Section 1 : Welcome! Course Introduction

 		Lecture 1 What Does the Course Cover 5:10
Lecture 2 About Proctor Testing Pdf

Section 2 : Introduction to programming with Python

 		Lecture 3 Programming Explained in 5 Minutes 5:4
Lecture 4 Why Python 5:11
Lecture 5 Why Jupyter 3:29
Lecture 6 Installing Python and Jupyter 7:12
Lecture 7 Jupyter’s Interface – the Dashboard 3:16
Lecture 8 Jupyter’s Interface – Prerequisites for Coding 6:15
Lecture 9 Python 2 vs Python 3 What's the Difference 2:57

Section 3 : Python Variables and Data Types

 		Lecture 10 Variables
Lecture 11 Numbers and Boolean Values 3:6
Lecture 12 Strings 12:18

Section 4 : Basic Python Syntax

 		Lecture 13 Arithmetic Operators 3:24
Lecture 14 The Double Equality Sign 1:34
Lecture 15 Reassign Values 1:9
Lecture 16 Add Comments 3:20
Lecture 17 Line Continuation 0:50
Lecture 18 Indexing Elements 1:18
Lecture 19 Structure Your Code with Indentation 3:42

Section 5 : Python Operators Continued

 		Lecture 20 Comparison Operators 2:11
Lecture 21 Logical and Identity Operators 5:36

Section 6 : Conditional Statements

 		Lecture 22 Introduction to the IF statement 6:14
Lecture 23 Add an ELSE statement 5:38
Lecture 24 Else if, for Brief – ELIF 11:16
Lecture 25 A Note on Boolean values 4:39

Section 7 : Python Functions

 		Lecture 26 Defining a Function in Python 4:20
Lecture 27 Creating a Function with a Parameter 7:58
Lecture 28 Another Way to Define a Function 5:29
Lecture 29 Using a Function in another Function
Lecture 30 Combining Conditional Statements and Functions 3:7
Lecture 31 Creating Functions Containing a Few Arguments 2:48
Lecture 32 Notable Built-in Functions in Python 3:56

Section 8 : Python Sequences

 		Lecture 33 Lists 8:18
Lecture 34 Using Methods 6:54
Lecture 35 List Slicing 4:31
Lecture 36 Tuples 6:40
Lecture 37 Dictionaries 8:27

Section 9 : Using Iterations in Python

 		Lecture 38 For Loops 5:40
Lecture 39 While Loops and Incrementing 5:11
Lecture 40 Create Lists with the range() Function 6:22
Lecture 41 Use Conditional Statements and Loops Together 6:30
Lecture 42 All In – Conditional Statements, Functions, and Loops 2:27
Lecture 43 Iterating over Dictionaries 6:22

Section 10 : Advanced Python tools

 		Lecture 44 Object Oriented Programming 5:0
Lecture 45 Modules and Packages 1:6
Lecture 46 The Standard Library 2:47
Lecture 47 Importing Modules 4:10
Lecture 48 Must-have packages for Finance and Data Science 4:54
Lecture 49 Working with arrays 6:2
Lecture 50 Generating Random Numbers 2:52
Lecture 51 A Note on Using Financial Data in Python 2:42
Lecture 52 Sources of Financial Data
Lecture 53 Accessing the Notebook Files 2:35
Lecture 54 Importing and Organizing Data in Python – part I 3:44
Lecture 55 Importing and Organizing Data in Python – part II 7:2
Lecture 56 Importing and Organizing Data in Python – part II 4:38
Lecture 57 Importing and Organizing Data in Python – part III 4:19
Lecture 58 Changing the Index of Your Time-Series Data 3:17
Lecture 59 Restarting the Jupyter Kernel 2:17

