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Arthur DANJOU
8cf328e18a
- Updated import order in Point_Fixe.ipynb for consistency. - Changed lambda functions to regular function definitions for clarity in Point_Fixe.ipynb. - Added numpy import in TP1_EDO_EulerExp.ipynb, TP2_Lokta_Volterra.ipynb, and TP3_Convergence.ipynb for better readability. - Modified for loops in TP1_EDO_EulerExp.ipynb and TP2_Lokta_Volterra.ipynb to include strict=False for compatibility with future Python versions.
Studies
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
-
L3Analyse MatricielleAnalyse MultidimensionnelleCalculs NumériquesÉquations DifférentiellesMéthodes NumériquesProbabilitésProjet NumériqueStatistiques
-
M1Data AnalysisGeneral Linear ModelsMonte Carlo MethodsNumerical MethodsNumerical OptimizationPortfolio ManagementStatistical 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 🧠.