Master Python for Data Analysis in 7 Days

Day 1: Python Foundations & Data Types

Master the essential Python concepts you'll use every day in data analysis. No prior programming experience required!

What You'll Learn:

• Variables, data types, and operators
• Lists, dictionaries, and data structures
• Control flow with if statements and loops
• Functions for reusable code

# Real-world example: Analyzing sales data
sales_data = {
    'Monday': 15000,
    'Tuesday': 18000,
    'Wednesday': 22000,
    'Thursday': 19000,
    'Friday': 31000
}

# Calculate total weekly sales
total_sales = sum(sales_data.values())
average_daily = total_sales / len(sales_data)

print(f"Total weekly sales: ${total_sales:,}")
print(f"Average daily sales: ${average_daily:,.2f}")

# Find the best performing day
best_day = max(sales_data, key=sales_data.get)
print(f"Best day: {best_day} with ${sales_data[best_day]:,}")

Day 2: NumPy - The Foundation of Data Science

Discover why NumPy is the backbone of data science in Python. Learn to work with arrays 50x faster than regular Python lists.

What You'll Learn:

• Creating and manipulating NumPy arrays
• Mathematical operations and broadcasting
• Statistical functions for data analysis
• Array indexing and slicing techniques

import numpy as np

# Real-world example: Analyzing stock prices
stock_prices = np.array([145.23, 147.89, 143.56, 149.12, 151.34, 148.90, 152.45])
returns = (stock_prices[1:] - stock_prices[:-1]) / stock_prices[:-1] * 100

print(f"Daily returns: {returns}")
print(f"Average return: {returns.mean():.2f}%")
print(f"Volatility (std): {returns.std():.2f}%")
print(f"Best day: {returns.max():.2f}%")
print(f"Worst day: {returns.min():.2f}%")

# Risk analysis
positive_days = np.sum(returns > 0)
print(f"Profitable days: {positive_days} out of {len(returns)}")

Day 3: Pandas - Your Data Swiss Army Knife

Master the most powerful data manipulation library in Python. This is what separates amateur analysts from professionals.

What You'll Learn:

• DataFrames and Series fundamentals
• Data cleaning and preprocessing
• Grouping, filtering, and aggregation
• Merging and joining datasets

import pandas as pd

# Real-world example: E-commerce sales analysis
sales_df = pd.DataFrame({
    'date': pd.date_range('2024-01-01', periods=30),
    'product': ['Laptop', 'Phone', 'Tablet'] * 10,
    'price': [999, 699, 399] * 10,
    'quantity': np.random.randint(1, 10, 30),
    'region': ['North', 'South', 'East', 'West'] * 7 + ['North', 'South']
})

# Calculate revenue
sales_df['revenue'] = sales_df['price'] * sales_df['quantity']

# Analysis by product
product_analysis = sales_df.groupby('product').agg({
    'revenue': ['sum', 'mean'],
    'quantity': 'sum'
}).round(2)

print("Product Performance:")
print(product_analysis)

# Find top performing region
top_region = sales_df.groupby('region')['revenue'].sum().idxmax()
print(f"\nTop region: {top_region}")

Day 4: Data Visualization with Matplotlib & Seaborn

They say a picture is worth a thousand words. In data science, it's worth a thousand rows of data.

What You'll Learn:

• Creating publication-ready charts
• Statistical plots and distributions
• Multi-plot layouts and subplots
• Interactive visualizations

Day 5: Real-World Project - Spotify Music Analysis

Apply everything you've learned to analyze Spotify's music data and create insights that record labels would pay thousands for.

Project Deliverables:

• Analyze trends in music popularity over decades
• Predict hit songs using audio features
• Create artist recommendation system
• Build interactive dashboard

Day 6: Advanced Analytics & Machine Learning Basics

Take your skills to the next level with predictive analytics and basic machine learning concepts.

Day 7: Final Project & Career Preparation

Build your capstone project and prepare your portfolio for job applications.