**Python for Finance: Investment Fundamentals & Data Analytics**

English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 7 Hours | 994 MB

Learn Python Programming and Conduct Real-World Financial Analysis in Python – Complete Python Training

Do you want to learn how to use Python in a working environment?

Are you a young professional interested in a career in Data Science?

Would you like to explore how Python can be applied in the world of Finance and solve portfolio optimization problems?

If so, then this is the right course for you!

We are proud to present Python for Finance: Investment Fundamentals and Data Analytics – one of the most interesting and complete courses we have created so far. It took our team slightly over four months to create this course, but now, it is ready and waiting for you.

An exciting journey from A-Z.

If you are a complete beginner and you know nothing about coding, don’t worry! We start from the very basics. The first part of the course is ideal for beginners and people who want to brush up on their Python skills. And then, once we have covered the basics, we will be ready to tackle financial calculations and portfolio optimization tasks.

Finance Fundamentals.

And it gets even better! The Finance block of this course will teach you in-demand real-world skills employers are looking for. To be a high-paid programmer, you will have to specialize in a particular area of interest. In this course, we will focus on Finance, covering many tools and techniques used by finance professionals daily:

- Rate of return of stocks
- Risk of stocks
- Rate of return of stock portfolios
- Risk of stock portfolios
- Correlation between stocks
- Covariance
- Diversifiable and non-diversifiable risk
- Regression analysis
- Alpha and Beta coefficients
- Measuring a regression’s explanatory power with R^2
- Markowitz Efficient frontier calculation
- Capital asset pricing model
- Sharpe ratio
- Multivariate regression analysis
- Monte Carlo simulations
- Using Monte Carlo in a Corporate Finance context
- Derivatives and type of derivatives
- Applying the Black Scholes formula
- Using Monte Carlo for options pricing
- Using Monte Carlo for stock pricing

Everything is included! All these topics are first explained in theory and then applied in practice using Python.

Is there a better way to reinforce what you have learned in the first part of the course?

This course is great, even if you are an experienced programmer, as we will teach you a great deal about the finance theory and mechanics you will need if you start working in a finance context.

Teaching is our passion.

Everything we teach is explained in the best way possible. Plain and clear English, relevant examples and time-efficient videos. Don’t forget to check some of our sample videos to see how easy they are to understand.

If you have questions, contact us! We enjoy communicating with our students and take pride in responding within the 1 business day. Our goal is to create high-end materials that are fun, exciting, career-enhancing, and rewarding.

What you’ll learn

- Learn how to code in Python
- Take your career to the next level
- Work with Python’s conditional statements, functions, sequences, and loops
- Work with scientific packages, like NumPy
- Understand how to use the data analysis toolkit, Pandas
- Plot graphs with Matplotlib
- Use Python to solve real-world tasks
- Get a job as a data scientist with Python
- Acquire solid financial acumen
- Carry out in-depth investment analysis
- Build investment portfolios
- Calculate risk and return of individual securities
- Calculate risk and return of investment portfolios
- Apply best practices when working with financial data
- Use univariate and multivariate regression analysis
- Understand the Capital Asset Pricing Model
- Compare securities in terms of their Sharpe ratio
- Perform Monte Carlo simulations
- Learn how to price options by applying the Black Scholes formula
- Be comfortable applying for a developer job in a financial institution

