Python for Finance: Investment Fundamentals & Data Analytics

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01 - Welcome! Course Introduction

001 What Does the Course Cover.mp4

05:10

002 Download Useful Resources - Exercises and Solutions.mp4

02:54

002 Python-for-Finance-FAQ.pdf

external-links.txt

02 - Introduction to programming with Python

001 Programming Explained in 5 Minutes.mp4

05:04

001 Python-for-Finance-Course-Notes-Part-I.pdf

002 Why Python.mp4

05:11

003 Why Jupyter.mp4

03:29

004 Installing Python and Jupyter.mp4

07:12

005 Jupyters Interface the Dashboard.mp4

03:15

006 Jupyters Interface Prerequisites for Coding.mp4

06:15

006 Jupyter-Shortcuts.pdf

007 Python 2 vs Python 3 Whats the Difference.mp4

02:56

007 Python-for-Finance-Course-Notes-Part-I.pdf

03 - Python Variables and Data Types

001 Variables.mp4

04:51

002 Numbers and Boolean Values.mp4

03:05

003 Strings.mp4

06:38

external-links.txt

04 - Basic Python Syntax

001 Arithmetic Operators.mp4

03:23

002 The Double Equality Sign.mp4

01:33

003 Reassign Values.mp4

01:08

004 Add Comments.mp4

01:34

005 Line Continuation.mp4

00:49

006 Indexing Elements.mp4

01:18

007 Structure Your Code with Indentation.mp4

01:44

external-links.txt

05 - Python Operators Continued

001 Comparison Operators.mp4

02:10

002 Logical and Identity Operators.mp4

05:36

external-links.txt

06 - Conditional Statements

001 Introduction to the IF statement.mp4

03:01

002 Add an ELSE statement.mp4

02:45

003 Else if, for Brief ELIF.mp4

05:34

004 A Note on Boolean Values.mp4

02:14

external-links.txt

07 - Python Functions

001 Defining a Function in Python.mp4

02:02

002 Creating a Function with a Parameter.mp4

03:49

003 Another Way to Define a Function.mp4

02:36

004 Using a Function in another Function.mp4

01:49

005 Combining Conditional Statements and Functions.mp4

03:06

006 Creating Functions Containing a Few Arguments.mp4

01:17

007 Notable Built-in Functions in Python.mp4

03:56

external-links.txt

08 - Python Sequences

001 Lists.mp4

04:02

002 Using Methods.mp4

03:19

003 List Slicing.mp4

04:31

004 Tuples.mp4

03:11

005 Dictionaries.mp4

04:04

external-links.txt

09 - Using Iterations in Python

001 For Loops.mp4

02:56

002 While Loops and Incrementing.mp4

02:26

003 Create Lists with the range() Function.mp4

03:49

004 Use Conditional Statements and Loops Together.mp4

03:11

005 All In Conditional Statements, Functions, and Loops.mp4

02:27

006 Iterating over Dictionaries.mp4

03:07

external-links.txt

10 - Advanced Python tools

001 Object Oriented Programming.mp4

05:00

002 Modules and Packages.mp4

01:06

003 The Standard Library.mp4

02:47

004 Importing Modules.mp4

04:10

005 47-Packages-Exercise.pdf

005 Must-have packages for Finance and Data Science.mp4

04:53

006 Working with arrays.mp4

06:02

007 Generating Random Numbers.mp4

02:52

008 A Note on Using Financial Data in Python.mp4

02:42

009 Sources of Financial Data.mp4

06:49

010 Accessing the Notebook Files.mp4

02:35

011 Importing and Organizing Data in Python part I.mp4

03:44

012 Importing and Organizing Data in Python part II.A.mp4

07:02

013 Importing and Organizing Data in Python part II.B.mp4

04:37

014 Importing and Organizing Data in Python part III.mp4

04:19

015 Changing the Index of Your Time-Series Data.mp4

03:17

016 Restarting the Jupyter Kernel.mp4

02:17

external-links.txt

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

001 Considering both risk and return.mp4

02:33

001 Python-for-Finance-Course-Notes-Part-II.pdf

002 Python-for-Finance-Course-Notes-Part-II.pdf

002 What are we going to see next.mp4

02:34

003 Calculating a securitys rate of return.mp4

05:31

003 Python-for-Finance-Course-Notes-Part-II.pdf

004 Calculating a Securitys Rate of Return in Python - Simple Returns - Part I.mp4

05:25

005 Calculating a Securitys Rate of Return in Python - Simple Returns - Part II.mp4

03:30

