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 indemand, realworld 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 indepth 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 worldclass 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
 ObjectOriented Programming
 Modules, Packages, and the Python Standard Library
 Importing Modules
 What is Software Documentation
 The Python Documentation
 MustHave 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 TimeSeries 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 DecisionMaking 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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