The Financial Analysis in Python

The Financial Analysis in Python

BEST SELLER 100 Lectures 6h 26m 34s

Prepare for your examination with our training course. The course contains a complete batch of videos that will provide you with profound and thorough knowledge related to certification exam. Pass the test with flying colors.

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Curriculum For This Course

6 lectures 27m 36s
  • 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

3 lectures 12m 29s
  • 1. Variables
    3m 41s
  • 2. Numbers and Boolean Values
    3m 5s
  • 3. Strings
    5m 43s

7 lectures 11m 22s
  • 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

2 lectures 7m 46s
  • 1. Comparison Operators
    2m 10s
  • 2. Logical and Identity Operators
    5m 36s

4 lectures 13m 29s
  • 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

6 lectures 15m 25s
  • 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

5 lectures 19m 12s
  • 1. Lists
    4m 2s
  • 2. Using Methods
    3m 22s
  • 3. List Slicing
    4m 31s
  • 4. Tuples
    3m 13s
  • 5. Dictionaries
    4m 4s

6 lectures 15m 53s
  • 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

10 lectures 41m 53s
  • 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

10 lectures 42m 41s
  • 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

10 lectures 41m 27s
  • 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

4 lectures 21m 39s
  • 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

4 lectures 19m 34s
  • 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

8 lectures 27m 10s
  • 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

2 lectures 12m 2s
  • 1. Multivariate regression analysis - a valuable tool for finance practitioners
    5m 42s
  • 2. Running a multivariate regression in Python
    6m 20s

13 lectures 56m 56s
  • 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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