Applied Finance in Python

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Learn Path Description

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.

Skills You Will Gain

Courses In This Learning Path

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Total Duration

4 hours

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Level

Beginner

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Learn Type

Certifications

Introduction to Portfolio Risk Management in Python

This course will teach you how to evaluate basic portfolio risk and return, just like a Wall Street quantitative analyst. This is the first step in automating portfolio construction and management. Learn how to create market-cap weighted equity portfolios and what drives your portfolio returns. You can forecast and hedge market risks using scenario generation.

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Total Duration

4 hours

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Level

Intermediate

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Learn Type

Certifications

Quantitative Risk Management in Python

Quantitative risk management plays an important role in asset management, banking and insurance. Financial risk analysts, regulators, and actuaries need to be able quantitatively balance the rewards and risks.

This course will teach you how to manage your financial portfolio. This course includes an analysis on the 2008 financial crisis, and how it affected investment banks such as J.P. Morgan or Goldman Sachs. You will learn how to use Python to reduce risk exposure using the Value at Risk and Conditional Value At Risk measures. Learn how to calculate risk using techniques like Monte Carlo simulation, and how to use cutting-edge technologies such as neural networks to rebalance portfolios in real-time.

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Total Duration

4 hours

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Level

Intermediate

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Learn Type

Certifications

Credit Risk Modeling in Python

Perhaps you have applied for a loan, or a credit card. You are aware that financial companies may process your information before they make a decision. They could lose their business if they approve you for a loan. This course will show you how to prepare credit application data. Machine learning and business principles will be used to reduce risk and improve profitability. Two data sets will be used in order to simulate credit applications. However, the business value must also be considered. Join me to learn more about credit modeling and the expected value.

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Total Duration

4 hours

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Level

Intermediate

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Learn Type

Certifications

GARCH Models in Python

Finance is all about volatility. GARCH Python models are very popular for forecasting variance changes when working with time-dependent information. This course will show you how to use the GARCH models and which assumptions to make. This course teaches you how to predict volatility and evaluate model performance. Real-world data, including historical Tesla stock prices, will be used to give you practical experience in quantifying portfolio risks by using covariance, value-at-risk and stock beta calculations. You can apply the knowledge to many assets, including stocks and indices. This will allow you to use the GARCH models.

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