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The Financial Analysis in Python

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Prepare for your Microsoft examination with our training course. The No_code course contains a complete batch of videos that will provide you with profound and thorough knowledge related to Microsoft certification exam. Pass the Microsoft No_code test with flying colors.
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Curriculum For This Course
  • 1. Variables 3m 41s
  • 2. Numbers and Boolean Values 3m 5s
  • 3. Strings 5m 43s
  • 1. Arithmetic Operators 3m 23s
  • 2. The Double Equality Sign 1m 33s
  • 3. Reassign Values 1m 8s
  • 4. Add Comments 1m 25s
  • 5. Line Continuation 50s
  • 6. Indexing Elements 1m 18s
  • 7. Structure Your Code with Indentation 1m 45s
  • 1. Comparison Operators 2m 10s
  • 2. Logical and Identity Operators 5m 36s
  • 1. Introduction to the IF statement 3m 4s
  • 2. Add an ELSE statement 2m 39s
  • 3. Else if, for Brief - ELIF 5m 33s
  • 4. A Note on Boolean values 2m 13s
  • 1. Defining a Function in Python 2m 3s
  • 2. Creating a Function with a Parameter 3m 49s
  • 3. Another Way to Define a Function 2m 35s
  • 4. Using a Function in another Function 1m 49s
  • 5. Creating Functions Containing a Few Arguments 1m 13s
  • 6. Notable Built-in Functions in Python 3m 56s
  • 1. Lists 4m 2s
  • 2. Using Methods 3m 22s
  • 3. List Slicing 4m 31s
  • 4. Tuples 3m 13s
  • 5. Dictionaries 4m 4s
  • 1. For Loops 2m 26s
  • 2. While Loops and Incrementing 2m 26s
  • 3. Create Lists with the range() Function 2m 22s
  • 4. Use Conditional Statements and Loops Together 3m 5s
  • 5. All In - Conditional Statements, Functions, and Loops 2m 27s
  • 6. Iterating over Dictionaries 3m 7s
  • 1. Object Oriented Programming 5m
  • 2. Modules and Packages 1m 5s
  • 3. The Standard Library 2m 47s
  • 4. Importing Modules 4m 10s
  • 5. Must-have packages for Finance and Data Science 4m 53s
  • 6. Working with arrays 6m 2s
  • 7. Generating Random Numbers 2m 52s
  • 8. Importing and Organizing Data in Python - part I 3m 44s
  • 9. Importing and Organizing Data in Python - part II 7m 1s
  • 10. Importing and Organizing Data in Python - part III 4m 19s
  • 1. Considering both risk and return 2m 19s
  • 2. What are we going to see next 2m 34s
  • 3. Calculating a security's rate of return 5m 31s
  • 4. Calculating a Security's Rate of Return in Python - Simple Returns - Part I 5m 23s
  • 5. Calculating a Security's Rate of Return in Python - Simple Returns - Part II 3m 28s
  • 6. Calculating a Security's Return in Python - Logarithmic Returns 3m 39s
  • 7. What is a portfolio of securities and how to calculate its rate of return 2m 39s
  • 8. Calculating the Rate of Return of a Portfolio of Securities 8m 34s
  • 9. Popular stock indices that can help us understand financial markets 3m 31s
  • 10. Calculating the Rate of Return of Indices 5m 3s
  • 1. How do we measure a security's risk 6m 5s
  • 2. Calculating a Security's Risk in Python 5m 56s
  • 3. The benefits of portfolio diversification 3m 28s
  • 4. Calculating the covariance between securities 3m 35s
  • 5. Measuring the correlation between stocks 3m 59s
  • 6. Calculating Covariance and Correlation 5m
  • 7. Considering the risk of multiple securities in a portfolio 3m 19s
  • 8. Calculating Portfolio Risk 2m 39s
  • 9. Understanding Systematic vs 2m 58s
  • 10. Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio 4m 28s
  • 1. The fundamentals of simple regression analysis 3m 55s
  • 2. Running a Regression in Python 6m 35s
  • 3. Are all regressions created equal? Learning how to distinguish good regressions 4m 55s
  • 4. Computing Alpha, Beta, and R Squared in Python 6m 14s
  • 1. Markowitz Portfolio Theory - One of the main pillars of modern Finance 6m 34s
  • 2. Obtaining the Efficient Frontier in Python - Part I 5m 35s
  • 3. Obtaining the Efficient Frontier in Python - Part II 5m 18s
  • 4. Obtaining the Efficient Frontier in Python - Part III 2m 7s
  • 1. The intuition behind the Capital Asset Pricing Model (CAPM) 4m 45s
  • 2. Understanding and calculating a security's Beta 4m 14s
  • 3. Calculating the Beta of a Stock 3m 38s
  • 4. The CAPM formula 4m 20s
  • 5. Calculating the Expected Return of a Stock (CAPM) 2m 16s
  • 6. Introducing the Sharpe ratio and the way it can be applied in practice 2m 21s
  • 7. Obtaining the Sharpe ratio in Python 1m 23s
  • 8. Measuring alpha and verifying how good (or bad) a portfolio manager is doing 4m 13s
  • 1. Multivariate regression analysis - a valuable tool for finance practitioners 5m 42s
  • 2. Running a multivariate regression in Python 6m 20s
  • 1. The essence of Monte Carlo simulations 2m 32s
  • 2. Monte Carlo applied in a Corporate Finance context 2m 30s
  • 3. Monte Carlo: Predicting Gross Profit - Part I 6m 3s
  • 4. Monte Carlo: Predicting Gross Profit - Part II 2m 57s
  • 5. Forecasting Stock Prices with a Monte Carlo Simulation 4m 27s
  • 6. Monte Carlo: Forecasting Stock Prices - Part I 3m 39s
  • 7. Monte Carlo: Forecasting Stock Prices - Part II 4m 38s
  • 8. Monte Carlo: Forecasting Stock Prices - Part III 4m 17s
  • 9. An Introduction to Derivative Contracts 6m 32s
  • 10. The Black Scholes Formula for Option Pricing 4m 51s
  • 11. Monte Carlo: Black-Scholes-Merton 6m
  • 12. Monte Carlo: Euler Discretization - Part I 6m 21s
  • 13. Monte Carlo: Euler Discretization - Part II 2m 9s

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