Ahmed H.
Before 365:
Accountant
After 365:
International Data Analyst
See all reviews
Online Course
free
Python for Finance
with
Martin Ganchev
and
Ned Krastev
Blend investment analysis skills with Python programming: Master financial analysis using Python
8858 students already have enrolled
Skill level:
Basic
Duration:
6 hours
-
Lessons (4 hours)
-
Practice exams (1.5 hours)
- Lessons (4 hours)
- Practice exams (1.5 hours)
CPE credits:
7.5
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
CPE stands for Continuing Professional Education and represents the mandatory credits a wide range of
professionals must earn to maintain their licenses and stay current with regulations and best
practices. One CPE credit typically equals 50 minutes of learning. For more details, visit NASBA's
official website: www.nasbaregistry.org
Accredited:
certificate
What You Learn
- Obtain the efficient frontier in Python to visualize optimal portfolios.
- Calculate and compare rates of return and associated risk of securities.
- Measure portfolio risk and identify idiosyncratic and market risk types.
- Apply Markowitz optimization to construct optimal investment portfolios.
- Leverage CAPM and Monte Carlo simulations to estimate return and risk.
Topics & tools
pythondata analysisfinancial analysisprogramminginvestment analysismontecarlo simulationdata preprocessingregression analysismultivariate regressiondata visualizationtheoryfinance skillsindustry specialization
Your instructor
Content Creator & Content Manager at 365 Data Science | Data Science Instructor
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Economic and Social Sciences
Founder & CEO, 365 Careers | Co-founder, 365 Data Science
Track record
Bringing real-world expertise from leading global companies
Academic background
Master's degree, Finance
Media and recognition
- Named by Forbes Bulgaria among the most successful in online ed
- 1.6M+ students in 210+ countries
- Courses used by employees at 80% of Fortune 500 companies
Course OVERVIEW
Description
CPE Credits: 7.5
Field of Study: Information Technology
Delivery Method: QAS Self Study
Delivery Method: QAS Self Study
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.
Prerequisites
- Python (version 3.8 or later), pandas library, and a code editor or IDE (e.g., Jupyter Notebook, Spyder, or VS Code)
- Basic familiarity with Python programming is required.
- Familiarity with NumPy is helpful but not mandatory.
Advanced preparation
Curriculum
65 lessons
27 exercises
4 exams
- 2. Calculating and Comparing Rates of Return in Python46 MinAs 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 FreeExercise FreeWhat Are We Going to See Next FreeCalculating a Security's Rate of Return FreeExercise FreeCalculating a Security's Rate of Return in Python - Simple Returns - Part I FreeCalculating a Security's Rate of Return in Python - Simple Returns - Part II FreeCalculating a Security's Rate of Return in Python - Logarithmic Returns FreeWhat Is a Portfolio of Securities and How to Calculate Its Rate of Return FreeExercise FreeUsing 'Loc' and 'Iloc' - Notes FreeCalculating the Rate of Return of a Portfolio of Securities FreePopular Stock Indices FreeExercise FreeCalculating the Rate of Return of Indices Free
- 3. Meаsuring Investment Risk41 MinAchieving 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 FreeExercise FreeCalculating a Security's Risk in Python FreeThe Benefits of Portfolio Diversification FreeExercise FreeCalculating the Covariance Between Securities FreeExercise FreeMeasuring the Correlation between Stocks FreeExercise FreeCalculating Covariance and Correlation FreeConsidering the Risk of Multiple Securities in a Portfolio FreeCalculating Portfolio Risk FreeUnderstanding Systematic vs. Idiosyncratic Risk FreeExercise FreeCalculating Diversifiable and Non-diversifiable Risk of a Portfolio FreePractice exam Free
- 4. Using Regressions for Financial Analysis22 MinUnderstanding 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.
- 5. Markowitz Portfolio Optimization20 MinOne 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.
- 6. The Capital Asset Pricing Model26 MinAs 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) FreeExercise FreeUnderstanding and Calculating a Security's Beta FreeExercise FreeCalculating the Beta of a Stock FreeThe CAPM Formula FreeExercise FreeCalculating the Expected Return of a Stock (CAPM) FreeIntroducing the Sharpe Ratio and the Way It Can Be Applied in Practice FreeExercise FreeObtaining the Sharpe Ratio in Python FreeMeasuring Alpha and Verifying How Good (or Bad) a Portfolio Manager Is Doing FreePractice exam FreeExercise Free
- 7. Multivariate Regression Analysis12 MinWhile 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.
- 8. Monte Carlo Simulations as a Decision-Making Tool60 MinYou 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 FreeExercise FreeWhat is a Normal Distribution? - Note FreeMonte Carlo Applied in a Corporate Finance Context FreeExercise FreeMonte Carlo: Predicting Gross Profit - Part I FreeMonte Carlo: Predicting Gross Profit - Part II FreeForecasting Stock Prices with a Monte Carlo Simulation FreeExercise FreeAnother Way to Calculate Simple and Log Returns - Note FreeMonte Carlo: Forecasting Stock Prices - Part I FreeMonte Carlo: Forecasting Stock Prices - Part II FreeMonte Carlo: Forecasting Stock Prices - Part III FreeAn Introduction to Derivative Contracts FreeExercise FreeThe Black-Scholes Formula for Option Pricing FreeMonte Carlo: Black-Scholes-Merton FreeExercise FreeMonte Carlo: Euler Discretization - Part I FreeMonte Carlo: Euler Discretization - Part II FreePractice exam Free
- 9. Course exam80 Min
Free preview
1.1 Course Introduction
4 min
1.2 Must-Have Packages for Finance and Data Science
5 min
1.3 Working with Arrays
6 min
1.4 Generating Random Numbers
3 min
1.5 Important Note on Using Online Financial Data Sources
1 min
1.6 Using Financial Data in Python
3 min
96%
of our students recommend
365 Data Science.
9 in 10
people walk away career-ready
with practical data and AI skills.
$29,000
average salary increase
after moving to an AI and data science career
ACCREDITED certificates
Craft a resume and LinkedIn profile you’re proud of—featuring certificates recognized by leading global
institutions.
Earn CPE-accredited credentials that showcase your dedication, growth, and essential skills—the qualities
employers value most.
How it WORKS
Student REVIEWS
