Update Readme.md

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. We often clone GitLab repositories into our Jupyter environment, modify files or write analysis code, and then commit our changes back to GitLab to track our progress and collaborate effectively.
+
+## 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()