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

1. Clone the repository:

   git clone <repository-url>
   cd studies
   

2. Set up the Python environment:

   uv sync
   

3. Run the linter:

   ruff check .
   

4. Format code:

   ruff format .
   

📝 Notes

• Some subprojects have isolated pyproject.toml files (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