Code
MUSF QUA 2203
Level
L2
Field
Techniques quantitatives
Language
Anglais/English
ECTS Credits
1
Class hours
20
Total student load
20
Program Manager(s)
Department
- Data analytics, Économie et Finances
- Service Bachelor
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.
Content : structure and schedule
Note: the order of sessions and title names may be subject to change at the beginning of the course.
Session 1 : Fundamentals of data manipulation (3h)
- Introduction to Python and Jupyter Notebooks
- Dataframe and basic operations with Pandas (loading CSV, filtering, grouping)
- Data cleaning techniques (handling missing values, type conversions)
Session 2 : Data visualization and correlation matrices (3h)
- Introduction to Matplotlib (line plots, bar charts, scatter plots)
- Advanced visualizations with Seaborn (heatmaps...)
- Correlation matrices and NumPy interpretation
Session 3 : Simple linear regression (3h)
- Simple linear regression with sci-kit learn (train/test splitting, model fitting)
- Understanding the model equation (variables, coefficients)
- Evaluating model performance (R-squared, MSE)
Session 4 : Business applications (3h)
- Analyzing a Kaggle dataset
- Applying regression analysis to business case studies
- Presenting concrete insights
Sustainable Development Goals
Goal 4 : Quality Education – Ensure inclusive and quality education and promote lifelong learning
opportunities for all : compulsory.
Goal 9 : Industry, Innovation and Infrastructure – Build resilient infrastructure, promote sustainable
industrialization and foster innovation : strongly recommended.
Number of SDG's addressed among the 17
2
Learning delivery
synchrone
Pedagogical methods
This course will be divided in 4 learning units spread over 6 weeks
Classes are 3 hours with a 15 minutes half time break
Evaluation and grading system and catch up exams
Final test (table-top Multiple Choice Questions) = 100%
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 knwoledge of Python syntax (loops, functions and variables), Familiarity with basic mathematics (algebra, statistics).