The course **Introduction to Portfolio Management with Python** is designed for individuals **with a foundational understanding of Python, probability theory and statistics**. 

Its **primary goal** is to introduce **key financial concepts**, with an emphasis on **diversified investing**.

Throughout this course, **you will learn**:

- Fundamental financial concepts, such as investing, asset, portfolio, return and risk;

- Ways for computing different kinds of risk and return using Python;

- Trade-off between high return and low risk;

- How to create portfolios with the smallest risk of financial loss and with the most justified risk by potential return;

- Classification of external factors and their potential impact on our portfolio.

This course will help those who have knowledge of Python and Probability Theory and want to get some basic knowledge of Portfolio Management, including fundamental financial concepts like risk and return, basics of portfolio optimization and influence of factors on portfolio's behaviour.

To start, we'll explore some fundamental financial concepts that will be essential for the rest of the course. These include investing, diversification, portfolio, return and risk. We'll also learn how to use Python to work with them.

Now that we've covered the basics of portfolio analysis, it's time to explore methods for choosing optimal weights for our portfolio. Specifically, we'll discuss fundamental concepts in portfolio optimization, including Modern Portfolio Theory and its key component - the Efficient Frontier. Additionally, we'll learn how to balance high returns with low risk, and how to determine the weights for a portfolios that achieves the best possible return-risk trade-off or the lowest risk, all using Python.

Finally, after covering the fundamentals of portfolio analysis and optimization, we’re ready to dive into predicting our portfolio's behavior and understanding the impact of various factors on it. Practically, we'll explore the basics of Factor Analysis, including different types of factors and several models built around them.

What You Will Learn

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What You Will Learn