diff --git a/module 2/exo4/data_analysis.ipynb b/module 2/exo4/data_analysis.ipynb
index 43886cb27a4908e9da89cae59749ac4f8451b273..8a6846fb32e04c5f9be4e58445a549185a7e1875 100644
--- a/module 2/exo4/data_analysis.ipynb	
+++ b/module 2/exo4/data_analysis.ipynb	
@@ -4,3 +4,51 @@ Date,Activity,Duration,Mood
 2024-09-16,Work,3,4
 2024-09-16,Exercise,1,5
 
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# Load the data
+data = pd.DataFrame({
+    'Date': ['2024-09-13', '2024-09-13', '2024-09-16', '2024-09-16'],
+    'Activity': ['Work', 'Exercise', 'Work', 'Exercise'],
+    'Duration': [3, 1, 3, 1],
+    'Mood': [5, 4, 4, 5]
+})
+
+# Convert 'Date' column to datetime format
+data['Date'] = pd.to_datetime(data['Date'])
+
+# Display the first few rows of the dataset
+print("Data Overview:")
+print(data)
+
+# Basic Statistics
+print("\nBasic Statistics:")
+print(data.describe(include='all'))
+
+# Additional Statistics
+mean_duration = data['Duration'].mean()
+median_mood = data['Mood'].median()
+print(f"\nAverage Duration: {mean_duration:.2f}")
+print(f"Median Mood: {median_mood:.2f}")
+
+# Plot Duration Over Time using Matplotlib
+plt.figure(figsize=(12, 6))
+for activity in data['Activity'].unique():
+    subset = data[data['Activity'] == activity]
+    plt.plot(subset['Date'], subset['Duration'], marker='o', linestyle='-', label=activity)
+plt.title('Daily Duration of Activities')
+plt.xlabel('Date')
+plt.ylabel('Duration (hours)')
+plt.legend(title='Activity')
+plt.xticks(rotation=45)
+plt.tight_layout()
+plt.show()
+
+# Histogram of Mood Ratings using Matplotlib
+plt.figure(figsize=(8, 6))
+plt.hist(data['Mood'], bins=5, color='skyblue', edgecolor='black')
+plt.title('Distribution of Mood Ratings')
+plt.xlabel('Mood Rating')
+plt.ylabel('Frequency')
+plt.show()