Udemy - Python for Finance: Investment Fundamentals & Data Analytics [Course Drive]
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| Course Downloaded from coursedrive.org.txt | 538 B | ||
| Python for Finance - Investment Fundamentals & Data Analytics | |||
| 01 Welcome Course Introduction | |||
| 001 What Does the Course Cover.mp4 | 13.06 MB | ||
| 002 Download Useful Resources - Exercises and Solutions.mp4 | 8.21 MB | ||
| Must Read.txt | 540 B | ||
| Visit Coursedrive.org.url | 124 B | ||
| 02 Introduction to programming with Python | |||
| 003 Programming Explained in 5 Minutes.mp4 | 14.9 MB | ||
| 004 Why Python.mp4 | 15.66 MB | ||
| 005 Why Jupyter.mp4 | 9.86 MB | ||
| 006 Installing Python and Jupyter.mp4 | 15.48 MB | ||
| 007 Jupyters Interface the Dashboard.mp4 | 6.38 MB | ||
| 008 Jupyters Interface Prerequisites for Coding.mp4 | 12.31 MB | ||
| attached_files | |||
| 003 Programming Explained in 5 Minutes | |||
| Python-for-Finance-Course-Notes-Part-I.pdf | 2.23 MB | ||
| quizzes | |||
| 001 Programming Explained in 5 Minutes.html | 3.69 KB | ||
| 002 Why Python.html | 3.66 KB | ||
| 003 Why Jupyter.html | 3.58 KB | ||
| 004 Jupyters Interface.html | 4.67 KB | ||
| 03 Python Variables and Data Types | |||
| 009 Variables.mp4 | 7.22 MB | ||
| 010 Numbers and Boolean Values.mp4 | 6.57 MB | ||
| 011 Strings.mp4 | 11.94 MB | ||
| ReadMe.txt | 538 B | ||
| Visit Coursedrive.org.url | 124 B | ||
| quizzes | |||
| 005 Variables.html | 2.58 KB | ||
| 006 Numbers and Boolean Values.html | 2.46 KB | ||
| 007 Strings.html | 4.69 KB | ||
| 04 Basic Python Syntax | |||
| 012 Arithmetic Operators.mp4 | 7.16 MB | ||
| 013 The Double Equality Sign.mp4 | 2.86 MB | ||
| 014 Reassign Values.mp4 | 2.08 MB | ||
| 015 Add Comments.mp4 | 2.62 MB | ||
| 016 Line Continuation.mp4 | 1.35 MB | ||
| 017 Indexing Elements.mp4 | 2.64 MB | ||
| 018 Structure Your Code with Indentation.mp4 | 3.25 MB | ||
| quizzes | |||
| 008 Arithmetic Operators.html | 2.48 KB | ||
| 009 The Double Equality Sign.html | 2.48 KB | ||
| 010 Reassign values.html | 2.71 KB | ||
| 011 Add Comments.html | 2.49 KB | ||
| 012 Indexing Elements.html | 2.5 KB | ||
| 013 Structure Your Code with Indentation.html | 2.48 KB | ||
| 05 Python Operators Continued | |||
| 019 Comparison Operators.mp4 | 4.2 MB | ||
| 020 Logical and Identity Operators.mp4 | 11.88 MB | ||
| quizzes | |||
| 014 Comparison Operators.html | 3.45 KB | ||
| 015 Logical and Identity Operators.html | 3.25 KB | ||
| 06 Conditional Statements | |||
| 021 Introduction to the IF statement.mp4 | 5.7 MB | ||
| 022 Add an ELSE statement.mp4 | 5.24 MB | ||
| 023 Else if for Brief ELIF.mp4 | 11.45 MB | ||
| 024 A Note on Boolean values.mp4 | 4.44 MB | ||
| ReadMe.txt | 538 B | ||
| Visit Coursedrive.org.url | 124 B | ||
| quizzes | |||
| 016 Introduction to the IF statement.html | 2.63 KB | ||
| 017 A Note on Boolean Values.html | 2.5 KB | ||
| 07 Python Functions | |||
| 025 Defining a Function in Python.mp4 | 3.6 MB | ||
| 026 Creating a Function with a Parameter.mp4 | 8.11 MB | ||
| 027 Another Way to Define a Function.mp4 | 5.29 MB | ||
| 028 Using a Function in another Function.mp4 | 3.25 MB | ||
| 029 Creating Functions Containing a Few Arguments.mp4 | 2.52 MB | ||
| 030 Combining Conditional Statements and Functions.mp4 | 6.21 MB | ||
| 031 Creating Functions Containing a Few Arguments.mp4 | 2.52 MB | ||
| 032 Notable Built-in Functions in Python.mp4 | 8.45 MB | ||
| quizzes | |||
| 018 Functions.html | 3.86 KB | ||
| 08 Python Sequences | |||
| 033 Lists.mp4 | 8.37 MB | ||
| 034 Using Methods.mp4 | 7.23 MB | ||
| 035 List Slicing.mp4 | 10.35 MB | ||
| 036 Tuples.mp4 | 6.48 MB | ||
| 037 Dictionaries.mp4 | 9.03 MB | ||
| quizzes | |||
| 019 Lists.html | 2.42 KB | ||
| 020 Using Methods.html | 2.67 KB | ||
| 021 Dictionaries.html | 2.67 KB | ||
| 09 Using Iterations in Python | |||
| 038 For Loops.mp4 | 4.84 MB | ||
| 039 While Loops and Incrementing.mp4 | 6.67 MB | ||
| 040 Create Lists with the range Function.mp4 | 4.46 MB | ||