006 Calculating a Securitys Return in Python - Logarithmic Returns.mp4

03:43

007 Python-for-Finance-Course-Notes-Part-II.pdf

007 What is a portfolio of securities and how to calculate its rate of return.mp4

02:42

008 Calculating a Portfolio of Securities Rate of Return.mp4

08:37

009 Popular stock indices that can help us understand financial markets.mp4

03:27

009 Python-for-Finance-Course-Notes-Part-II.pdf

010 Calculating the Indices Rate of Return.mp4

05:03

010 Indices-Data-1.csv

010 Indices-Data-2.csv

external-links.txt

12 - PART II Finance Measuring Investment Risk

001 How do we measure a securitys risk.mp4

06:05

001 Python-for-Finance-Course-Notes-Part-II.pdf

002 Calculating a Securitys Risk in Python.mp4

05:55

003 Python-for-Finance-Course-Notes-Part-II.pdf

003 The benefits of portfolio diversification.mp4

03:28

004 Calculating the covariance between securities.mp4

03:35

004 Python-for-Finance-Course-Notes-Part-II.pdf

005 Measuring the correlation between stocks.mp4

03:59

005 Python-for-Finance-Course-Notes-Part-II.pdf

006 Calculating Covariance and Correlation.mp4

05:00

007 Considering the risk of multiple securities in a portfolio.mp4

03:19

007 Python-for-Finance-Course-Notes-Part-II.pdf

008 Calculating Portfolio Risk.mp4

02:39

009 Python-for-Finance-Course-Notes-Part-II.pdf

009 Understanding Systematic vs. Idiosyncratic risk.mp4

02:58

010 Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio.mp4

04:28

external-links.txt

13 - PART II Finance - Using Regressions for Financial Analysis

001 Housing-Data.xlsx

001 Python-for-Finance-Course-Notes-Part-II.pdf

001 The fundamentals of simple regression analysis.mp4

03:55

002 Housing.xlsx

002 Running a Regression in Python.mp4

06:35

003 Are all regressions created equal Learning how to distinguish good regressions.mp4

04:58

003 Python-for-Finance-Course-Notes-Part-II.pdf

004 Computing Alpha, Beta, and R Squared in Python.mp4

06:14

004 Housing.xlsx

external-links.txt

14 - PART II Finance - Markowitz Portfolio Optimization

001 14.Markowitz-Efficient-frontier.xlsx

001 Markowitz Portfolio Theory - One of the main pillars of modern Finance.mp4

06:34

001 Python-for-Finance-Course-Notes-Part-II.pdf

002 Markowitz-Data.csv

002 Obtaining the Efficient Frontier in Python Part I.mp4

05:35

003 Markowitz-Data.csv

003 Obtaining the Efficient Frontier in Python Part II.mp4

05:18

004 Markowitz-Data.csv

004 Obtaining the Efficient Frontier in Python Part III.mp4

02:07

external-links.txt

15 - Part II Finance - The Capital Asset Pricing Model

001 Python-for-Finance-Course-Notes-Part-II.pdf

001 The intuition behind the Capital Asset Pricing Model (CAPM).mp4

04:44

002 Python-for-Finance-Course-Notes-Part-II.pdf

002 Understanding and calculating a securitys Beta.mp4

04:14

003 CAPM-Data.csv

003 Calculating the Beta of a Stock.mp4

03:38

004 Python-for-Finance-Course-Notes-Part-II.pdf

004 The CAPM formula.mp4

04:20

005 CAPM-Data.csv

005 Calculating the Expected Return of a Stock (CAPM).mp4

02:16

006 Introducing the Sharpe ratio and how to put it into practice.mp4

02:21

006 Python-for-Finance-Course-Notes-Part-II.pdf

007 CAPM-Data.csv

007 Obtaining the Sharpe ratio in Python.mp4

01:23

008 Measuring alpha and verifying how good (or bad) a portfolio manager is doing.mp4

04:14

008 Python-for-Finance-Course-Notes-Part-II.pdf

external-links.txt

16 - Part II Finance Multivariate regression analysis

001 Multivariate regression analysis - a valuable tool for finance practitioners.mp4

05:42

001 Python-for-Finance-Course-Notes-Part-II.pdf

002 Housing.xlsx

002 Running a multivariate regression in Python.mp4

06:20

external-links.txt

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

001 Python-for-Finance-Course-Notes-Part-II.pdf

001 The essence of Monte Carlo simulations.mp4

02:31

002 Monte Carlo applied in a Corporate Finance context.mp4

02:31

002 Python-for-Finance-Course-Notes-Part-II.pdf

003 Monte Carlo Predicting Gross Profit Part I.mp4

