Learn to analyze financial data using Python. Master NumPy, Pandas, data visualization, portfolio analytics, technical analysis, financial modeling, and statistical analysis. Build real-world projects including an automated investment research system.
This free intermediate course teaches you to analyze financial data using Python, taking you from the basics of variables and data types all the way through portfolio construction, technical indicators, and financial modeling. You will work with NumPy and Pandas to handle real-world financial datasets, calculate returns, measure volatility and risk, and run correlation analysis. By the time you reach the capstone project, you will be building a complete investment research system from scratch.
The course is designed for finance students, analysts, and professionals who want to apply Python to their day-to-day work with financial data. A basic comfort with numbers helps, but no prior Python experience is assumed. Each module builds on the last, moving through data visualization and portfolio optimization before covering technical analysis strategies, statistical methods, and valuation modeling.
Finishing the course and passing the final exam earns you a certificate of completion you can share on LinkedIn or add to your resume. Because the course is entirely free and requires no signup to start, it is a practical first step for anyone looking to bring quantitative skills into their finance career or studies.
13 modules • 26 lessons
The course covers Python fundamentals, NumPy and Pandas, sourcing and exploring real financial data, data visualization, portfolio construction and optimization, technical analysis indicators, financial modeling and valuation, and statistical methods for financial research. It ends with a capstone project where you build a complete investment research system.
Yes, the entire course is free with no signup required. Completing the course and passing the final exam earns you a certificate of completion at no cost.
The course is labeled intermediate, so comfort with basic math and financial concepts will help you get the most from it. No prior Python experience is required, as the opening modules cover variables, data types, lists, and dictionaries before moving into libraries and financial applications.
The course focuses on NumPy for numerical operations and Pandas for working with DataFrames and time-series data. Modules also cover data visualization, portfolio analytics, and statistical analysis, all within a Python environment that runs directly in your browser.
Yes. Finishing all lessons and passing the final exam earns you a certificate of completion. You can add it to your LinkedIn profile or resume to show employers or academic programs that you have hands-on Python skills applied to financial data analysis.
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