Descriptif
This course introduces Python as a tool for data science, progressing from programming foundations to machine learning and practical applications.
The first part covers Python fundamentals, data structures, control flow, functions and classes, NumPy and PyTorch, Pandas, data preparation, visualization, exploratory data analysis, and basic statistics.
The second part introduces the main machine learning approaches, including regression, classification, model evaluation, clustering, and dimensionality reduction.
The final part explores applications to image processing, neural networks and convolutional neural networks, time-series forecasting, and natural language processing.
The course is organized over 14 weeks, with a two-hour lecture and a two-hour practical session each week.
- Python Fundamentals
- Data Structures and Control Flow
- Functions and Reusable Code
- Classes
- NumPy and PyTorch
- Pandas and Data Preparation
- Data Visualization, Exploratory Data Analysis, Statistics and Probability
- Machine Learning Workflow and Regression
- Classification and Model Evaluation
- Clustering and Dimensionality Reduction
- Image Processing
- Neural Networks and Convolutional Neural Networks
- Time-Series Forecasting
- Natural Language Processing
Objectifs pédagogiques
By the end of the course, students will be able to write structured Python programs, manipulate and visualize data, apply and evaluate the main machine learning methods, and use Python to address practical data science problems.
Pour les étudiants du diplôme M1 AMS - Mathématiques Appliquées et Statistiques
No specific prerequisites. No prior knowledge of Python or programming is required.
Format des notes
Numérique sur 20Littérale/grade américainPour les étudiants du diplôme M1 AMS - Mathématiques Appliquées et Statistiques
Vos modalités d'acquisition :
The final grade is based on:
- a graded multiple-choice quiz during the first part of the course;
- a graded practical assignment during the second part of the course;
- a final paper-based written examination.
The respective weights of these assessments will be specified at the beginning of the course.
The course unit is passed if the final grade is greater than or equal to 10/20. It awards 6 ECTS credits.
A retake examination is permitted. The grade obtained in the retake examination is retained and replaces the initial grade.
Le rattrapage est autorisé (Note de rattrapage conservée)- le rattrapage est obligatoire si :
- Note initiale < 7
- le rattrapage peut être demandé par l'étudiant si :
- Note initiale < 7
- Crédits ECTS acquis : 6 ECTS
La note obtenue rentre dans le calcul de votre GPA.