# Journal de bord du Mooc / Mooc's logbook

# 10-11/06/2025 - Exercises from module 1

## Excercise 01-1: Gitlab search

Excercise **number 1**:

1.  Learn how to use the Gitlab interface: search for the two files containing "LE MOOC RECHERCHE REPRODUCTIBLE C'EST GENIAL" using "*Search or jump to*...". 

The answers to this questions are presented in the form of a **path relative to the repository** and are:
- *module1/exo1/aebef6b0a5.txt*
- *module1/exo1/f683bbad4b.txt*

Excercise **number 2**:

2. Learn how to find modification made in the repository by using the **GitLab history** function. To do so find the commit number and author of the commit that added the title **Helloworld Python** in the file *module1/exo2/readme.md*

The answers to this excercise are:
- commit number: 505c4e26
- author of the commit: Arnaud Legrand

## Excercise 01-02: Getting familiar with Markdown

Excercise **a.** and **b.**:

In this exercise, write for the first time a **markdown file** whose rendered version correspons to the [lms.fun-mooc.fr/](https://lms.fun-mooc.fr/asset-v1:inria+41016+self-paced+type@asset+block/module1_exo2_fichier-markdown.pdf) file. To help first time-users (like me) with the language: [lms.fun-mooc.fr/](https://lms.fun-mooc.fr/courses/course-v1:inria+41016+self-paced/courseware/2bfe60a86fed4994b5493a220c38eb69/13f6fd96266746a0bd9d717a12f1f835/4?activate_block_id=block-v1%3Ainria%2B41016%2Bself-paced%2Btype%40vertical%2Bblock%4089c7893cb5aa4644b06569c65945d4d9). 

You can find the markdown file written by me at: [https://app-learninglab.inria.fr/](https://app-learninglab.inria.fr/moocrr/gitlab/dc97a6904245e0d07c1302ee90fceec3/mooc-rr/blob/master/module1/exo2/fichier-markdown.md).

# 18/06/2025 - Update of the journal

## Introduction: Using Jupyter and GitLab

In this MOOC on reproducible research, I am learning how to use **Jupyter Notebooks** in combination with **GitLab** for scientific programming and collaborative version control.

- **Jupyter** is an interactive environment that lets you write and run code in small cells, see outputs instantly, and combine code with visualizations and markdown explanations — all in one place. It's ideal for data exploration, visualization, and documentation.

- **GitLab** is a web-based platform for **version control** and **collaboration**. It allows us to track changes in our code or notebooks, share with others, and maintain a reproducible research history.

During the course, GitLab hosts the exercises, data, and instructions, while Jupyter is used to interactively write code and analyze data. 

## Data analysis with Python (from module 1 exercises)

## Exercise 02-2: Descriptive statistics

In this exercise, I learned how to calculate basic descriptive statistics using Python and the NumPy library. The dataset consisted of 100+ numerical values.

Using `np.mean`, `np.std(ddof=1)`, `np.min`, `np.median`, and `np.max`, I computed:

- **Mean**: ~14.11  
- **Standard deviation**: ~4.33
- **Minimum**: 2.8  
- **Median**: 14.5 
- **Maximum**: 23.4

This helped me understand how to summarize and describe the distribution of a dataset.

## Exercise 02-03: Visualizing the dataset

Next, I practiced using **Matplotlib** to create two types of plots:
1. **Sequence plot**: A line graph showing each data point in the order it appears.
2. **Histogram**: A graphical representation of the distribution of the dataset.

Code used (Python):
```
python
import numpy as np
import matplotlib.pyplot as plt

# Data list defined earlier
plt.figure()
plt.plot(data, 'b')  # blue line plot
plt.show()

plt.figure()
plt.hist(data, color='blue', edgecolor='black')
plt.show()
```


# 24/06/2025 Extended Analysis of Réseau Sentinelles Data (Module 3)

## Overview

We had to do exercises for two diseases—**influenza-like illness (ILI)** and **chickenpox (varicella)**—using weekly incidence data from the French public health surveillance system, **Réseau Sentinelles**. The focus was to learn how to import and analyse data.


## 1. Working with Local Data Copies

To ensure robustness and reproducibility:

- **Large downloads are saved locally**, preserving the original URL for traceability.

This setup avoids broken links and version changes in automated analyses.

## 2. Seasonal Aggregation of ILI and Chickenpox

For both datasets this standard pipeline was followed on Jupyter:

1. **Load** the CSV, skip comment rows.
2. **Parse** the `'week'` column into ISO-week periods.
3. **Clean** by dropping missing values.
4. **Index** the data by weekly periods and **sort** chronologically.
5. **Aggregate** weekly incidence counts to compute **annual totals**.
6. **Identify the year with the strongest or weakest epidemic**.

**Note**: at the end of every modification on Jupyter it is important to Run line by line to check the code and then first update in Jupyter before the final commit to Gitlab.