Python for Finance: Investment Fundamentals & Data Analytics

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Overview

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Updated in May 2025. This course now features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course provides a solid foundation in using Python for financial analysis and investment decision-making. You'll learn to apply Python programming and data analysis tools to solve financial problems, from basic calculations to advanced portfolio optimization. The course starts with Python programming basics, including the installation of Jupyter and Anaconda, and progresses to key financial concepts such as calculating rates of return, measuring investment risk, and handling time-series data. As you advance, you’ll gain hands-on experience using Python libraries tailored for financial modeling and analysis. Topics like the Capital Asset Pricing Model (CAPM), Markowitz Portfolio Theory, and Monte Carlo simulations will help you optimize portfolios and evaluate risk. By the end, you’ll be equipped to analyze financial data, perform regression analysis, and apply Python to real-world investment challenges. This course is perfect for finance enthusiasts and those looking to merge finance with programming and data analysis. Basic finance knowledge is helpful, but no prior Python experience is required.

Syllabus

Welcome! Course Introduction

In this module, we will introduce the course, outlining the main objectives and topics that will be covered. We’ll also introduce the instructors and explain who this course is designed for, providing a comprehensive overview of what to expect throughout the lessons.

Introduction to Jupyter and Programming with Python

In this module, we will break down the fundamentals of programming, focusing on Python's suitability for finance. You'll also learn about Jupyter Notebooks and how to install and set them up for an optimal coding experience. This section will equip you with the tools to begin your programming journey.

Python Variables and Data Types

In this module, we will introduce you to Python variables and data types. You will learn how to work with different types of data, such as numbers and strings, and understand the role they play in programming. This foundation will help you handle more complex data operations as you progress.

Basic Python Syntax

In this module, we will cover Python’s essential syntax elements, including operators, commenting, and the importance of indentation. You’ll learn techniques to enhance code readability and functionality, preparing you for more complex coding challenges.

More on Python Operators

In this module, we will dive deeper into Python operators, focusing on comparison, logical, and identity operators. You will enhance your ability to create expressions that drive decision-making in your code.

Conditional Statements

In this module, we will explore conditional statements, such as IF, ELSE, and ELIF. You’ll learn how to build logic-driven code that can handle different scenarios and outcomes based on conditions.

Python Functions

In this module, we will focus on Python functions—how to define them, use parameters, and combine them with other tools. You’ll also explore some of Python’s built-in functions to streamline your programming.

Python Sequences

In this module, we will cover Python’s sequence types, including lists, tuples, and dictionaries. You’ll learn how to store, slice, and manage data effectively within these structures.

Using Iterations in Python

In this module, we will explore how to use iterations to process and analyze data in Python. You’ll learn how to work with loops and conditionals together to automate tasks, enhancing the functionality of your code.

Advanced Python Tools

In this module, we will delve into more advanced Python concepts, such as object-oriented programming and using external libraries. You’ll also learn which Python packages are essential for data analysis and finance, helping you work more efficiently.

PART II FINANCE - Calculating and Comparing Rates of Return in Python

In this module, we will explore the foundational concepts of calculating and comparing rates of return. You’ll learn how to apply these concepts in Python to compute the returns of individual securities, portfolios, and stock indices, providing key insights into risk and performance.

PART II Finance - Measuring Investment Risk

In this module, we will dive into risk measurement in finance. You’ll learn how to quantify the risk of securities and portfolios, calculate covariance and correlation, and use Python tools to analyze the risks associated with investment decisions.

PART II Finance - Using Regressions for Financial Analysis

In this module, we will cover regression analysis and its application in finance. You will learn how to run regressions in Python, interpret the results, and use key indicators such as Alpha and Beta to assess financial performance.

PART II Finance - Markowitz Portfolio Optimization

In this module, we will introduce Markowitz Portfolio Optimization, focusing on building efficient portfolios. You’ll learn to calculate the efficient frontier in Python and optimize asset allocation to achieve the best balance between risk and return.

PART II Finance - The Capital Asset Pricing Model

In this module, we will examine the Capital Asset Pricing Model (CAPM), its calculation, and its significance in finance. You’ll use Python to calculate Beta, expected returns, and performance metrics like the Sharpe Ratio and Alpha to evaluate investments.

PART II Finance - Multivariate Regression Analysis

In this module, we will focus on multivariate regression analysis, applying it in the context of finance. You’ll learn to run multivariate regressions in Python and analyze the relationships between multiple variables affecting asset performance.

PART II Finance - Monte Carlo Simulations as a Decision-Making Tool

In this module, we will delve into Monte Carlo simulations and their powerful applications in finance. You’ll use Python to simulate future profits, forecast stock prices, and apply the Black Scholes formula, enhancing your ability to make informed investment decisions.