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Revise ArtStudies project details and technologies

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Updated project description and added M2 section with new technologies.
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Arthur Danjou
2025-10-03 11:33:25 +02:00
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@@ -11,11 +11,14 @@ tags:
- r - r
--- ---
[ArtStudies](https://go.arthurdanjou.fr/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. # 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: The projects are organized into two main sections:
- **L3** – Third year of the Bachelor's degree in Mathematics - **L3** – Third year of the Bachelor's degree in Mathematics
- **M1** – First year of the Master'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 ## 📁 File Structure
@@ -38,6 +41,10 @@ The projects are organized into two main sections:
- `Portfolio Management` - `Portfolio Management`
- `Statistical Learning` - `Statistical Learning`
- `M2`
- `Machine Learning`
- `SQL`
## 🛠️ Technologies & Tools ## 🛠️ Technologies & Tools
- [Python](https://www.python.org): A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing. - [Python](https://www.python.org): A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing.
@@ -49,6 +56,8 @@ The projects are organized into two main sections:
- [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. - [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. - [TensorFlow](https://www.tensorflow.org): A comprehensive open-source framework for building and deploying machine learning and deep learning models.
- [Matplotlib](https://matplotlib.org): A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python. - [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. - [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). - [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. - [ggplot2](https://ggplot2.tidyverse.org): A grammar-based graphics package for creating complex and elegant visualizations in R.