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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.

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1
Python
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.

4 hours

Dakota Wixom Headshot

Dakota Wixom

Quantitative Analyst and Founder of QuantCourse.com

2
Python
Quantitative Risk Management in Python

Learn about risk management, value at risk and more applied to the 2008 financial crisis using Python.

4 hours

Jamsheed Shorish Headshot

Jamsheed Shorish

Computational Economist

3
Python
Credit Risk Modeling in Python

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

4 hours

Michael Crabtree Headshot

Michael Crabtree

Data Scientist

4
Python
GARCH Models in Python

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

4 hours

Chelsea Yang Headshot

Chelsea Yang

Data Science Instructor

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