Section 11 : PART II FINANCE Calculating and Comparing Rates of Return in Python

 		Lecture 60 Considering both risk and return 2:33
Lecture 61 What are we going to see next 2:35
Lecture 62 Calculating a security's rate of return 5:31
Lecture 63 Calculating a Security’s Rate of Return in Python – Simple Returns – Part I 5:24
Lecture 64 Calculating a Security’s Rate of Return in Python – Simple Returns – Part II
Lecture 65 Calculating a Security’s Return in Python – Logarithmic Returns 3:40
Lecture 66 What is a portfolio of securities and how to calculate its rate of return 2:39
Lecture 67 Calculating a Portfolio of Securities' Rate of Return 8:35
Lecture 68 Popular stock indices that can help us understand financial markets 3:24
Lecture 69 Calculating the Indices' Rate of Return 5:3

Section 12 : PART II Finance Measuring Investment Risk

 		Lecture 70 How do we measure a security's risk 6:6
Lecture 71 Calculating a Security’s Risk in Python 5:56
Lecture 72 The benefits of portfolio diversification 3:28
Lecture 73 Calculating the covariance between securities 3:35
Lecture 74 Measuring the correlation between stocks 3:59
Lecture 75 Calculating Covariance and Correlation 5:0
Lecture 76 Considering the risk of multiple securities in a portfolio 3:20
Lecture 77 Calculating Portfolio Risk 2:39
Lecture 78 Understanding Systematic vs 2:59
Lecture 79 Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio 4:28

Section 13 : PART II Finance - Using Regressions for Financial Analysis

 		Lecture 80 The fundamentals of simple regression analysis 3:55
Lecture 81 Running a Regression in Python 6:35
Lecture 82 Are all regressions created equal Learning how to distinguish good regressions 4:55
Lecture 83 mp4 6:14

Section 14 : PART II Finance - Markowitz Portfolio Optimization

 		Lecture 84 Markowitz Portfolio Theory - One of the main pillars of modern Finance 6:34
Lecture 85 Obtaining the Efficient Frontier in Python – Part I 5:36
Lecture 86 Obtaining the Efficient Frontier in Python – Part II
Lecture 87 Obtaining the Efficient Frontier in Python – Part III 2:8

Section 15 : Part II Finance - The Capital Asset Pricing Model

 		Lecture 88 The intuition behind the Capital Asset Pricing Model (CAPM) 4:45
Lecture 89 Understanding and calculating a security's Beta 4:15
Lecture 90 Calculating the Beta of a Stock 3:38
Lecture 91 The CAPM formula 4:20
Lecture 92 Calculating the Expected Return of a Stock (CAPM) 2:16
Lecture 93 Introducing the Sharpe ratio and how to put it into practice 2:21
Lecture 94 Obtaining the Sharpe ratio in Python 1:23
Lecture 95 Measuring alpha and verifying how good (or bad) a portfolio manager is doing 4:13

Section 16 : Part II Finance Multivariate regression analysis

 		Lecture 96 Multivariate regression analysis - a valuable tool for finance practitioners 5:42
Lecture 97 Running a multivariate regression in Python 6:21

Section 17 : PART II Finance - Monte Carlo simulations as a decision-making tool

 		Lecture 98 The essence of Monte Carlo simulations 2:32
Lecture 99 Monte Carlo applied in a Corporate Finance context 2:31
Lecture 100 Monte Carlo Predicting Gross Profit – Part I 6:3
Lecture 101 Monte Carlo Predicting Gross Profit – Part II 2:57
Lecture 102 Forecasting Stock Prices with a Monte Carlo Simulation 4:28
Lecture 103 Monte Carlo Forecasting Stock Prices - Part I 3:39
Lecture 104 Monte Carlo Forecasting Stock Prices - Part II 4:39
Lecture 105 Monte Carlo Forecasting Stock Prices - Part III 4:18
Lecture 106 An Introduction to Derivative Contracts 6:33
Lecture 107 The Black Scholes Formula for Option Pricing 4:52
Lecture 108 Monte Carlo Black-Scholes-Merton 6:1
Lecture 109 Monte Carlo Euler Discretization - Part I 6:22
Lecture 110 Monte Carlo Euler Discretization - Part II 2:10