Merge branch 'master' of...

Merge branch 'master' of https://app-learninglab.inria.fr/moocrr/gitlab/dc97a6904245e0d07c1302ee90fceec3/mooc-rr

journal/Readme.md

@@ -28,7 +28,51 @@ In this exercise, write for the first time a **markdown file** whose rendered ve
 
 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()