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Applied Finance in Python

Enhance your Python financial skills. Learn how to evaluate portfolios, calculate credit risk, and create GARCH models to forecast volatility.
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PythonApplied Finance16 hours10,749

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Track Description

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.

Prerequisites

There are no prerequisites for this track

  • Course

    1

    Introduction to Portfolio Risk Management in Python

    Evaluate portfolio risk and returns, construct market-cap weighted equity portfolios and learn how to forecast and hedge market risk via scenario generation.

  • Course

    3

    Credit Risk Modeling in Python

    Learn how to prepare credit application data, apply machine learning and business rules to reduce risk and ensure profitability.

  • Course

    4

    GARCH Models in Python

    Learn about GARCH Models, how to implement them and calibrate them on financial data from stocks to foreign exchange.

Applied Finance in Python
4 courses
Track
Complete

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