---
slug: artstudies
title: 🎓 ArtStudies
description: A curated collection of mathematics and data science projects developed during my academic journey.
publishedAt: 2023/09/01
readingTime: 1
favorite: true
tags:
  - data
  - python
  - r
---

# ArtStudies

[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

- `L3`
  - `Analyse Matricielle`
  - `Analyse Multidimensionnelle`
  - `Calculs Numériques`
  - `Équations Différentielles`
  - `Méthodes Numériques`
  - `Probabilités`
  - `Projet Numérique`
  - `Statistiques`

- `M1`
  - `Data Analysis`
  - `General Linear Models`
  - `Monte Carlo Methods`
  - `Numerical Methods`
  - `Numerical Optimization`
  - `Portfolio Management`
  - `Statistical Learning`

- `M2`
  - `Data Visualisation`
  - `Deep Learning`
  - `Linear Models`
  - `Machine Learning`
  - `VBA`
  - `SQL`

## 🛠️ 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.
- and my 🧠.
