---
slug: artstudies
title: ArtStudies - Academic Projects Collection
type: Academic Project
description: A curated collection of mathematics and data science projects developed during my academic journey, spanning Bachelor's and Master's studies.
publishedAt: 2023-09-01
readingTime: 1
favorite: true
status: In progress
tags:
  - Python
  - R
  - Data Science
  - Mathematics
icon: i-ph-book-duotone
---

[**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 three 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.