The ‘Python for Finance: Investment Fundamentals and Data Analytics Course’ is a beginner-friendly program that covers the basics of Python, financial calculations & investment portfolios. The course covers the practical skills that are required in the field of finance, including the stock market, calculations of risk and returns, the Black-Scholes formula, and more.

The course follows a practical approach, the students will learn the theoretical concepts before putting them into practice. The course is usually available for **INR 5,200** on Udemy but students can click on the link and get the ‘**Python for Finance: Investment Fundamentals and Data Analytics Course**’ for **INR 649**.

## Who all can opt for this course?

- Aspiring Data Scientists
- Aspiring Programmers
- Those with an interest in investments and finance
- Programmers who want to focus on the financial industry
- Anyone who wishes to learn how to code and put their knowledge to use
- Graduates and professionals in finance who need to use their Python skills more effectively

## Course Highlights

Key Highlights | Details |
---|---|

Registration Link | Apply Now! |

Price | INR 649 (INR 5,200) 88% off |

Duration | 09 hours |

Rating | 4.5/5 |

Student Enrollment | 1,23,968 students |

Instructor | 365 Careers https://www.linkedin.com/in/365careers |

Topics Covered | Python programming, Python variables, data types, basic Python syntax, conditional statements, functions, etc. |

Course Level | Beginner |

Total Student Reviews | 25,811 |

## Learning Outcomes

- Learn Python coding
- Learn how to the loops, functions, sequences, and conditional statements available in Python
- Learn how to use NumPy
- Know how to use the Pandas data analysis toolbox
- Learn how to use Matplotlib to plot graphs
- Use Python to do practical tasks
- Get a job as a data scientist who can work with Python
- Do a thorough investment analysis
- Build investment portfolios
- Determine the return and risk of investment portfolios
- Know the best practices to follow while working with financial data
- Employ regression analysis with one and more variables
- Using the Black Scholes method to price options will teach you how to do it

## Course Content

S.No. | Module (Duration) | Topics |
---|---|---|

1. | Welcome! Course Introduction (08 minutes) | What Does the Course Cover? |

Download Useful Resources – Exercises and Solutions | ||

2. | Introduction to programming with Python (33 minutes) | Programming Explained in 5 Minutes |

