Code
MUSF QUA 2203
Level
L2
Field
Techniques quantitatives
Language
Anglais/English
ECTS Credits
3
Class hours
20
Total student load
60
Program Manager(s)
Department
- Data analytics, Économie et Finances
Educational team
Introduction to the module
This course introduced to the fundamental concepts of data manipulation and basic machine learning using Python. The module is designed for bachelor-level learners (bac +2) with minimal programming experience.
The focus will be on understanding and applying essential techniques for data analysis, visualization, and machine learning, emphasizing pratical examples and business applications.
Objectifs d'apprentissage
- 2.1 - Identify and analyze in depth problems, causes and impacts
- 4.1 - Mobiliser les méthodes issues de la recherche pour construire une analyse et produire des recommandations de qualité
- 4.3 - Apply cross-disciplinary management approaches and tools effectively and judiciously
Content : structure and schedule
Lesson 1: Panorama of AI
Lesson 2: Basic Python and Jupyter notebook.
Lesson 3: Python: Control flow, data structures, Pandas.
Lesson 4: Data visualisation with Python
Lesson 5: AI and Business transformation
Lesson 6: AI algorithms: Linear regression
Lesson 7: AI algorithms: Logistic regression
Lesson 6: AI algorithms: K-means clustering
Group presentations
Contrôle
Sustainable Development Goals
SDG 4: Quality Education
The module contributes to this goal by providing students with essential technical and analytical skills in the field of Artificial Intelligence and Data Science. By mastering tools such as Python and machine learning, students develop high-level 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
synchrone
Evaluation and grading system and catch up exams
Homework (groups): 20%
Final presentations (groups): 40%
Contrôle (individual): 40%
The catch-up exam is a table-top assignment which counts for 100% of the final 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
There is no textbook for this course.
Suggested readings will be updated on Moodle prior to the beginning of course and potentially over the four
lecture sessions.
Keywords
Python, data manipulation, Pandas, data visualization, Matplotlib, Seaborn, correlation/causation, NumPy, machine learning, simple linear regression, model evaluation, Sci-kit learn.
Prerequisites
Basic knowledge of Python syntax (loops, functions and variables), Familiarity with basic mathematics (algebra, statistics).