Update project descriptions for ArtHome, ArtSite, and Studies to enhance clarity and consistency

content/projects/studies.md

@@ -1,7 +1,7 @@
 ---
 slug: studies
-title: 🎓 Studies projects
-description:  A collection of projects done during my studies.
+title: 🎓 Studies Projects
+description: A curated collection of mathematics and data science projects developed during my academic journey.
 publishedAt: 2023/09/01
 readingTime: 1
 favorite: true
@@ -11,35 +11,45 @@ tags:
   - r
 ---
 
-[Studies projects](https://github.com/ArthurDanjou/studies) is a collection of mathematics projects done during my studies. It includes projects in _Python_ and in _R_.
+[Studies Projects](https://github.com/ArthurDanjou/studies) 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 divided into two main categories: _L3_ and _M1_, corresponding to the third year of the bachelor's degree and the first year of the master's degree in mathematics.
+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
+
+## 📁 File Structure
 
-File structure:
 - `L3`
   - `Analyse Matricielle`
   - `Analyse Multidimensionnelle`
   - `Calculs Numériques`
-  - `Equations Différentielles`
+  - `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`
 
-Made with:
-- [Python](https://www.python.org): Python is an interpreted, high-level and general-purpose programming language.
-- [R](https://www.r-project.org): R is a programming language and free software environment for statistical computing and graphics.
-- [Jupyter](https://jupyter.org): Jupyter is a free, open-source, interactive web tool known as a computational notebook, which researchers can use to combine software code, computational output, explanatory text and multimedia resources in a single document.
-- [Pandas](https://pandas.pydata.org): Pandas is a fast, powerful, flexible and easy to use open source data analysis and data manipulation library built on top of the Python programming language.
-- [Numpy](https://numpy.org): NumPy is the fundamental package for scientific computing in Python.
-- [Scipy](https://www.scipy.org): SciPy is a free and open-source Python library used for scientific and technical computing.
-- [Matplotlib](https://matplotlib.org): Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
-- [RMarkdown](https://rmarkdown.rstudio.com): R Markdown is an authoring framework for data science. You can use a single R Markdown file to save and execute code and generate high-quality reports that can be shared with an audience.
-- [FactoMineR](https://factominer.free.fr/): FactoMineR is an R package dedicated to multivariate exploratory data analysis.
-- [ggplot2](https://ggplot2.tidyverse.org): ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics.
-- and my 🧠
+## 🛠️ 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.
+- [Matplotlib](https://matplotlib.org): A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python.
+- [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.
+- and my 🧠.