| 041 Use Conditional Statements and Loops Together.mp4 | 6.05 MB | ||
| 042 All In Conditional Statements Functions and Loops.mp4 | 4.36 MB | ||
| 043 Iterating over Dictionaries.mp4 | 6.46 MB | ||
| quizzes | |||
| 022 For Loops.html | 2.43 KB | ||
| 023 Create Lists with the range Function.html | 2.73 KB | ||
| 10 Useful tips and tools | |||
| 044 Object Oriented Programming.mp4 | 9.47 MB | ||
| 045 Modules and Packages.mp4 | 2.47 MB | ||
| 046 The Standard Library.mp4 | 5.87 MB | ||
| 047 Importing Modules.mp4 | 8.29 MB | ||
| 048 Must-have packages for Finance and Data Science.mp4 | 12.42 MB | ||
| 049 Working with arrays.mp4 | 11.21 MB | ||
| 050 Generating Random Numbers.mp4 | 5.66 MB | ||
| 051 Importing and Organizing Data in Python part I.mp4 | 9.54 MB | ||
| 052 Importing and Organizing Data in Python part II.mp4 | 21.48 MB | ||
| 053 Importing and Organizing Data in Python part III.mp4 | 11.54 MB | ||
| ReadMe.txt | 538 B | ||
| Visit Coursedrive.org.url | 124 B | ||
| quizzes | |||
| 024 Object Oriented Programming - Quiz.html | 3.36 KB | ||
| 025 Modules - Quiz.html | 3.42 KB | ||
| 026 The Standard Library - Quiz.html | 2.51 KB | ||
| 027 Importing Modules - Quiz.html | 3.47 KB | ||
| 028 Must-have packages - Quiz.html | 4.57 KB | ||
| 11 PART II FINANCE Calculating and Comparing Rates of Return in Python | |||
| 054 Considering both risk and return.mp4 | 11.59 MB | ||
| 055 What are we going to see next.mp4 | 5.47 MB | ||
| 056 Calculating a securitys rate of return.mp4 | 11.54 MB | ||
| 057 Calculating a Securitys Rate of Return in Python Simple Returns Part I.mp4 | 13.95 MB | ||
| 058 Calculating a Securitys Rate of Return in Python Simple Returns Part II.mp4 | 7.69 MB | ||
| 059 Calculating a Securitys Return in Python Logarithmic Returns.mp4 | 9.02 MB | ||
| 060 What is a portfolio of securities and how to calculate its rate of return.mp4 | 5.69 MB | ||
| 061 Calculating the Rate of Return of a Portfolio of Securities.mp4 | 22.3 MB | ||
| 062 Popular stock indices that can help us understand financial markets.mp4 | 7.72 MB | ||
| 063 Calculating the Rate of Return of Indices.mp4 | 12.59 MB | ||
| attached_files | |||
| 054 Considering both risk and return | |||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| 055 What are we going to see next | |||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (1).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (2).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (3).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (4).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| quizzes | |||
| 029 Risk and return - Quiz.html | 2.59 KB | ||
| 030 Calculating a securitys rate of return.html | 2.72 KB | ||
| 031 What is a portfolio of securities and how to calculate its rate of return - Quiz.html | 2.65 KB | ||
| 032 Which of the following is not an index - Quiz.html | 2.43 KB | ||
| 12 PART II Finance Measuring Investment Risk | |||
| 064 How do we measure a securitys risk.mp4 | 12.49 MB | ||
| 065 Calculating a Securitys Risk in Python.mp4 | 16.39 MB | ||
| 066 The benefits of portfolio diversification.mp4 | 8.18 MB | ||
| 067 Calculating the covariance between securities.mp4 | 7.69 MB | ||
| 068 Measuring the correlation between stocks.mp4 | 7.65 MB | ||
| 069 Calculating Covariance and Correlation.mp4 | 12.19 MB | ||
| 070 Considering the risk of multiple securities in a portfolio.mp4 | 14.3 MB | ||
| 071 Calculating Portfolio Risk.mp4 | 6.34 MB | ||
| 072 Understanding Systematic vs. Idiosyncratic risk.mp4 | 6.97 MB | ||
| 073 Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio.mp4 | 10.42 MB | ||
| attached_files | |||