06:03

004 Monte Carlo Predicting Gross Profit Part II.mp4

02:56

005 Forecasting Stock Prices with a Monte Carlo Simulation.mp4

04:30

006 Monte Carlo Forecasting Stock Prices - Part I.mp4

03:39

007 Monte Carlo Forecasting Stock Prices - Part II.mp4

04:38

008 Monte Carlo Forecasting Stock Prices - Part III.mp4

04:17

009 An Introduction to Derivative Contracts.mp4

06:32

009 Python-for-Finance-Course-Notes-Part-II.pdf

010 Python-for-Finance-Course-Notes-Part-II.pdf

010 The Black Scholes Formula for Option Pricing.mp4

04:51

011 Monte Carlo Black-Scholes-Merton.mp4

06:00

012 Monte Carlo Euler Discretization - Part I.mp4

06:21

013 Monte Carlo Euler Discretization - Part II.mp4

02:09

external-links.txt

18 - APPENDIX - pandas Fundamentals

001 pandas Series - Introduction.mp4

08:33

001 pandas-dataframes-introduction-exercise.zip

001 pandas-dataframes-introduction-lecture.zip

001 pandas-dataframes-introduction-solution.zip

002 pandas - Working with Methods - Part I.mp4

04:49

002 pandas-working-with-methods-part-i-lecture.zip

003 pandas - Working with Methods - Part II.mp4

02:32

003 pandas-working-with-methods-exercise.zip

003 pandas-working-with-methods-part-ii-lecture.zip

003 pandas-working-with-methods-solution.zip

004 pandas - Using Parameters and Arguments.mp4

04:09

004 using-parameters-and-arguments-exercise.zip

004 using-parameters-and-arguments-lecture.zip

004 using-parameters-and-arguments-solution.zip

005 Location.csv

005 Region.csv

005 pandas Series - .unique() and .nunique().mp4

03:49

005 pandas-series.unique-and.nunique-exercise.zip

005 pandas-series.unique-and.nunique-lecture.zip

005 pandas-series.unique-and.nunique-solution.zip

006 Location.csv

006 Region.csv

006 pandas Series - .sort values().mp4

03:58

006 pandas-series.sort-values-exercise.zip

006 pandas-series.sort-values-lecture.zip

006 pandas-series.sort-values-solution.zip

007 pandas DataFrames - Introduction - Part I.mp4

04:41

008 Lending-company.csv

008 Sales-products.csv

008 pandas DataFrames - Introduction - Part II.mp4

05:05

008 pandas-series-introduction-exercise.zip

008 pandas-series-introduction-lecture.zip

008 pandas-series-introduction-solution.zip

009 Lending-company.csv

009 Sales-products.csv

009 pandas DataFrames - Common Attributes.mp4

04:15

009 pandas-dataframes-common-attributes-exercise.zip

009 pandas-dataframes-common-attributes-lecture.zip

009 pandas-dataframes-common-attributes-solution.zip

010 Lending-company.csv

010 Sales-products.csv

010 pandas DataFrames - Data Selection.mp4

06:55

010 pandas-dataframes-data-selection-exercise.zip

010 pandas-dataframes-data-selection-lecture.zip

010 pandas-dataframes-data-selection-solution.zip

011 Lending-company.csv

011 Sales-products.csv

011 pandas DataFrames - Data Selection with .iloc[].mp4

05:56

011 pandas-dataframes-data-selection.iloc-exercise.zip

011 pandas-dataframes-data-selection.iloc-lecture.zip

011 pandas-dataframes-data-selection.iloc-solution.zip

012 Lending-company.csv

012 Sales-products.csv

012 pandas DataFrames - Data Selection with .loc[].mp4

03:52

012 pandas-dataframes-data-selection.loc-exercise.zip

012 pandas-dataframes-data-selection.loc-lecture.zip

012 pandas-dataframes-data-selection.loc-solution.zip

19 - APPENDIX - Technical Analysis

001 Technical Analysis - Principles, Applications, Assumptions.mp4

02:56

002 Charts Used in Technical Analysis.mp4

05:32

003 Other Tools Used in Technical Analysis.mp4

01:52

004 Trend, Support and Resistance Lines.mp4

03:57

005 Common Chart Patterns.mp4

04:25

006 Price Indicators.mp4

03:46

007 Momentum Oscillators.mp4

03:42

008 Non-price Based Indicators.mp4

04:47

009 Technical Analysis - Cycles.mp4

04:07

010 Intermarket Analysis.mp4

01:09

20 - BONUS LECTURE

001 Bonus Lecture Next Steps.html

Description

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

Who is this 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

What You Need to Know?

