# MOOC Reproducible Research: my logbook

## Module 1: Cahier de notes, cahier de laboratoire

I have started this module on June 5.
So far, I have learned:
- What GitLab is and how to use it;
- How to create and edit Markdown files;
- How to write in Markdown using headers, lists, links, and code blocks.

I also watched the training videos provided in the Module 1,
and completed several quizzes relsted to th each thematique of this Module.

### Excercise 1 is done:

- 01-1: Found the commit for Helloworld Python
    - Commit number: `505c4e26`
    - Author of the commit: `Arnaud Legrand`

- 01-2: Created a Markdown file with all required element and compared with solution

### Excercise 2 is done:

- 2.1: Commit Jupyter notebook (toy_notebook.ipynb)
    - I have perfomed the various commits and saved the files "toy_notebook" in \Module2\exo1
    - I have also performed commit and saved the file "toy_notebook" in \Module1\exo2

- 2.2: commit committer
    - Comitter: Diana_kerimbekova (f8dc60cab5180566667b00ce62a51ae7)
    - `623c226a`: Adding the toy_notebook to \Module 1\exo2
    - `8767d70c`: Commit message
    - ...........
    - 
![Git commit graph](commiter1.jpg)
![Git commit graph](commiter2.jpg)


- 2.3: commit graph

![Git commit graph](commit_graph.jpg)

### What I learned during Mission 2:

- How to write and edit Markdown file
- How to use GitLab history
- beginnig of the work with Jupyter notebook and commited it

# Mission 2 - Module 2.

## Excercice 02 (1st part)

This task is related to Module 2 "la vitrine et l'envers du décor
: le document computationnel".

As a part of this exercise, I reproduced the given PDF document using a Jupyter notebook. I commited the completed notebiik to Gitlab, under the following part: **module2/exo1/toy_notebook_en.ipynb**.

While reproducing the PDF document in Jupyter notebook, I successfully completed the following tasks:    
- Added the main title **"À propos de pi"**
- Included the relevant mathematical formulas
- Wrote and executed the code that prints the value of $\pi$
- Added a hyperlink to the Wikipedia article on **"aiguilles de Buffon"**
- Implemented **Buffon's method** in code, displaying
- Wrote the code that display the final diagram
    
Afterwards, I compared my version with the reference solution provided on the Mooc platform. This comparison helped me identify the differences and better understand the expected structure.

*All actions have been verified and marked as completed in the Jupyter notebook and on the Mooc platform.*

## Excercise 02 (2nd part)

In this exercise, I performed a simple statistical analysis. Using the provided data, I computed the **mean**, **standard variation**, **minimum**, **median**, and **maximum** values of the dataset.

## Excercise 02 (3rd part)

Based on the dataset provided in the previous excercise (2.2), I reproduced a **sequence plot** and **a histogram** to visualize the data.