Python for Finance: Investment Fundamentals & Data Analytics

Packt via Coursera

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Details

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Provider

Coursera
Pricing

Free Online Course (Audit)
Languages

English
Certificate

Paid Certificate Available
Duration & workload

9 hours 48 minutes
Sessions

On-Demand
Level

Intermediate
Subtitles

English

Found in

Overview

Step into the world of finance with Python as your primary tool. This course starts with a thorough introduction to Python, covering essential programming concepts like variables, loops, and functions. You’ll become proficient in handling data types, creating iterations, and applying Python’s syntax to real-world problems. With hands-on experience in Jupyter notebooks, you’ll quickly grasp the fundamentals needed for financial applications. The second phase dives deep into financial analysis, leveraging Python to calculate returns, measure risk, and run complex models like Markowitz portfolio optimization. You’ll learn how to use regression analysis to interpret market trends and make data-backed investment decisions. By integrating Python's advanced tools, you can analyze large datasets with precision, allowing for more accurate forecasts and financial assessments. As the course progresses, you’ll explore sophisticated techniques like the Capital Asset Pricing Model (CAPM) and Monte Carlo simulations. These powerful tools will equip you to evaluate investment opportunities and optimize decision-making. By the end, you’ll not only understand finance but also be able to programmatically implement financial strategies using Python. This course is designed for finance professionals, data analysts, and students with a foundational understanding of mathematics. Familiarity with basic finance concepts is recommended, but no prior programming 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 delve into the fundamentals of Python programming and the Jupyter Notebook environment. You’ll learn how to set up the necessary tools, explore Python’s features, and become familiar with the Jupyter interface, ensuring a solid foundation for the course.

Python Variables and Data Types

In this module, we will introduce the core data types in Python, including variables, numbers, Booleans, and strings. You will learn how to store and manipulate different types of data, forming the basis for more advanced programming tasks.

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 introduce Python’s looping mechanisms, focusing on for-loops, while-loops, and the range() function. You’ll also learn to integrate loops with conditional logic and functions for powerful automation.

Advanced Python Tools

In this module, we will introduce advanced Python tools, including OOP concepts, modules, and data manipulation techniques. You’ll learn how to work with external packages and handle complex data operations in finance.

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.