Datacamp – Applied Finance in Python 2024-8

Updated August 10, 2026 341 MB

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Descriptions

Applied Finance in Python, Enhance your Python financial skills and learn how to manipulate data and make better data-driven decisions. You’ll begin this track by discovering how to evaluate portfolios, mitigate risk exposure, and use the Monte Carlo simulation to model probability. Next, you’ll learn how to rebalance a portfolio using neural networks. Through interactive coding exercises, you’ll use powerful libraries, including SciPy, statsmodels, scikit-learn, TensorFlow, Keras, and XGBoost, to examine and manage risk. You’ll then apply what you’ve learned to answer questions commonly faced by financial firms, such as whether or not to approve a loan or a credit card request, using machine learning and financial techniques. Along the way, you’ll also create GARCH models and get hands-on with real datasets that feature Microsoft stocks, historical foreign exchange rates, and cryptocurrency data. Start this track to advance your Python financial skills.

What you’ll learn

Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.
Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.
Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.
Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.