Programming Explained in 5 Minutes | ||

Why Python? | ||

Why Python? | ||

Why Jupyter? | ||

Why Jupyter? | ||

Installing Python and Jupyter | ||

Jupyter’s Interface – the Dashboard | ||

Jupyter’s Interface – Prerequisites for Coding | ||

Jupyter’s Interface | ||

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

3. | Python Variables and Data Types (14 minutes) | Variables |

Variables | ||

Numbers and Boolean Values | ||

Numbers and Boolean Values | ||

Strings | ||

Strings | ||

4. | Basic Python Syntax (11 minutes) | Arithmetic Operators |

Arithmetic Operators | ||

The Double Equality Sign | ||

The Double Equality Sign | ||

Reassign Values | ||

Reassign values | ||

Add Comments | ||

Add Comments | ||

Line Continuation | ||

Indexing Elements | ||

Indexing Elements | ||

Structure Your Code with Indentation | ||

Structure Your Code with Indentation | ||

5. | Python Operators Continued (07 minutes) | Comparison Operators |

Comparison Operators | ||

Logical and Identity Operators | ||

Logical and Identity Operators | ||

6. | Conditional Statements (13 minutes) | Introduction to the IF statement |

Introduction to the IF statement | ||

Add an ELSE statement | ||

Else if, for Brief – ELIF | ||

A Note on Boolean Values | ||

A Note on Boolean Values | ||

7. | Python Functions (18 minutes) | Defining a Function in Python |

Creating a Function with a Parameter | ||

Another Way to Define a Function | ||

Another Way to Define a Function | ||

Using a Function in another Function | ||

Combining Conditional Statements and Functions | ||

Creating Functions Containing a Few Arguments | ||

Notable Built-in Functions in Python | ||

Functions | ||

8. | Python Sequences (19 minutes) | Lists |

Lists | ||

Using Methods | ||

Using Methods | ||

List Slicing | ||

Tuples | ||

Dictionaries | ||

Dictionaries | ||

9. | Using Iterations in Python (17 minutes) | For Loops |

For Loops | ||

While Loops and Incrementing | ||

Create Lists with the range() Function | ||

Create Lists with the range() Function | ||

Use Conditional Statements and Loops Together | ||

All In – Conditional Statements, Functions, and Loops | ||

Iterating over Dictionaries | ||

10. | Advanced Python tools (01 hour 04 minutes) | Object Oriented Programming |

Object Oriented Programming – Quiz | ||

Modules and Packages | ||

Modules – Quiz | ||

The Standard Library | ||

The Standard Library – Quiz | ||

Importing Modules | ||

Importing Modules – Quiz | ||

Must-have packages for Finance and Data Science | ||

Must-have packages – Quiz | ||

Working with arrays | ||

Generating Random Numbers | ||

A Note on Using Financial Data in Python | ||

Sources of Financial Data | ||

Accessing the Notebook Files | ||

Importing and Organizing Data in Python – part I | ||

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

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

Importing and Organizing Data in Python – part III | ||

Changing the Index of Your Time-Series Data | ||

Restarting the Jupyter Kernel | ||

11. | PART II FINANCE: Calculating and Comparing Rates of Return in Python (42 minutes) | Considering both risk and return |

Risk and return – Quiz | ||

What are we going to see next? | ||

Calculating a security’s rate of return | ||

Calculating a security’s rate of return | ||

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

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

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

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

What is a portfolio of securities and how to calculate its rate of return – Quiz | ||

Calculating a Portfolio of Securities’ Rate of Return | ||

Popular stock indices that can help us understand financial markets | ||

Which of the following is not an index? – Quiz | ||

Calculating the Indices’ Rate of Return | ||

12. | PART II Finance: Measuring Investment Risk (41 minutes) | How do we measure a security’s risk? |

Which of the following sentences is true? – Quiz | ||

Calculating a Security’s Risk in Python | ||

The benefits of portfolio diversification | ||

Investing in stocks – Quiz | ||

Calculating the covariance between securities | ||

Covariance – Quiz | ||

Measuring the correlation between stocks | ||

Correlation – Quiz | ||

Calculating Covariance and Correlation | ||

Considering the risk of multiple securities in a portfolio | ||

Calculating Portfolio Risk | ||

Understanding Systematic vs. Idiosyncratic risk | ||

Diversifiable Risk – Quiz | ||

Calculating Diversifiable and Non-Diversifiable Risk of a Portfolio | ||

13. | PART II Finance – Using Regressions for Financial Analysis (21 minutes) | The fundamentals of simple regression analysis |

Regressions – Quiz | ||

Running a Regression in Python | ||

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

Regressions – Quiz | ||

Computing Alpha, Beta, and R Squared in Python | ||

14. | PART II Finance – Markowitz Portfolio Optimization (19 minutes) | Markowitz Portfolio Theory – One of the main pillars of modern Finance |

Markowitz – Quiz | ||

Obtaining the Efficient Frontier in Python – Part I | ||

Obtaining the Efficient Frontier in Python – Part II | ||

Obtaining the Efficient Frontier in Python – Part III | ||

15. | Part II Finance – The Capital Asset Pricing Model (27 minutes) | The intuition behind the Capital Asset Pricing Model (CAPM) |

CAPM – Quiz | ||

Understanding and calculating a security’s Beta | ||

Beta – Quiz | ||

Calculating the Beta of a Stock | ||

The CAPM formula | ||

CAPM – Quiz | ||

Calculating the Expected Return of a Stock (CAPM) | ||

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

Sharpe ratios – Quiz | ||

Obtaining the Sharpe ratio in Python | ||

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

Alpha – Quiz | ||

16. | Part II Finance: Multivariate regression analysis (12 minutes) | Multivariate regression analysis – a valuable tool for finance practitioners |

Multivariate Regressions – Quiz | ||

Running a multivariate regression in Python | ||

17. | PART II Finance – Monte Carlo simulations as a decision-making tool (56 minutes) | The essence of Monte Carlo simulations |

