Python for Finance: Investment Fundamentals and Data Analytics

Video description

This course will take you on a journey where you will learn how to code in Python. You will learn how to use Python in a real working environment and explore how Python can be applied in the world of finance to solve portfolio optimization problems.

The first part of the course is ideal for beginners and people who want to brush up on their Python skills. Once we have covered the basics, we will be ready to tackle financial calculations and portfolio optimization tasks. The finance block of this course will teach you in-demand, real-world skills employers are looking for. This explains topics such as how to work with Python’s conditional statements, functions, sequences, and loops, build investment portfolios, and more.

What You Will Learn

  • Learn to work with Python s conditional statements, functions, sequences, and loops
  • Learn to conduct in-depth investment analysis
  • Calculate risk and return for individual securities
  • Calculate risk and return for investment portfolios
  • Perform Monte Carlo simulations
  • Learn how to price options by applying the Black Scholes formula


This course is designed for data scientists, programming beginners, people interested in finance and investments, programmers who want to specialize in finance, finance graduates, and professionals who need to know more about how to apply their knowledge in Python.

About The Author

365 Careers Ltd.: 365 Careers’ courses have been taken by more than 203,000 students in 204 countries. People working at world-class firms such as Apple, PayPal, and Citibank have completed 365 Careers trainings. By choosing 365 Careers, you make sure you will learn from proven experts who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time.

If you want to become a financial analyst, a finance manager, an FP&A analyst, an investment banker, a business executive, an entrepreneur, a business intelligence analyst, a data analyst, or a data scientist, 365 Careers’ courses are the perfect place to start.

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Table of contents

  1. Chapter 1 : Welcome! Course Introduction
    1. What Does the Course Cover?
  2. Chapter 2 : Introduction to Jupyter and Programming with Python
    1. Python
    2. Why Python
    3. Jupyter
    4. Setting Up the Environment
    5. Programming Explained in 5 Minutes
    6. Why Jupyter
    7. Installing Anaconda
    8. Jupyter's Interface – the Dashboard
    9. Jupyter's Interface – Prerequisites for Coding
    10. Jupyter - Using Shortcuts
    11. Jupyter - Handling Error Messages
    12. Jupyter - Restarting the Kernel
  3. Chapter 3 : Python Variables and Data Types
    1. Python Variables
    2. Understanding Numbers and Boolean Values
    3. Strings
  4. Chapter 4 : Basic Python Syntax
    1. The Arithmetic Operators of Python
    2. What is the Double Equality Sign
    3. How to Reassign Values
    4. How to Add Comments
    5. Understanding Line Continuation
    6. How to Index Elements
    7. How to Structure Your Code with Indentation
  5. Chapter 5 : More on Python Operators
    1. Python Comparison Operators
    2. Python's Logical and Identity Operators
  6. Chapter 6 : Conditional Statements
    1. Getting to Know the IF Statement
    2. Adding an ELSE statement
    3. Else if, for Brief – ELIF
    4. An Additional Explanation of Boolean Values
  7. Chapter 7 : Python Functions
    1. How to Define a Function in Python
    2. How to Create a Function with a Parameter
    3. Another Way to Define a Function
    4. How to Use a Function within a Function
    5. Use Conditional Statements and Functions Together
    6. How to Create Functions that Contain a Few Arguments
    7. Built-In Functions in Python Worth Knowing
  8. Chapter 8 : Python Sequences
    1. Introduction to Lists
    2. Using Methods in Python
    3. What is List Slicing
    4. Working with Tuples
    5. Python Dictionaries
  9. Chapter 9 : Using Iterations in Python
    1. Using For Loops
    2. Using While Loops and Incrementing
    3. Use the range() Function to Create Lists
    4. Combine Conditional Statements and Loops
    5. All In – Conditional Statements, Functions, and Loops
    6. How to Iterate over Dictionaries
  10. Chapter 10 : Advanced Python Tools
    1. Object-Oriented Programming
    2. Modules, Packages, and the Python Standard Library
    3. Importing Modules
    4. What is Software Documentation
    5. The Python Documentation
    6. Must-Have Packages Fin DSc
    7. Arrays
    8. Generating Random Numbers
    9. Using Financial Data in Python
    10. Importing Data Part I
    11. Importing Data Part II
    12. Importing Data Part III
    13. Changing the Index of Your Time-Series Data
    14. Restarting the Jupyter Kernel
  11. Chapter 11 : PART II FINANCE - Calculating and Comparing Rates of Return in Python
    1. Considering Both Risk and Return
    2. What are We Going to See Next?
    3. Calculating a Security's Rate of Return
    4. Simple Returns Part I
    5. Simple Returns Part II
    6. Log Returns
    7. Portfolio of Securities and Calculating Rate of Return
    8. Calculating the Rate of Return of a Portfolio of Securities
    9. Popular Stock Indices
    10. Calculating the Return of Indices
  12. Chapter 12 : PART II Finance - Measuring Investment Risk
    1. How Do We Measure a Security's Risk?
    2. Calculating a Security's Risk in Python
    3. The Benefits of Portfolio Diversification
    4. Calculating the Covariance between Securities
    5. Measuring the Correlation between Securities
    6. Calculating Covariance and Correlation
    7. Considering the Risk of Multiple Securities
    8. Calculating Portfolio Risk
    9. Understanding Systematic Versus Idiosyncratic Risk
    10. Calculating Diversifiable and NonDiversifiable Risk
  13. Chapter 13 : PART II Finance - Using Regressions for Financial Analysis
    1. Simple Regression Analysis
    2. Running a Regression in Python
    3. How to Distinguish Good Regressions
    4. Computing Alpha, Beta, and R2 in Python
  14. Chapter 14 : PART II Finance - Markowitz Portfolio Optimization
    1. Markowitz Portfolio Theory
    2. Obtaining the Efficient Frontier Part I
    3. Obtaining the Efficient Frontier Part II
    4. Obtaining the Efficient Frontier Part III
  15. Chapter 15 : PART II Finance - The Capital Asset Pricing Model
    1. The Intuition Behind the CAPM
    2. Understanding and Calculating Beta
    3. Calculating the Beta of a Stock
    4. The CAPM Formula
    5. Calculating the Expected Return of a Stock
    6. Introducing the Sharpe Ratio
    7. Obtaining the Sharpe Ratio in Python
    8. Measuring Alpha
  16. Chapter 16 : PART II Finance - Multivariate Regression Analysis
    1. Multivariate Regression Analysis
    2. Running a Multivariate Regression in Python
  17. Chapter 17 : PART II Finance - Monte Carlo Simulations as a Decision-Making Tool
    1. The Essence of Monte Carlo Simulations
    2. Monte Carlo in Corporate Finance
    3. MC Predicting Gross Profit Part I
    4. MC Predicting Gross Profit Part II
    5. Forecasting Stock Prices with an MC Simulation
    6. MC Forecasting Stock Prices Part I
    7. MC Forecasting Stock Prices Part II
    8. MC Forecasting Stock Prices Part III
    9. An Introduction to Derivative Contracts
    10. The Black Scholes Formula
    11. MC Black Scholes Merton Updated
    12. MC Euler Discretization Part I
    13. MC Euler Discretization Part II

Product information

  • Title: Python for Finance: Investment Fundamentals and Data Analytics
  • Author(s): 365 Careers Ltd.
  • Release date: August 2021
  • Publisher(s): Packt Publishing
  • ISBN: 9781789618976