Studies projects

Studies projects 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: Python is an interpreted, high-level and general-purpose programming language.
• R: R is a programming language and free software environment for statistical computing and graphics.
• Jupyter: 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: 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: NumPy is the fundamental package for scientific computing in Python.
• Scipy: SciPy is a free and open-source Python library used for scientific and technical computing.
• Matplotlib: Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
• RMarkdown: 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: FactoMineR is an R package dedicated to multivariate exploratory data analysis.
• ggplot2: ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics.
• and my 🧠