The Financial Analysis in Python

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What’s included

  • 100 : Lectures
  • 6h 26m 34s : Duration
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$14.99/24.99

Lectures
1. Programming Explained in 5 Minutes- 5m 4s
2. Why Python?- 5m 11s
3. Why Jupyter?- 3m 29s
4. Installing Python and Jupyter- 4m 22s
5. Jupyter's Interface - the Dashboard- 3m 15s
6. Jupyter's Interface - Prerequisites for Coding- 6m 15s
Lectures
1. Variables- 3m 41s
2. Numbers and Boolean Values- 3m 5s
3. Strings- 5m 43s
Lectures
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
Lectures
1. Comparison Operators- 2m 10s
2. Logical and Identity Operators- 5m 36s
Lectures
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
Lectures
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
Lectures
1. Lists- 4m 2s
2. Using Methods- 3m 22s
3. List Slicing- 4m 31s
4. Tuples- 3m 13s
5. Dictionaries- 4m 4s
Lectures
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
Lectures
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
Lectures
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
Lectures
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
Lectures
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
Lectures
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
Lectures
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
Lectures
1. Multivariate regression analysis - a valuable tool for finance practitioners- 5m 42s
2. Running a multivariate regression in Python- 6m 20s
Lectures
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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