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Arthur DANJOU
6eecdd6ab3
- Bump Python version from 3.11 to 3.13 in .python-version file. - Reset execution counts to null in Jupyter Notebook for reproducibility. - Improve code readability by adjusting comments and formatting in the notebook. - Change the policy definition to use numpy.ndarray for better clarity. - Modify pyproject.toml to enable E501 rule for line length management.
ArtStudies
ArtStudies 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
- M2 – Second year of the Master's degree in Mathematics
📁 File Structure
-
L3Analyse MatricielleAnalyse MultidimensionnelleCalculs NumériquesÉquations DifférentiellesMéthodes NumériquesProbabilitésProjet NumériqueStatistiques
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M1Data AnalysisGeneral Linear ModelsMonte Carlo MethodsNumerical MethodsNumerical OptimizationPortfolio ManagementStatistical Learning
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M2Data VisualisationDeep LearningGenerative AILinear ModelsMachine LearningReinforcement LearningSQLStatistiques Non ParamétriqueUnsupervised LearningVBA
🛠️ 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.
- Keras: A high-level neural networks API, running on top of TensorFlow, designed for fast experimentation.
- Matplotlib: A versatile plotting library for creating high-quality static, animated, and interactive visualizations in Python.
- Plotly: An interactive graphing library for creating dynamic visualizations in Python and R.
- Seaborn: A statistical data visualization library built on top of Matplotlib, providing a high-level interface for drawing attractive and informative graphics.
- 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.
- RShiny: A web application framework for building interactive web apps directly from R.
- LangChain: A framework for developing applications powered by language models.
- and my 🧠.