**Table of Contents**

**Welcome! Course Introduction**

1 What Does the Course Cover?

2 Download Useful Resources – Exercises and Solutions

**Introduction to programming with Python**

3 Programming Explained in 5 Minutes

4 Why Python?

5 Why Jupyter?

6 Installing Python and Jupyter

7 Jupyter’s Interface – the Dashboard

8 Jupyter’s Interface – Prerequisites for Coding

9 Python 2 vs Python 3: What’s the Difference?

**Python Variables and Data Types**

10 Variables

11 Numbers and Boolean Values

12 Strings

**Basic Python Syntax**

13 Arithmetic Operators

14 The Double Equality Sign

15 Reassign Values

16 Add Comments

17 Line Continuation

18 Indexing Elements

19 Structure Your Code with Indentation

**Python Operators Continued**

20 Comparison Operators

21 Logical and Identity Operators

**Conditional Statements**

22 Introduction to the IF statement

23 Add an ELSE statement

24 Else if, for Brief – ELIF

25 A Note on Boolean values

**Python Functions**

26 Defining a Function in Python

27 Creating a Function with a Parameter

28 Another Way to Define a Function

29 Using a Function in another Function

30 Combining Conditional Statements and Functions

31 Creating Functions Containing a Few Arguments

32 Notable Built-in Functions in Python

**Python Sequences**

33 Lists

34 Using Methods

35 List Slicing

36 Tuples

37 Dictionaries

**Using Iterations in Python**

38 For Loops

39 While Loops and Incrementing

40 Create Lists with the range() Function

41 Use Conditional Statements and Loops Together

42 All In – Conditional Statements, Functions, and Loops

43 Iterating over Dictionaries

**Advanced Python tools**

44 Object Oriented Programming

45 Modules and Packages

46 The Standard Library

47 Importing Modules

48 Must-have packages for Finance and Data Science

49 Working with arrays

50 Generating Random Numbers

51 A Note on Using Financial Data in Python

52 Sources of Financial Data

53 Accessing the Notebook Files

54 Importing and Organizing Data in Python – part I

55 Importing and Organizing Data in Python – part II.A

56 Importing and Organizing Data in Python – part II.B

57 Importing and Organizing Data in Python – part III

58 Changing the Index of Your Time-Series Data

59 Restarting the Jupyter Kernel

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

60 Considering both risk and return

61 What are we going to see next?

62 Calculating a security’s rate of return

63 Calculating a Security’s Rate of Return in Python – Simple Returns – Part I

64 Calculating a Security’s Rate of Return in Python – Simple Returns – Part II

65 Calculating a Security’s Return in Python – Logarithmic Returns

66 What is a portfolio of securities and how to calculate its rate of return

67 Calculating a Portfolio of Securities’ Rate of Return

68 Popular stock indices that can help us understand financial markets

69 Calculating the Indices’ Rate of Return

**PART II Finance: Measuring Investment Risk**

70 How do we measure a security’s risk?

71 Calculating a Security’s Risk in Python

72 The benefits of portfolio diversification

73 Calculating the covariance between securities

74 Measuring the correlation between stocks

75 Calculating Covariance and Correlation

76 Considering the risk of multiple securities in a portfolio

77 Calculating Portfolio Risk

78 Understanding Systematic vs. Idiosyncratic risk

79 Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio

**PART II Finance – Using Regressions for Financial Analysis**

80 The fundamentals of simple regression analysis

81 Running a Regression in Python

82 Are all regressions created equal? Learning how to distinguish good regressions

83 Computing Alpha, Beta, and R Squared in Python

**PART II Finance – Markowitz Portfolio Optimization**

84 Markowitz Portfolio Theory – One of the main pillars of modern Finance

85 Obtaining the Efficient Frontier in Python – Part I

86 Obtaining the Efficient Frontier in Python – Part II

87 Obtaining the Efficient Frontier in Python – Part III

**Part II Finance – The Capital Asset Pricing Model**

88 The intuition behind the Capital Asset Pricing Model (CAPM)

89 Understanding and calculating a security’s Beta

90 Calculating the Beta of a Stock

91 The CAPM formula

92 Calculating the Expected Return of a Stock (CAPM)

93 Introducing the Sharpe ratio and how to put it into practice

94 Obtaining the Sharpe ratio in Python

95 Measuring alpha and verifying how good (or bad) a portfolio manager is doing

**Part II Finance: Multivariate regression analysis**

96 Multivariate regression analysis – a valuable tool for finance practitioners

97 Running a multivariate regression in Python

**PART II Finance – Monte Carlo simulations as a decision-making tool**

98 The essence of Monte Carlo simulations

99 Monte Carlo applied in a Corporate Finance context

100 Monte Carlo: Predicting Gross Profit – Part I

101 Monte Carlo: Predicting Gross Profit – Part II

102 Forecasting Stock Prices with a Monte Carlo Simulation

103 Monte Carlo: Forecasting Stock Prices – Part I

104 Monte Carlo: Forecasting Stock Prices – Part II

105 Monte Carlo: Forecasting Stock Prices – Part III

106 An Introduction to Derivative Contracts

107 The Black Scholes Formula for Option Pricing

108 Monte Carlo: Black-Scholes-Merton

109 Monte Carlo: Euler Discretization – Part I

110 Monte Carlo: Euler Discretization – Part II

**BONUS LECTURE**

111 Bonus Lecture: Next Steps

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