Refactor: Split portfolio to projects and writings sections, and update content structure

- Renamed 'portfolio' collection to 'projects' in content configuration.
- Introduced a new 'writings' collection with corresponding schema.
- Updated README to reflect changes in content structure and navigation.
- Removed the old portfolio page and added new pages for projects and writings.
- Added multiple new project and writing markdown files with relevant content.
- Updated license year to 2025.
- Enhanced AppHeader for new navigation links.
- Improved ProseImg component styling.

content/projects/studies.md

@@ -0,0 +1,44 @@
+---
+slug: studies
+title: 🎓 Studies projects
+description:  A collection of projects done during my studies.
+publishedAt: 2023/09/01
+readingTime: 1
+tags:
+  - data
+  - python
+  - 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_.
+
+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.
+
+File structure:
+- `L3`
+  - `Analyse Matricielle`
+  - `Analyse Multidimensionnelle`
+  - `Calculs Numériques`
+  - `Equations Différentielles`
+  - `Méthodes Numériques`
+  - `Probabilités`
+  - `Projet Numérique`
+  - `Statistiques`
+- `M1`
+  - `Data Analysis`
+  - `General Linear Models`
+  - `Monte Carlo Methods`
+  - `Portfolio Management`
+
+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 🧠