| 064 How do we measure a securitys risk | |||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| 066 The benefits of portfolio diversification | |||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| 067 Calculating the covariance between securities | |||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| 068 Measuring the correlation between stocks | |||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| 070 Considering the risk of multiple securities in a portfolio | |||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| 072 Understanding Systematic vs. Idiosyncratic risk | |||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (1).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (2).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (3).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (4).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (5).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| quizzes | |||
| 033 Which of the following sentences is true - Quiz.html | 2.66 KB | ||
| 034 Investing in stocks - Quiz.html | 2.57 KB | ||
| 035 Covariance - Quiz.html | 2.71 KB | ||
| 036 Correlation - Quiz.html | 2.48 KB | ||
| 037 Diversifiable Risk - Quiz.html | 2.57 KB | ||
| 13 PART II Finance - Using Regressions for Financial Analysis | |||
| 074 The fundamentals of simple regression analysis.mp4 | 7.44 MB | ||
| 075 Running a Regression in Python.mp4 | 14.24 MB | ||
| 076 Are all regressions created equal Learning how to distinguish good regressions.mp4 | 10.55 MB | ||
| 077 Computing Alpha Beta and R Squared in Python.mp4 | 14.44 MB | ||
| attached_files | |||
| 074 The fundamentals of simple regression analysis | |||
| Housing-Data.xlsx | 14.44 KB | ||
| 075 Running a Regression in Python | |||
| Housing.xlsx | 10.04 KB | ||
| 077 Computing Alpha Beta and R Squared in Python | |||
| Housing.xlsx | 10.04 KB | ||
| Housing (1).xlsx | 10.04 KB | ||
| Housing-Data.xlsx | 14.44 KB | ||
| Housing.xlsx | 10.04 KB | ||
| Python-for-Finance-Course-Notes-Part-II (1).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| quizzes | |||
| 038 Regressions - Quiz.html | 2.53 KB | ||
| 039 Regressions - Quiz.html | 2.52 KB | ||
| 14 PART II Finance - Markowitz Portfolio Optimization | |||
| 078 Markowitz Portfolio Theory - One of the main pillars of modern Finance.mp4 | 13.32 MB | ||
| 079 Obtaining the Efficient Frontier in Python Part I.mp4 | 12.53 MB | ||
| 080 Obtaining the Efficient Frontier in Python Part II.mp4 | 16.03 MB | ||
| 081 Obtaining the Efficient Frontier in Python Part III.mp4 | 5.4 MB | ||
| ReadMe.txt | 538 B | ||
| Visit Coursedrive.org.url | 124 B | ||
| attached_files | |||
| 14.Markowitz-Efficient-frontier.xlsx | 15.49 KB | ||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| quizzes | |||
| 040 Markowitz - Quiz.html | 2.61 KB | ||
| 15 Part II Finance - The Capital Asset Pricing Model | |||
| 082 The intuition behind the Capital Asset Pricing Model CAPM.mp4 | 10.02 MB | ||
| 083 Understanding and calculating a securitys Beta.mp4 | 8.29 MB | ||
| 084 Calculating the Beta of a Stock.mp4 | 8.27 MB | ||
| 085 The CAPM formula.mp4 | 7.66 MB | ||
| 086 Calculating the Expected Return of a Stock CAPM.mp4 | 5.31 MB | ||
| 087 Introducing the Sharpe ratio and the way it can be applied in practice.mp4 | 5.09 MB | ||
| 088 Obtaining the Sharpe ratio in Python.mp4 | 2.67 MB | ||
| 089 Measuring alpha and verifying how good or bad a portfolio manager is doing.mp4 | 8.32 MB | ||
| attached_files | |||
| 083 Understanding and calculating a securitys Beta | |||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| 085 The CAPM formula | |||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (1).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (2).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (3).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (4).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| quizzes | |||
| 041 CAPM - Quiz.html | 2.62 KB | ||
| 042 Beta - Quiz.html | 2.48 KB | ||
| 043 CAPM - Quiz.html | 2.55 KB | ||
| 044 Sharpe ratios - Quiz.html | 2.57 KB | ||
| 045 Alpha - Quiz.html | 2.49 KB | ||
| 16 Part II Finance Multivariate regression analysis | |||
| 090 Multivariate regression analysis - a valuable tool for finance practitioners.mp4 | 12.44 MB | ||
| 091 Running a multivariate regression in Python.mp4 | 21.81 MB | ||
| attached_files | |||
