🔬 Data Science with Python

Master data manipulation, analysis, and visualization using Python. Learn pandas, numpy, matplotlib, seaborn, and real-world data science workflows from data collection to insights.

📚 Course Modules

• Python for Data Science: NumPy, Pandas basics
• Data Manipulation: Cleaning, transformation, merging
• Data Visualization: Matplotlib, Seaborn, Plotly
• Statistical Analysis: Descriptive & inferential stats
• Real-world Projects: EDA on real datasets
• Feature Engineering: Creating meaningful features
• Data Wrangling: Handling missing data, outliers

💻 Python Implementation Example

# Complete EDA Example with Python
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.datasets import load_iris

# Load data
iris = load_iris()
df = pd.DataFrame(iris.data, columns=iris.feature_names)
df['species'] = [iris.target_names[t] for t in iris.target]

# Data Overview
print("Dataset Shape:", df.shape)
print("
Data Info:")
print(df.info())

# Statistical Summary
print("
Descriptive Statistics:")
print(df.describe())

# Check for missing values
print("
Missing Values:")
print(df.isnull().sum())

# Visualization
fig, axes = plt.subplots(2, 2, figsize=(14, 10))

# 1. Distribution plots
sns.histplot(data=df, x='sepal length (cm)', hue='species', kde=True, ax=axes[0,0])
axes[0,0].set_title('Sepal Length Distribution')

# 2. Correlation heatmap
numeric_df = df.select_dtypes(include=[np.number])
sns.heatmap(numeric_df.corr(), annot=True, cmap='coolwarm', ax=axes[0,1])
axes[0,1].set_title('Feature Correlation Matrix')

# 3. Box plots
sns.boxplot(data=df, x='species', y='petal length (cm)', ax=axes[1,0])
axes[1,0].set_title('Petal Length by Species')

# 4. Scatter plot
sns.scatterplot(data=df, x='sepal length (cm)', y='petal length (cm)', 
                hue='species', style='species', s=100, ax=axes[1,1])
axes[1,1].set_title('Sepal vs Petal Length')

plt.tight_layout()
plt.show()

# Feature Engineering
df['sepal_petal_ratio'] = df['sepal length (cm)'] / df['petal length (cm)']
print(f"
New feature created: sepal_petal_ratio")