Python for Finance

with Martin Ganchev and Ned Krastev
4.8/5
(483)

Blend investment analysis skills with Python programming: Master financial analysis using Python

6 hours of content 9370 students
Start for free

What you get:

  • 6 hours of content
  • 27 Interactive exercises
  • 224 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

Python for Finance

A course by Martin Ganchev and Ned Krastev
Start for free

What you get:

  • 6 hours of content
  • 27 Interactive exercises
  • 224 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement
Start for free

What you get:

  • 6 hours of content
  • 27 Interactive exercises
  • 224 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

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.

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

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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Course Introduction

1.1 Course Introduction

4 min

Must-Have Packages for Finance and Data Science

1.2 Must-Have Packages for Finance and Data Science

5 min

Working with Arrays

1.3 Working with Arrays

6 min

Generating Random Numbers

1.4 Generating Random Numbers

3 min

Important Note on Using Online Financial Data Sources

1.5 Important Note on Using Online Financial Data Sources

1 min

Using Financial Data in Python

1.6 Using Financial Data in Python

3 min

Curriculum

  • 1. Useful Tools
    11 Lessons 42 Min
    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.
    Course Introduction
    4 min
    Must-Have Packages for Finance and Data Science
    5 min
    Working with Arrays
    6 min
    Generating Random Numbers
    3 min
    Important Note on Using Online Financial Data Sources Read now
    1 min
    Using Financial Data in Python
    3 min
    Importing and Organizing Data in Python - Part I
    4 min
    Importing and Organizing Data in Python - Part II
    7 min
    Importing and Organizing Data in Python - Part III
    4 min
    Changing the Index of Your Time-Series Data
    3 min
    Restarting the Jupyter Kernel
    2 min
  • 2. Calculating and Comparing Rates of Return in Python
    11 Lessons 46 Min
    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
    3 min
    What Are We Going to See Next
    3 min
    Calculating a Security's Rate of Return
    6 min
    Calculating a Security's Rate of Return in Python - Simple Returns - Part I
    5 min
    Calculating a Security's Rate of Return in Python - Simple Returns - Part II
    3 min
    Calculating a Security's Rate of Return in Python - Logarithmic Returns
    4 min
    What Is a Portfolio of Securities and How to Calculate Its Rate of Return
    3 min
    Using 'Loc' and 'Iloc' - Notes Read now
    1 min
    Calculating the Rate of Return of a Portfolio of Securities
    9 min
    Popular Stock Indices
    4 min
    Calculating the Rate of Return of Indices
    5 min
  • 3. Meаsuring Investment Risk
    10 Lessons 41 Min
    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
    6 min
    Calculating a Security's Risk in Python
    6 min
    The Benefits of Portfolio Diversification
    3 min
    Calculating the Covariance Between Securities
    4 min
    Measuring the Correlation between Stocks
    4 min
    Calculating Covariance and Correlation
    5 min
    Considering the Risk of Multiple Securities in a Portfolio
    3 min
    Calculating Portfolio Risk
    3 min
    Understanding Systematic vs. Idiosyncratic Risk
    3 min
    Calculating Diversifiable and Non-diversifiable Risk of a Portfolio
    4 min
  • 4. Using Regressions for Financial Analysis
    4 Lessons 22 Min
    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
    4 min
    Running a Regression in Python
    7 min
    Are All Regressions Created Equal? Learning How to Distinguish Good Regressions
    5 min
    Computing Alpha, Beta, and R Squared in Python
    6 min
  • 5. Markowitz Portfolio Optimization
    4 Lessons 20 Min
    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
    7 min
    Obtaining the Efficient Frontier in Python - Part I
    6 min
    Obtaining the Efficient Frontier in Python - Part II
    5 min
    Obtaining the Efficient Frontier in Python - Part III
    2 min
  • 6. The Capital Asset Pricing Model
    8 Lessons 26 Min
    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)
    5 min
    Understanding and Calculating a Security's Beta
    4 min
    Calculating the Beta of a Stock
    4 min
    The CAPM Formula
    4 min
    Calculating the Expected Return of a Stock (CAPM)
    2 min
    Introducing the Sharpe Ratio and the Way It Can Be Applied in Practice
    2 min
    Obtaining the Sharpe Ratio in Python
    1 min
    Measuring Alpha and Verifying How Good (or Bad) a Portfolio Manager Is Doing
    4 min
  • 7. Multivariate Regression Analysis
    2 Lessons 12 Min
    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
    6 min
    Running a Multivariate Regression in Python
    6 min
  • 8. Monte Carlo Simulations as a Decision-Making Tool
    15 Lessons 60 Min
    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
    3 min
    What is a Normal Distribution? - Note Read now
    1 min
    Monte Carlo Applied in a Corporate Finance Context
    3 min
    Monte Carlo: Predicting Gross Profit - Part I
    6 min
    Monte Carlo: Predicting Gross Profit - Part II
    3 min
    Forecasting Stock Prices with a Monte Carlo Simulation
    4 min
    Another Way to Calculate Simple and Log Returns - Note Read now
    1 min
    Monte Carlo: Forecasting Stock Prices - Part I
    4 min
    Monte Carlo: Forecasting Stock Prices - Part II
    5 min
    Monte Carlo: Forecasting Stock Prices - Part III
    4 min
    An Introduction to Derivative Contracts
    7 min
    The Black-Scholes Formula for Option Pricing
    5 min
    Monte Carlo: Black-Scholes-Merton
    6 min
    Monte Carlo: Euler Discretization - Part I
    6 min
    Monte Carlo: Euler Discretization - Part II
    2 min
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Topics

Financial AnalysisPythonProgrammingData AnalysisInvestment AnalysisMontecarlo SimulationData PreprocessingRegression AnalysisMultivariate RegressionData VisualizationTheoryFinance SkillsIndustry Specialization

Tools & Technologies

python

Course Requirements

  • 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.

Who Should Take This Course?

Level of difficulty: Beginner

  • Aspiring investment analysts, financial analysts, data analysts, data scientists
  • Current investment analysts, financial analysts, data analysts, data scientists who are passionate about acquiring domain-specific knowledge in investment analysis

Exams and Certification

A 365 Financial Analyst Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Martin Ganchev

Martin Ganchev

Worked at

15 Courses

42023 Reviews

621423 Students

Martin Ganchev has been creating high-quality online education content since 2016, helping over one million students worldwide gain valuable business, finance, and data skills. As the second employee at 365 Careers, he played a key role in building some of the company's most successful courses, known for their clarity, practical value, and engaging delivery. With a Master of Science in Economic and Social Sciences from Bocconi University, Martin specializes in statistics, econometrics, Python, SQL, and business intelligence—equipping students to analyze data rigorously, automate workflows, and turn complex results into clear business insights.

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What Our Learners Say

Sushil Baguant
Sushil Baguant
23.03.2026

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