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- Renamed 'portfolio' collection to 'projects' in content configuration. - Introduced a new 'writings' collection with corresponding schema. - Updated README to reflect changes in content structure and navigation. - Removed the old portfolio page and added new pages for projects and writings. - Added multiple new project and writing markdown files with relevant content. - Updated license year to 2025. - Enhanced AppHeader for new navigation links. - Improved ProseImg component styling.
2.4 KiB
2.4 KiB
slug, title, description, publishedAt, readingTime, tags
| slug | title | description | publishedAt | readingTime | tags | |||
|---|---|---|---|---|---|---|---|---|
| studies | 🎓 Studies projects | A collection of projects done during my studies. | 2023/09/01 | 1 |
|
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:
L3Analyse MatricielleAnalyse MultidimensionnelleCalculs NumériquesEquations DifférentiellesMéthodes NumériquesProbabilitésProjet NumériqueStatistiques
M1Data AnalysisGeneral Linear ModelsMonte Carlo MethodsPortfolio 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 🧠