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

More details

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? Â
Would you like to explore how Python can be applied in the world of Finance and solve portfolio optimization problems? Â
If so, then this is the right course for you! Â
We are proud to present Python for Finance: Investment Fundamentals and Data Analytics â€“ one of the most interesting and complete courses we have created so far. Â
An exciting journey from Beginner to Pro.Â Â
If you are a complete beginner and you know nothing about coding, donâ€™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.Â Â
Finance Fundamentals. Â
And it gets even better! The Finance part 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:Â Â
Rate of return of stocks Â
Risk of stocks Â
Rate of return of stock portfolios Â
Risk of stock portfolios Â
Correlation between stocks Â
Covariance Â
Diversifiable and non-diversifiable risk Â
Regression analysis Â
Alpha and Beta coefficients Â
Measuring a regressionâ€™s explanatory power with R^2 Â
Markowitz Efficient frontier calculation Â
Capital asset pricing model Â
Sharpe ratio Â
Multivariate regression analysis Â
Monte Carlo simulations Â
Using Monte Carlo in a Corporate Finance context Â
Derivatives and type of derivatives Â
Applying the Black Scholes formula Â
Using Monte Carlo for options pricing Â
Using Monte Carlo for stock pricing
Everything is included! All these topics are first explained in theory and then applied in practice using Python. This is the best way to reinforce what you have learned.Â Â
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.Â Â Â Â
Teaching is our passion. Â
Everything we teach is explained in the best way possible. Plain and clear English, relevant examples and time-efficient lessons. Donâ€™t forget to check some of our sample videos to see how easy they are to understand.Â Â
If you have questions, contact us! We enjoy communicating with our students and take pride in responding very soon. Our goal is to create high-end materials that are fun, exciting, career-enhancing, and rewarding.Â Â Â
What makes this training different from the rest of the Programming and Finance courses out there? Â
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 â€“ HD video and animations (this isnâ€™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 on Udemy.
Complete training â€“ 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 youâ€™ve learned.
Course Challenge: Solve our exercises and make this course an interactive experience.
Excellent support: If you donâ€™t understand a concept or you simply want to drop us a line, youâ€™ll receive an answer within 1 business day.
Dynamic: We donâ€™t want to waste your time! The instructors set a very good pace throughout the whole course.
Please donâ€™t forget that the course comes with Udemyâ€™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?
Click 'Buy now' to start your learning journey today. We will be happy to see you inside the course.
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

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365 Careers is the #1 best-selling provider of business, finance, and data science courses on Udemy. The company’s courses have been taken by more than 2,000,000 students in 210 countries. People working at world-class firms like Apple, PayPal, and Citibank have completed 365 Careers trainings. Currently, 365 focuses on the following topics on Udemy: 1) Finance – Finance fundamentals, Financial modeling in Excel, Valuation, Accounting, Capital budgeting, Financial statement analysis (FSA), Investment banking (IB), Leveraged buyout (LBO), Financial planning and analysis (FP&A), Corporate budgeting, applying Python for Finance, Tesla valuation case study, CFA, ACCA, and CPA2) Data science – Statistics, Mathematics, Probability, SQL, Python programming, Python for Finance, Business Intelligence, R, Machine Learning, TensorFlow, Tableau, the integration of SQL and Tableau, the integration of SQL, Python, Tableau, Power BI, Credit Risk Modeling, and Credit Analytics, Data literacy, Product Management, Pandas, Numpy, Python Programming, Data Strategy3) Entrepreneurship – Business Strategy, Management and HR Management, Marketing, Decision Making, Negotiation, and Persuasion, Tesla's Strategy and Marketing4) Office productivity – Microsoft Excel, PowerPoint, Microsoft Word, and Microsoft Outlook5) Blockchain for BusinessAll of our courses are: - Pre-scripted - Hands-on - Laser-focused - Engaging - Real-life tested By choosing 365 Careers, you make sure you will learn from proven experts, who have a passion for teaching, and can take you from beginner to pro in the shortest possible amount of time. If you want to become a financial analyst, a data scientist, a business analyst, a data analyst, a business intelligence analyst, a business executive, a finance manager, an FP&A analyst, an investment banker, or an entrepreneur365 Careers’ courses are the perfect place to start.

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