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3.8 KiB
ArtStudies
ArtStudies Projects is a curated collection of academic projects completed throughout my mathematics studies. The repository showcases work in both Python and R, focusing on mathematical modeling, data analysis, and numerical methods.
- L3 – Third year of the Bachelor's degree in Mathematics
- M1 – First year of the Master's degree in Mathematics
- M2 – Second year of the Master's degree in Mathematics
📁 Project Structure
L3 - Bachelor's Degree
| Course | Description |
|---|---|
Analyse Matricielle |
Matrix analysis and numerical linear algebra |
Analyse Multidimensionnelle |
Multivariate data analysis (PCA, MCA, CA) |
Calculs Numériques |
Numerical computation methods |
Equations Différentielles |
Differential equations solving |
Méthodes Numériques |
Numerical methods implementation |
Projet Numérique |
Numerical project |
Statistiques |
Applied statistics |
M1 - Master's Degree 1st Year
| Course | Description |
|---|---|
Data Analysis |
Exploratory data analysis and visualization |
General Linear Models |
Regression and ANOVA models |
Monte Carlo Methods |
Statistical simulation techniques |
Numerical Methods |
Numerical algorithms implementation |
Numerical Optimisation |
Optimization algorithms |
Portfolio Management |
Financial portfolio optimization |
Statistical Learning |
Machine learning fundamentals |
M2 - Master's Degree 2nd Year
| Course | Description |
|---|---|
Advanced Machine Learning |
Advanced ML techniques |
Classification and Regression |
Supervised learning methods |
Clustering In Practice |
Unsupervised learning and clustering |
Data Visualisation |
Data visualization principles and tools |
Deep Learning |
Neural networks and deep architectures |
Enjeux Climatiques |
Climate issues and data analysis |
Generative AI |
Generative models (LLMs, diffusion) |
Linear Models |
Linear modeling techniques |
Machine Learning |
Core machine learning algorithms |
Reinforcement Learning |
Reinforcement learning algorithms |
SQL |
Database and SQL queries |
Statistiques Non Paramétrique |
Non-parametric statistics |
Time Series |
Time series analysis and forecasting |
Unsupervised Learning |
Unsupervised learning methods |
VBA |
Visual Basic for Applications |
🛠️ Technologies & Tools
Python
- Data Science:
numpy,pandas,scipy,matplotlib,seaborn,plotly,geopandas - Machine Learning:
scikit-learn,xgboost,catboost,shap,umap-learn,imblearn - Deep Learning:
tensorflow,keras,torch,accelerate - LLM/RAG:
langchain,langchain-community,sentence-transformers,faiss-cpu - Other:
statsmodels,plotly,polars,requests,openpyxl
R
- Core: tidyverse, ggplot2, FactoMineR, caret, glmnet
- Shiny: RShiny for interactive web applications
- Reporting: RMarkdown for reproducible reports
Tools
- Jupyter – Interactive notebooks for reproducible research
- RStudio – R development environment
- uv – Fast Python package manager and virtual environment
- ruff – Python linter and formatter
- lintr – R linter
🚀 Getting Started
-
Clone the repository:
git clone <repository-url> cd studies -
Set up the Python environment:
uv sync -
Run the linter:
ruff check . -
Format code:
ruff format .
📝 Notes
- Some subprojects have isolated
pyproject.tomlfiles (e.g.,M2/Reinforcement Learning/project/) - Large datasets are not versioned—download via notebook code when needed
- Course materials and documentation are primarily in French