artsite/content/projects/artstudies.md

slug, title, type, description, shortDescription, publishedAt, readingTime, favorite, status, tags, icon

slug	title	type	description	shortDescription	publishedAt	readingTime	favorite	status	tags	icon
artstudies	ArtStudies - Academic Projects Collection	Academic Project	A curated collection of mathematics and data science projects developed during my academic journey, spanning Bachelor's and Master's studies.	A collection of academic projects in mathematics and data science from my university studies.	2023-09-01	1	true	In progress	Python R Data Science Mathematics	i-ph-book-duotone

ArtStudies Projects is a curated collection of academic projects completed throughout my mathematics studies. The repository showcases work in both Python and R, with a focus on mathematical modeling, data analysis, and numerical methods.

The projects are organized into three 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

• 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

• M2

  • Data Visualisation
  • Deep Learning
  • Linear Models
  • Machine Learning
  • VBA
  • SQL

🛠️ 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.