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