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Arthur DANJOU
81814b507e
feat: add new homelab project documentation with details on services and hardware fix: correct project code links for Monte Carlo Project and Schelling Segregation Model refactor: rename Studies Projects to ArtStudies for better clarity and consistency i18n: add project descriptions in English, Spanish, and French locales chore: update package name to artsite and adjust dependency versions style: add cover image for ArtLab project fix: update chat message labels for better readability and translation usage
56 lines
2.9 KiB
Markdown
56 lines
2.9 KiB
Markdown
---
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slug: artstudies
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title: 🎓 ArtStudies
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description: A curated collection of mathematics and data science projects developed during my academic journey.
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publishedAt: 2023/09/01
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readingTime: 1
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favorite: true
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tags:
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- data
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- python
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- r
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---
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[ArtStudies](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.
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The projects are organized into two main sections:
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- **L3** – Third year of the Bachelor's degree in Mathematics
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- **M1** – First year of the Master's degree in Mathematics
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## 📁 File Structure
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- `L3`
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- `Analyse Matricielle`
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- `Analyse Multidimensionnelle`
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- `Calculs Numériques`
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- `Équations Différentielles`
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- `Méthodes Numériques`
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- `Probabilités`
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- `Projet Numérique`
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- `Statistiques`
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- `M1`
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- `Data Analysis`
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- `General Linear Models`
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- `Monte Carlo Methods`
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- `Numerical Methods`
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- `Numerical Optimization`
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- `Portfolio Management`
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- `Statistical Learning`
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## 🛠️ Technologies & Tools
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- [Python](https://www.python.org): A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing.
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- [R](https://www.r-project.org): A statistical computing environment, perfect for data analysis and visualization.
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- [Jupyter](https://jupyter.org): Interactive notebooks combining code, results, and rich text for reproducible research.
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- [Pandas](https://pandas.pydata.org): A data manipulation library providing data structures and operations for manipulating numerical tables and time series.
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- [NumPy](https://numpy.org): Core package for numerical computing with support for large, multi-dimensional arrays and matrices.
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- [SciPy](https://www.scipy.org): A library for advanced scientific computations including optimization, integration, and signal processing.
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- [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.
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- [TensorFlow](https://www.tensorflow.org): A comprehensive open-source framework for building and deploying machine learning and deep learning models.
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- [Matplotlib](https://matplotlib.org): A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python.
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- [RMarkdown](https://rmarkdown.rstudio.com): A dynamic tool for combining code, results, and narrative into high-quality documents and presentations.
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- [FactoMineR](https://factominer.free.fr/): An R package focused on multivariate exploratory data analysis (e.g., PCA, MCA, CA).
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- [ggplot2](https://ggplot2.tidyverse.org): A grammar-based graphics package for creating complex and elegant visualizations in R.
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- and my 🧠.
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