in probability and statistics useful for other courses (management, marketing, and finance etc.). By the end of the course, students will understand and be able to apply statistical concepts in business contexts, analyse data, interpret and effectively communicate the findings. They will develop analytical skills to become proactive, open-minded managers who can use data for decision-making and solve business problems by identifying needs and applying appropriate statistical tools.
Learning objectives
- 2.2 - Collecter, analyser et synthétiser des données, en garantissant l’intégrité, la traçabilité et la fiabilité du processus de production des données, afin de produire des informations exploitables pour la prise de décision.
Course Learning objectives
— (CPS2) Analytical thinking: The ability to think critically, to break down complex information, ideas, or problems into smaller, more manageable parts in order to better understand them and develop solutions.
— (CDK01) Conduct qualitative & quantitative studies/analyses supported by relevant data
— (CDK41) Perform, interpret, and communicate the results of econometric analyses of economic data, machine learning methods, and social network analysis methods.
— (XK03) Démontrer une compréhension du calcul et les bases des méthodes quantitatives
Content : structure and schedule
Chapter 1 introduces the concepts of descriptive statistics. It is structured as follows:
1.1 Vocabulary and representation of statistical variables
1.2 Describing a variable: statistical graphs, mean and spread
1.3 Describing a relationship: correlation and scatterplot
Chapter 2 deals with elementary probability concepts. It is organized as follows:
2.1 Introduction - Vocabulary
2.2 Algebra of events
2.3 Definition of a probability.
2.4 Combinatorics (or enumeration).
2.5 Conditional probabilities.
Chapter 3 deals with the notions and concepts of Random Variables. It is structured around the following modules:
3.1 Notion of random variable.
3.2 Usual discrete laws.
3.3 Usual density laws.
Chapter 4 deals with estimation methods and statistical test theory. More specifically, confidence interval estimation - Conformity testing. It is organized into the following 4 sections:
4.1 Estimation.
4.2 Hypothesis testing.
4.3 Proportion (or frequency) case.
4.4 The case of an average.
Chapter 5 deals with tests for comparing two populations.
5.1 Comparing proportions
5.2 Comparing means
5.3 Student's t-test
Chapter 6 deals with analysis of variance and linear regression models
6.1 ANOVA
6.2 Linear regression
Intersession: Learners are required to do their own research and analysis of the concepts they have learned in class, as well as to carry out exercises to put the concepts they have learned into practice. Practice is very important for a thorough understanding of theoretical concepts.
Sustainable Development Goals
ODD 4 : Éducation de qualité
Le module contribue à cet objectif en apportant aux étudiants des compétences techniques et analytiques essentielles dans le domaine de la science des données. En maîtrisant les méthodes et techniques statistiques, les étudiants développent une culture numérique indispensable à leur insertion professionnelle et aux défis de l'économie moderne, favorisant ainsi une éducation de qualité et pertinente.
SDG 4: Quality Education
The module contributes to this goal by providing students with essential technical and analytical skills in the field of Data science. By mastering statistical methods and techniques, students develop 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
Mixte
Pedagogical methods
FR : Les modalités d’enseignement combinent des cours magistraux suivis de séances de travaux pratiques. La dernière partie du cours est dispensée selon un modèle d’apprentissage hybride, intégrant des supports issus de MOOC et des séances interactives en classe. Les devoirs à la maison permettent aux étudiants de consolider leurs connaissances théoriques et d'approfondir leurs compétences techniques.
EN : Teaching methods comprise lectures followed by practical sessions. The final section of the course is delivered via a blended learning model, integrating MOOC materials with interactive classroom sessions. Independent homework assignments allow students to consolidate their theoretical knowledge and technical competence.
Evaluation and grading system and catch up exams
CC1: 35 %
- pen-and-paper closed-book test
CC2: 15 %
- average of grades for 3 homework assignments
CC3: 50 %
- pen-and-paper closed-book test
In the event of failure (final grade below 10), a final catch-up test is organized, with a table-top exam that counts for 100% of the final catch-up grade.
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
Lecture suggéré (FR):
Anderson.Sweeny - Williams : Statistiques pour l'économie et la gestion, De Boeck
Suggested (EN):
Moore, D. S., McCabe, G. P., & Craig, B. A. (2021). Introduction to the Practice of Statistics (10th ed.). W.H. Freeman and Company.
Keywords
Probabilités, statistiques, variable, répartition, echantillonnage / Probability, statistics, variable, distribution, sampling.
Prerequisites
Mathématiques pour la gestion (QUA 1201-QUA 1202) / Mathematics for management (QUA 1201-QUA 1202) / Excel pour les managers (INF 1203)