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
Audience
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.
Publisher resources
Table of contents
- Chapter 1 : Welcome! Course Introduction
- Chapter 2 : Introduction to Jupyter and Programming with Python
- Chapter 3 : Python Variables and Data Types
- Chapter 4 : Basic Python Syntax
- Chapter 5 : More on Python Operators
- Chapter 6 : Conditional Statements
- Chapter 7 : Python Functions
- Chapter 8 : Python Sequences
- Chapter 9 : Using Iterations in Python
-
Chapter 10 : Advanced Python Tools
- Object-Oriented Programming
- Modules, Packages, and the Python Standard Library
- Importing Modules
- What is Software Documentation
- The Python Documentation
- Must-Have Packages Fin DSc
- Arrays
- Generating Random Numbers
- Using Financial Data in Python
- Importing Data Part I
- Importing Data Part II
- Importing Data Part III
- Changing the Index of Your Time-Series Data
- Restarting the Jupyter Kernel
-
Chapter 11 : PART II FINANCE - Calculating and Comparing Rates of Return in Python
- Considering Both Risk and Return
- What are We Going to See Next?
- Calculating a Security's Rate of Return
- Simple Returns Part I
- Simple Returns Part II
- Log Returns
- Portfolio of Securities and Calculating Rate of Return
- Calculating the Rate of Return of a Portfolio of Securities
- Popular Stock Indices
- Calculating the Return of Indices
-
Chapter 12 : PART II Finance - Measuring Investment Risk
- How Do We Measure a Security's Risk?
- Calculating a Security's Risk in Python
- The Benefits of Portfolio Diversification
- Calculating the Covariance between Securities
- Measuring the Correlation between Securities
- Calculating Covariance and Correlation
- Considering the Risk of Multiple Securities
- Calculating Portfolio Risk
- Understanding Systematic Versus Idiosyncratic Risk
- Calculating Diversifiable and NonDiversifiable Risk
- Chapter 13 : PART II Finance - Using Regressions for Financial Analysis
- Chapter 14 : PART II Finance - Markowitz Portfolio Optimization
- Chapter 15 : PART II Finance - The Capital Asset Pricing Model
- Chapter 16 : PART II Finance - Multivariate Regression Analysis
-
Chapter 17 : PART II Finance - Monte Carlo Simulations as a Decision-Making Tool
- The Essence of Monte Carlo Simulations
- Monte Carlo in Corporate Finance
- MC Predicting Gross Profit Part I
- MC Predicting Gross Profit Part II
- Forecasting Stock Prices with an MC Simulation
- MC Forecasting Stock Prices Part I
- MC Forecasting Stock Prices Part II
- MC Forecasting Stock Prices Part III
- An Introduction to Derivative Contracts
- The Black Scholes Formula
- MC Black Scholes Merton Updated
- MC Euler Discretization Part I
- MC Euler Discretization Part II
Product information
- Title: Python for Finance: Investment Fundamentals and Data Analytics
- Author(s):
- Release date: August 2021
- Publisher(s): Packt Publishing
- ISBN: 9781789618976
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