Python for Finance

Python for Finance is the crossing point where programming in Python blends with financial theory. Together, they give you the know-how to apply that theory into practice and real-life scenarios. In a world where individuals and companies are aiming to become more and more autonomous, your ability to combine programming skills with financial data will allow you to create independent analyses. And that competency will give you an edge over your competitors or in your personal investments. To prepare you for these multi-faceted challenges, this course provides the relevant topics in financial theory and their hands-on application in Python.

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Section 1

Useful Tools

In this course, we assume that you have already gone through our Introduction to Python course. However, you can add more layers to what you have learnt there. In this section, we will add more Python programming skills to your arsenal, explaining OOP, importing modules, in particular - the pandas-datareader module, as it allows us to download financial data and more.

Premium course icon Object-Oriented Programming
Premium course icon Modules and Packages
Premium course icon The Standard Library
Premium course icon Importing Modules
Premium course icon Must-Have Packages for Finance and Data Science
Premium course icon Working with Arrays
Premium course icon Generating Random Numbers
Premium course icon Important Note on Using Online Financial Data Sources
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Premium course icon Using Financial Data in Python
Premium course icon Importing and Organizing Data in Python - Part I
Premium course icon Importing and Organizing Data in Python - Part II
Premium course icon Importing and Organizing Data in Python - Part III
Premium course icon Restarting the Jupyter Kernel
Premium course icon Changing the Index of Your Time-Series Data
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Section 2

Calculating and Comparing Rates of Return in Python

As an investor, you would like to be able to compare the performance of the stocks in your portfolio. One of the most important measures that will allow you to do that is the rate of return of the stock. This section will explain the relevant theory in detail and will provide you with the tools to do that yourself using Python.

Premium course icon Considering Both Risk and Return
Premium course icon What Are We Going to See Next
Premium course icon Calculating a Security's Rate of Return
Premium course icon Calculating a Security's Rate of Return in Python - Simple Returns - Part I
Premium course icon Calculating a Security's Rate of Return in Python - Simple Returns - Part II
Premium course icon Calculating a Security's Rate of Return in Python - Logarithmic Returns
Premium course icon What Is a Portfolio of Securities and How to Calculate Its Rate of Return
Premium course icon Calculating the Rate of Return of a Portfolio of Securities
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Premium course icon Popular Stock Indices that Can Help us Understand Financial Markets
Premium course icon Calculating the Rate of Return of Indices
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Section 3

Measuring Investment Risk

Achieving a high rate of return of your stocks doesn’t come at no cost. Every investment is associated with a certain level of risk, and an investor must be well aware of it before putting their money in a certain basket. This section will teach you how to understand and measure such risk using Python.

Premium course icon How Do We Measure a Security's Risk
Premium course icon Calculating a Security's Risk in Python
Premium course icon The Benefits of Portfolio Diversification
Premium course icon Calculating the Covariance Between Securities
Premium course icon Measuring the Correlation between Stocks
Premium course icon Calculating Covariance and Correlation
Premium course icon Considering the Risk of Multiple Securities in a Portfolio
Premium course icon Calculating Portfolio Risk
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Premium course icon Understanding Systematic vs. Idiosyncratic Risk
Premium course icon Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio
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Section 4

Using Regressions for Financial Analysis

Understanding rates of return and risk is not all there is about finance. Working with regression analysis is a must, and you will see that Python only helps you to be quicker and more precise when doing such estimations.

Premium course icon The Fundamentals of Simple Regression Analysis
Premium course icon Running a Regression in Python
Premium course icon Are All Regressions Created Equal? Learning How to Distinguish Good Regressions
Premium course icon Computing Alpha, Beta, and R Squared in Python

Section 5

Markowitz Portfolio Optimization

One of the main pillars of modern finance is the Markowitz Portfolio Theory. It relates to building a portfolio optimization model, which is quite a complex task mathematically. However, you can see once more how Python can make such a challenge manageable, so long as we stick to theory and are careful at each step while coding.

Premium course icon Markowitz Portfolio Theory - One of the main Pillars of Modern Finance
Premium course icon Obtaining the Efficient Frontier in Python - Part I
Premium course icon Obtaining the Efficient Frontier in Python - Part II
Premium course icon Obtaining the Efficient Frontier in Python - Part III

Section 6

The Capital Asset Pricing Model

As good or bad your portfolio may be, it is not self-sufficient. It is always part of a bigger picture – the market. That’s why you’d always want to compare the performance of your portfolio to the market. The Capital Asset Pricing Model (CAPM), the Beta of a stock, the Sharpe ratio and other measures will come in handy… and will be applied to real data with Python!

Premium course icon The Intuition behind the Capital Asset Pricing Model (CAPM)
Premium course icon Understanding and Calculating a Security's Beta
Premium course icon Calculating the Beta of a Stock
Premium course icon The CAPM Formula
Premium course icon Calculating the Expected Return of a Stock (CAPM)
Premium course icon Introducing the Sharpe Ratio and the Way It Can Be Applied in Practice
Premium course icon Obtaining the Sharpe Ratio in Python
Premium course icon Measuring Alpha and Verifying How Good (or Bad) a Portfolio Manager Is Doing

Section 7

Multivariate Regression Analysis

While in Section 4 we deal with simple regression analysis, here we will take this technique to the next level. As you can guess, multivariate regression analysis is more advanced, but is also more interesting, as it allows you to deal with more complex financial problems.

Premium course icon Multivariate Regression Analysis - a Valuable Tool for Finance Practitioners
Premium course icon Running a Multivariate Regression in Python

Section 8

Monte Carlo Simulations as a Decision-Making Tool

You can’t consider yourself a full-fledged investor if you don’t know how to use Monte Carlo Simulations. All tools you’ve learnt so far in the course will be essential to your ability to embrace the advantages of this technique for visualizing the potential outcomes of financial operations and improving your estimation of the risk associated with them.

Premium course icon The Essence of Monte Carlo Simulations
Premium course icon Monte Carlo Applied in a Corporate Finance Context
Premium course icon Monte Carlo: Predicting Gross Profit - Part I
Premium course icon Monte Carlo: Predicting Gross Profit - Part II
Premium course icon Forecasting Stock Prices with a Monte Carlo Simulation
Premium course icon Monte Carlo: Forecasting Stock Prices - Part I
Premium course icon Monte Carlo: Forecasting Stock Prices - Part II
Premium course icon Monte Carlo: Forecasting Stock Prices - Part III
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Premium course icon An Introduction to Derivative Contracts
Premium course icon The Black-Scholes Formula for Option Pricing
Premium course icon Monte Carlo: Black-Scholes-Merton
Premium course icon Monte Carlo: Euler Discretization - Part I
Premium course icon Monte Carlo: Euler Discretization - Part II
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MODULE 4

Advanced Specialization

This course is part of Module 4 of the 365 Data Science Program. The complete training consists of four modules, each building upon your knowledge from the previous one. Module 4 is focused on developing a specialized, industry-relevant skill set, and students are encouraged to complete Modules 1, 2, and 3 before they start this part of the training. Here, you will learn how to perform Credit Risk Modeling for banks, Customer Analytics for retail or other commercial companies, and Time Series Analysis for finance and stock data.

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