From cd139460410eebd27e378c60ebd2f8468ad50a3d Mon Sep 17 00:00:00 2001 From: Arthur DANJOU Date: Mon, 9 Feb 2026 11:54:33 +0100 Subject: [PATCH] Edit readme.md file --- README.md | 140 +++++++++++++++++++++++++++++++++++------------------- 1 file changed, 91 insertions(+), 49 deletions(-) diff --git a/README.md b/README.md index dff7e03..c2c6964 100644 --- a/README.md +++ b/README.md @@ -2,63 +2,105 @@ [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. -The projects are organized into two main sections: - **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 -## 📁 File Structure +## 📁 Project Structure -- `L3` - - `Analyse Matricielle` - - `Analyse Multidimensionnelle` - - `Calculs Numériques` - - `Équations Différentielles` - - `Méthodes Numériques` - - `Probabilités` - - `Projet Numérique` - - `Statistiques` +### L3 - Bachelor's Degree -- `M1` - - `Data Analysis` - - `General Linear Models` - - `Monte Carlo Methods` - - `Numerical Methods` - - `Numerical Optimization` - - `Portfolio Management` - - `Statistical Learning` +| 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 | -- `M2` - - `Clustering In Practice` - - `Data Visualisation` - - `Deep Learning` - - `Generative AI` - - `Linear Models` - - `Machine Learning` - - `Reinforcement Learning` - - `SQL` - - `Statistiques Non Paramétrique` - - `Unsupervised Learning` - - `VBA` +### 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](https://www.python.org): A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing. -- [R](https://www.r-project.org): A statistical computing environment, perfect for data analysis and visualization. -- [Jupyter](https://jupyter.org): Interactive notebooks combining code, results, and rich text for reproducible research. -- [Pandas](https://pandas.pydata.org): A data manipulation library providing data structures and operations for manipulating numerical tables and time series. -- [NumPy](https://numpy.org): Core package for numerical computing with support for large, multi-dimensional arrays and matrices. -- [SciPy](https://www.scipy.org): A library for advanced scientific computations including optimization, integration, and signal processing. -- [Scikit-learn](https://scikit-learn.org): A robust library offering simple and efficient tools for machine learning and statistical modeling, including classification, regression, and clustering. -- [TensorFlow](https://www.tensorflow.org): A comprehensive open-source framework for building and deploying machine learning and deep learning models. -- [Keras](https://keras.io): A high-level neural networks API, running on top of TensorFlow, designed for fast experimentation. -- [Matplotlib](https://matplotlib.org): A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python. -- [Plotly](https://plotly.com): An interactive graphing library for creating dynamic visualizations in Python and R. -- [Seaborn](https://seaborn.pydata.org): A statistical data visualization library built on top of Matplotlib, providing a high-level interface for drawing attractive and informative graphics. -- [RMarkdown](https://rmarkdown.rstudio.com): A dynamic tool for combining code, results, and narrative into high-quality documents and presentations. -- [FactoMineR](https://factominer.free.fr/): An R package focused on multivariate exploratory data analysis (e.g., PCA, MCA, CA). -- [ggplot2](https://ggplot2.tidyverse.org): A grammar-based graphics package for creating complex and elegant visualizations in R. -- [RShiny](https://shiny.rstudio.com): A web application framework for building interactive web apps directly from R. -- [LangChain](https://langchain.com): A framework for developing applications powered by language models. -- and my 🧠. +### 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: + ```bash + git clone + cd studies + ``` + +2. Set up the Python environment: + ```bash + uv sync + ``` + +3. Run the linter: + ```bash + ruff check . + ``` + +4. Format code: + ```bash + 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