| Housing.xlsx | 10.04 KB | ||
| Python-for-Finance-Course-Notes-Part-II (4).pdf | 1.36 MB | ||
| quizzes | |||
| 046 Multivariate Regressions - Quiz.html | 2.54 KB | ||
| 17 PART II Finance - Monte Carlo simulations as a decision-making tool | |||
| An Introduction to Derivative Contracts.mp4 | 12.21 MB | ||
| Forecasting Stock Prices with a Monte Carlo Simulation.mp4 | 8.31 MB | ||
| Monte Carlo - Black-Scholes-Merton.mp4 | 13.61 MB | ||
| Monte Carlo - Euler Discretization - Part I.mp4 | 14.42 MB | ||
| Monte Carlo - Euler Discretization - Part II.mp4 | 5.43 MB | ||
| Monte Carlo - Forecasting Stock Prices - Part I.mp4 | 8.21 MB | ||
| Monte Carlo - Forecasting Stock Prices - Part II.mp4 | 10.86 MB | ||
| Monte Carlo - Forecasting Stock Prices - Part III.mp4 | 10.25 MB | ||
| Monte Carlo - Predicting Gross Profit – Part I.mp4 | 18.6 MB | ||
| Monte Carlo - Predicting Gross Profit – Part II.mp4 | 6.69 MB | ||
| Monte Carlo applied in a Corporate Finance context.mp4 | 5.93 MB | ||
| ReadMe.txt | 538 B | ||
| The Black Scholes Formula for Option Pricing.mp4 | 8.95 MB | ||
| The essence of Monte Carlo simulations.mp4 | 5.11 MB | ||
| Visit Coursedrive.org.url | 124 B | ||
| attached_files | |||
| Python-for-Finance-Course-Notes-Part-II (1).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (2).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II (3).pdf | 1.36 MB | ||
| Python-for-Finance-Course-Notes-Part-II.pdf | 1.36 MB | ||
| quizzes | |||
| 047 Monte Carlo - Quiz.html | 2.65 KB | ||
| ReadMe.txt | 538 B | ||
| Visit Coursedrive.org.url | 124 B | ||
| Visit Coursedrive.org.url | 124 B |
Description
Python for Finance: Investment Fundamentals & Data Analytics Download
Learn Python Programming and Conduct Real-World Financial Analysis in Python - Complete Python Training
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
Requirements
You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course
All software and data used in the course is free
Description
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 makes this course different from the rest of the Programming and Finance courses out there?
This course will teach you how to code in Python and apply these skills in the world of Finance. It is both a Programming and a Finance course.
High-quality production – HD video and animations (this isn’t a collection of boring lectures!)
Knowledgeable instructors. Martin is a quant geek fascinated by the world of Data Science, and Ned is a finance practitioner with several years of experience who loves explaining Finance topics in real life and here on Udemy.
Complete training – we will cover all the major topics you need to understand to start coding in Python and solving the financial topics introduced in this course (and they are many!)
Extensive Case Studies that will help you reinforce everything you’ve learned.
Course Challenge: Solve our exercises and make this course an interactive experience.
Excellent support: If you don’t understand a concept or you simply want to drop us a line, you’ll receive an answer within 1 business day.
Dynamic: We don’t want to waste your time! The instructors set a very good pace throughout the whole course.
Please don’t forget that the course comes with Udemy’s 30-day unconditional, money-back-in-full guarantee. And why not give such a guarantee, when we are convinced the course will provide a ton of value for you?
Just subscribe to this course! If you don't acquire these skills now, you will miss an opportunity to separate yourself from the others. Don't risk your future success! Let's start learning together now!
Who this course is for:
Aspiring data scientists
Programming beginners
People interested in finance and investments
Programmers who want to specialize in finance
Everyone who wants to learn how to code and apply their skills in practice
Finance graduates and professionals who need to better apply their knowledge in Python
visit for more freetutorials
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