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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28 quizzes
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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.
Object-Oriented Programming Modules and Packages The Standard Library Importing Modules Must-Have Packages for Finance and Data Science Working with Arrays Generating Random Numbers Important Note on Using Online Financial Data Sources Using Financial Data in Python Importing and Organizing Data in Python - Part I Importing and Organizing Data in Python - Part II Importing and Organizing Data in Python - Part III Restarting the Jupyter Kernel Changing the Index of Your Time-Series Data 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.
Considering Both Risk and Return What Are We Going to See Next Calculating a Security's Rate of Return Calculating a Security's Rate of Return in Python - Simple Returns - Part I Calculating a Security's Rate of Return in Python - Simple Returns - Part II Calculating a Security's Rate of Return in Python - Logarithmic Returns What Is a Portfolio of Securities and How to Calculate Its Rate of Return Calculating the Rate of Return of a Portfolio of Securities Popular Stock Indices that Can Help us Understand Financial Markets Calculating the Rate of Return of Indices 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.
How Do We Measure a Security's Risk Calculating a Security's Risk in Python The Benefits of Portfolio Diversification Calculating the Covariance Between Securities Measuring the Correlation between Stocks Calculating Covariance and Correlation Considering the Risk of Multiple Securities in a Portfolio Calculating Portfolio Risk Understanding Systematic vs. Idiosyncratic Risk Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio 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.
The Fundamentals of Simple Regression Analysis Running a Regression in Python Are All Regressions Created Equal? Learning How to Distinguish Good Regressions 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.
Markowitz Portfolio Theory - One of the main Pillars of Modern Finance Obtaining the Efficient Frontier in Python - Part I Obtaining the Efficient Frontier in Python - Part II 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!
The Intuition behind the Capital Asset Pricing Model (CAPM) Understanding and Calculating a Security's Beta Calculating the Beta of a Stock The CAPM Formula Calculating the Expected Return of a Stock (CAPM) Introducing the Sharpe Ratio and the Way It Can Be Applied in Practice Obtaining the Sharpe Ratio in Python 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.
Multivariate Regression Analysis - a Valuable Tool for Finance Practitioners 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.
The Essence of Monte Carlo Simulations Monte Carlo Applied in a Corporate Finance Context Monte Carlo: Predicting Gross Profit - Part I Monte Carlo: Predicting Gross Profit - Part II Forecasting Stock Prices with a Monte Carlo Simulation Monte Carlo: Forecasting Stock Prices - Part I Monte Carlo: Forecasting Stock Prices - Part II Monte Carlo: Forecasting Stock Prices - Part III An Introduction to Derivative Contracts The Black-Scholes Formula for Option Pricing Monte Carlo: Black-Scholes-Merton Monte Carlo: Euler Discretization - Part I Monte Carlo: Euler Discretization - Part II 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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The course is in-depth and is delivered at a steady pace with eye catching visuals. The instructors go through all the basics really well. They try not to over-simplify the material, you get a good sense аof how deep Data Science is in the course. Great job!!!
This course is amazing! After watching the video carefully and doing all the exercises, I am even capable of having discussions with Machine learning major Master’s students! High standard course with reasonable pricing.
Very clear and in-depth explanation of data science and how all the inter-related concepts apply in real life business environment. Absolutely great for beginners! Best data science course I have come across so far!
I would highly recommend the course to any beginner who wants to venture into the world of Data Science. The concepts are very well explained and there is an emphasis on practical application which really helps create a better understanding of the concepts.
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