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.
5 hours 
34 exercises 
28 quizzes 
128 downloadables
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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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Trust the other 500,000 students
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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