Arthur Danjou
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🎓 Studies Projects

01 September 2023

A curated collection of mathematics and data science projects developed during my academic journey.

Studies Projects 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:

  • L3 – Third year of the Bachelor's degree in Mathematics
  • M1 – First 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

🛠️ Technologies & Tools

  • Python: A high-level, interpreted programming language, widely used for data science, machine learning, and scientific computing.
  • R: A statistical computing environment, perfect for data analysis and visualization.
  • Jupyter: Interactive notebooks combining code, results, and rich text for reproducible research.
  • Pandas: A data manipulation library providing data structures and operations for manipulating numerical tables and time series.
  • NumPy: Core package for numerical computing with support for large, multi-dimensional arrays and matrices.
  • SciPy: A library for advanced scientific computations including optimization, integration, and signal processing.
  • Scikit-learn: A robust library offering simple and efficient tools for machine learning and statistical modeling, including classification, regression, and clustering.
  • TensorFlow: A comprehensive open-source framework for building and deploying machine learning and deep learning models.
  • Matplotlib: A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python.
  • RMarkdown: A dynamic tool for combining code, results, and narrative into high-quality documents and presentations.
  • FactoMineR: An R package focused on multivariate exploratory data analysis (e.g., PCA, MCA, CA).
  • ggplot2: A grammar-based graphics package for creating complex and elegant visualizations in R.
  • and my 🧠.
Arthur Danjou

Hi! I'm Arthur, a Master's student in Applied Mathematics with a passion for AI, statistics, and building cool things with code.

I love turning ideas into real, working systems—whether it's a machine learning model, a self-hosted service, or a data-driven project.

This project is part of my journey to explore and apply what I learn every day. I share it here hoping it'll inspire or help others, just like I've been inspired by the open-source and tech communities.

Feel free to reach out on LinkedIn or GitHub if you have questions, feedback, or just want to connect!