Financial Data Analysis with Python: Oil vs. Bank Stocks in Recessions

When the economy shifts, understanding how different sectors react is crucial for financial analysis. This course guides you through comparing the performance of oil and bank stocks during historical recessions using Python. You will transition from a beginner to a confident data analyst capable of importing, cleaning, and extracting meaningful insights from complex financial datasets. You will learn to write structured, modern Python code to isolate key metrics like maximum and minimum closing prices during volatile market periods. What you'll learn: 1. Understand foundational financial concepts, market sectors, and how recessions impact banking versus energy stocks. 2. Configure a modern Python environment using virtual environments for clean dependency management. 3. Import, merge, and clean raw financial datasets using industry-standard data libraries. 4. Calculate critical stock metrics, including daily returns, moving averages, and maximum/minimum closing prices. 5. Structure your analysis code with Python type hints to ensure readability and maintainability. 6. Compare sector performance systematically to draw data-driven conclusions about historical market trends. The course begins with essential terminology regarding market cycles and financial data structures. From there, you will step through setting up your environment, loading datasets, cleaning data, and performing target comparisons on sector stocks. This course is designed for beginners interested in financial data analysis or Python programming. No prior coding or financial analysis experience is required. Start analyzing financial markets with clean, modern Python code today.