Financial analysis has been transformed by Python and machine learning—what used to take hours of manual spreadsheet work can now be automated, what used to be guesswork can now be modeled, and what used to be invisible patterns can now be detected. This course covers the full stack: downloading financial data, preparing it for analysis, calculating technical indicators, building trading strategies, modeling time series, optimizing portfolios, running Monte Carlo simulations, and applying machine learning and deep learning to problems like fraud detection and default prediction. Over twenty hours of content, with all code included, so you're not just learning concepts—you're building the skills to implement them.

This Course Offers

• Complete Python environment for financial work: From getting data out of Yahoo Finance and Quandl to converting prices to returns, changing frequency, visualizing time series, and investigating the statistical properties that matter in financial markets.
• Technical analysis and backtesting: Calculating indicators like Bollinger Bands, MACD, and RSI, then building and testing automated trading strategies to see what actually works (and what doesn't).
• Time series models used by professionals: Exponential smoothing, ARIMA, GARCH (including multivariate), factor models including CAPM and Fama-French—the tools for understanding how financial data behaves over time.
• Portfolio optimization and Monte Carlo methods: Asset allocation strategies, optimization techniques, and simulations for pricing American options and estimating Value at Risk (VaR).
• Machine learning for financial problems: Random forests, XGBoost, LightGBM, stacked models, hyperparameter tuning (including Bayesian optimization), handling class imbalance, and deep learning with PyTorch applied to credit card fraud and default prediction.
• End-to-end data science project: A complete walkthrough from problem definition to model deployment, showing how all the pieces fit together in a real financial context.

Why We Love This Course

1. It covers both traditional financial analysis and modern machine learning: You're not choosing between learning finance and learning data science—you're learning how they work together in practice.
2. All code is included and explained: No staring at slides wondering how to implement what you're learning. Every technique comes with working code you can run, modify, and apply to your own problems.
3. 20.5 hours provides serious depth: This isn't a survey course. Each topic gets the time it deserves, from statistical foundations through advanced modeling.
4. The instructor understands what financial professionals actually need: The focus on real data, practical implementation, and problems like fraud detection reflects what happens in industry, not just academic exercises.
5. It works for multiple audiences: Developers learn financial applications, financial analysts learn programming, traders learn modeling, and everyone learns how the pieces connect.

Financial markets generate more data than any human can process manually. The professionals who succeed are the ones who can make that data talk—building models, testing strategies, quantifying risk, and detecting opportunities before they're obvious. The question is whether you want to keep working with the tools and techniques of the last decade or learn to apply Python and machine learning to the problems that define modern finance. This course comes with a money-back guarantee if it's not clicking, so there's real room to see how different financial analysis feels when you can code.

• Developers who want to apply their coding skills to financial analysis and trading
• Financial analysts ready to move beyond Excel into programmable, reproducible analysis
• Data analysts and data scientists looking for domain-specific applications in finance
• Stock and cryptocurrency traders who want to move from gut feelings to systematic strategies
• Students building skills for careers in quantitative finance, fintech, or data science
• Teachers and researchers who need practical, code-based examples for courses or papers
• Quantitative analysts (quants) who want to expand their toolkit with modern machine learning
• Risk professionals who need better models for understanding and quantifying exposure
• Anyone working in or adjacent to finance who suspects there's more they could be doing with data
• Self-directed learners who want to understand how Python is changing the financial industry

• Statistics and basic Python knowledge are expected.
• Familiarity with financial concepts (stocks, returns, portfolios) is helpful.
• A computer with internet access to download data and run code.
• Willingness to work through code examples—copying and running is fine, but understanding comes from experimenting.
• Curiosity about how machine learning applies to financial problems.

Interested in exploring more business lessons? Check out our full course library to continue building your skills and advancing your learning journey.