Code
MUSF QUA 2201
Level
M1
Field
Techniques quantitatives
Language
Anglais/English
ECTS Credits
4
Class hours
20
Total student load
40
Program Manager(s)
Department
- Data analytics, Économie et Finances
- Service Bachelor
Educational team
Introduction to the module
The aim of this module is to acquire the basic knowledge of probability and statistics needed to understand other courses (econometrics, marketing, accounting, etc.).
At the end of this course, the learner will have understood and been able to use the basics of probability and statistics to model in the contexts described in the introduction (econometrics, marketing, accounting) and be able to exploit the following concepts and methods for calculations: see course outline (concepts in probability / statistics).
Statistics and data analysis are a tool for decision-making => Skills acquired through the modules: Develop fundamental skills while training future managers to be open-minded, proactive and able to use information technologies in management.
- Master the basic techniques used in business management (accounting, marketing, human resources management, law, etc.) and be able to combine them to create or develop economic activities.
Each service must respond to a question or a need, and the learner will be able to identify, analyze and solve a problem using the appropriate mathematical tools.
Learning goals/Programme objectives
- LG3 Having the foundations of responsible and sustainable management
- LG4 Having access to different cross disciplinary management approaches and tools
Learning objectives/Intended learning outcomes
- 3.1 - Understand and employ basic concepts, knowledge and theories related to the discipline
- 3.4 - Select and employ judiciously appropriate techniques and tools within the discipline
Rubrics
— (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:
1.2 Vocabulary and representation of statistical variables
1.3 Parameters of a statistical variable
1.4 Bivariate statistical series
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 means. It is divided into the following three sections:
5.1 Paired or independent samples?
5.2 The case of two small paired samples.
5.3 Two independent samples.
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
Aucun car la discipline est seulement introduite et ne permet pas de traiter des sujets efficacement par des outils de statistiques et analyse de données.
Par contre, les notions traitées pourraient être utilisées pour calculer certains indicateurs comme l’index relatif à l’égalité professionnelle entre homme et femmes ; notamment dans les exercices fournis par l’enseignant et les recherches personnelles des apprenants (ODD n°5 égalité hommes/femmes ; ODD n°12 Consommation responsable : les notions pourraient être utilisées pour limiter le gaspillage alimentaire, faire de la gestion des déchets efficace et rentable, identifier les empreintes matières), …
None, as the subject is only just being introduced, and does not allow us to deal effectively with statistical tools and data analysis.
On the other hand, the concepts covered could be used to calculate certain indicators such as the index relating to professional equality between men and women; in particular in the exercises provided by the teacher and the learners' personal research (SDG n°5 gender equality; SDG n°12 responsible consumption: the concepts could be used to limit food waste, make waste management efficient and profitable, identify material footprints), ...
Learning delivery
synchrone
Pedagogical methods
Certaines séances seront divisées en demi-groupe afin de pouvoir mettre en place une modulation de la méthode pédagogique entre les différents profils d’apprenants du programme Bachelor.
Une approche analytique est utilisée dans ce cours. Les apprenants apprennent à prendre conscience de l'utilité de l'objet ou concept (par exemple la formule ou le concept qui répond aux problématiques données dans un cas concret).
Etape 1 - Appropriation du concept, le concept doit être compris.
Etape 2 - Approche par le développement des compétences suite à la connaissance théorique. Les compétences sont acquises et développées lors de travaux pratiques, études de cas... Le concept est ici mis en place et travaillé.
Le cours se présente sous la forme de cours magistraux et de TP.
Au cours de la séance, l'enseignant peut être amené à faire quelques rappels de cours, ou à donner des explications sur des points mal compris. En aucun cas, la durée de ces parties de cours ne dépassera la moitié de la séance. L'enseignant peut envoyer un apprenant au tableau pour étudier et traiter l’un des exercices à préparer pour la séance en cours.
Ce système est basé sur un travail personnel de préparation. Il n'a d'intérêt que si l'apprenant suit le rythme prévu au planning, c’est à dire s'il a effectivement étudié avant la séance le(s) chapitre(s) prévu(s), et préparé les exercices associés.
Some sessions will be divided into half-groups, so that the teaching method can be modulated to suit the different learner profiles in the Bachelor program.
An analytical approach is used in this course. Learners learn to become aware of the usefulness of the object or concept (e.g. the formula or concept that answers the given problems in a concrete case).
Stage 1 - Appropriation of the concept, the concept must be understood.
Stage 2 - Skills development approach to theoretical knowledge. Skills are acquired and developed through practical work, case studies, etc. Here, the concept is implemented and worked on.
The course takes the form of lectures and practical work.
In the course of the session, the teacher may be asked to provide a few reminders of the course, or to explain points that have been misunderstood. In no case will the duration of these parts of the lesson exceed half the session. The teacher can send a learner to the blackboard to study and complete one of the exercises to be prepared for the current session.
This system is based on personal preparation. It is only worthwhile if the learner follows the schedule, i.e. has studied the chapter(s) and prepared the associated exercises before the session.
Evaluation and grading system and catch up exams
The final grade is made up of the average of the three continuous tests carried out throughout the module. This grade may be adjusted upwards depending on the learner's attendance (presence in class, interaction with the teacher, interest in the subject).
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
Référence de base : Le polycopié du cours : « Statistiques et analyse de données»
Basic reference: Course handout: "Statistics and data analysis".
.Références complémentaires : Anderson.Sweeny - Williams : Statistiques pour l'économie et la gestion, De Boeck
Additional references: Anderson, Sweeny and Williams: Statistics for Economics and Management, De Boeck
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)