Monte Carlo – Quiz | ||

Monte Carlo applied in a Corporate Finance context | ||

Monte Carlo in Corporate Finance – Quiz | ||

Monte Carlo: Predicting Gross Profit – Part I | ||

Monte Carlo: Predicting Gross Profit – Part II | ||

Forecasting Stock Prices with a Monte Carlo Simulation | ||

Monte Carlo Simulations – Quiz | ||

Monte Carlo: Forecasting Stock Prices – Part I | ||

Monte Carlo: Forecasting Stock Prices – Part II | ||

Monte Carlo: Forecasting Stock Prices – Part III | ||

An Introduction to Derivative Contracts | ||

Derivatives – Quiz | ||

The Black Scholes Formula for Option Pricing | ||

Monte Carlo: Black-Scholes-Merton | ||

Using Monte Carlo with Black-Scholes-Merton – Quiz | ||

Monte Carlo: Euler Discretization – Part I | ||

Monte Carlo: Euler Discretization – Part II | ||

18. | APPENDIX – pandas Fundamentals (58 minutes) | pandas Series – Introduction |

pandas – Working with Methods – Part I | ||

pandas – Working with Methods – Part II | ||

pandas – Using Parameters and Arguments | ||

pandas Series – .unique() and .nunique() | ||

pandas Series – .sort_values() | ||

pandas DataFrames – Introduction – Part I | ||

pandas DataFrames – Introduction – Part II | ||

pandas DataFrames – Common Attributes | ||

pandas DataFrames – Data Selection | ||

pandas DataFrames – Data Selection with .iloc[] | ||

pandas DataFrames – Data Selection with .loc[] | ||

19. | APPENDIX – Technical Analysis (36 minutes) | Technical Analysis – Principles, Applications, Assumptions |

Charts Used in Technical Analysis | ||

Other Tools Used in Technical Analysis | ||

Trend, Support and Resistance Lines | ||

Common Chart Patterns | ||

Price Indicators | ||

Momentum Oscillators | ||

Non-price Based Indicators | ||

Technical Analysis – Cycles | ||

Intermarket Analysis | ||

20. | BONUS LECTURE (36 seconds) | Bonus Lecture: Next Steps |

## Resources Required

The program requires Anaconda software. The instructor will teach how to install it.

## Featured Review

**Pedro Carlos Rosa Bom (5/5)**: It is without a doubt an excellent course, but some aspects have changed over the years. I believe the platform should notify users of these changes. For example, some codes no longer work.

## Pros

**Ritwik Dhande (5/5)**: This is one of the best course for computer science students who want to switch their career in a finance field**David N Ngana (5/5)**: The perfect course to become a Data Analyst in Finance Field.**Keng-Wei Hsu (5/5)**: This course is excellent!! It is recommended to those who don’t have any Python experience.**Soh Say Kiong (4/5)**: Best take away for me in this section is applying Monte Carlo Simulation to for stock call options pricing.

## Cons

**Nilaj Chakrabarty (1/5)**: The material is widely variable in terms of difficulty, the quizzes are basically worthless and the exercise notebooks are too simplistic.**April G (1/5)**: There were changes made to the API’s used in this course that not makes this course outdated.**Paul Mrockowski (2/5)**: Quizzes are worthless: one question, usually so basic that doesn’t prove knowledge or lack of knowledge of anything.**Jitendra Tolani (2/5)**: Highly disappointed, if something so crucial to the course in not being addressed, why did I pay the money for it?

## About the Author

The course is created by 365 careers. 365 careers are top-rated instructors on Udemy with an average 4.6 instructor rating and 6,60,994 reviews on Udemy. They have taught 22,95,331 students so far.

- On Udemy, 365 Careers is the top-selling provider of courses in business, finance, and data science.
- In 210 different countries, more than 2,000,000 students have taken the company’s courses.
- Individuals who have finished 365 Careers training now work at renowned companies like Apple, PayPal, and Citibank.
- In Udemy right now, 365 focuses on the following subjects: Finance, Data Science, Business Strategy, Office Productivity, and Business Blockchain.
- Every one of our courses is pre-scripted, practical, laser-focused, interesting, and tested in real-world situations.
- By selecting 365 Careers, you can be certain that you will learn from seasoned professionals who are passionate about sharing their knowledge and will help you go from a beginner to a pro in the shortest amount of time.
- The courses offered by 365 Careers are the ideal place to start whether you want to work as a financial analyst, data scientist, business analyst, data analyst, business intelligence analyst, business executive, finance manager, FP&A analyst, investment banker, or entrepreneur.

## Comparison Table

Parameters | Python for Finance: Investment Fundamentals & Data Analytics | Introduction to Finance, Accounting, Modeling and Valuation | The Complete Financial Analyst Training & Investing Course |
---|---|---|---|

Offers | INR 649 (INR 5,200) 88% off | INR 455 (87% off | INR 455 (87% off |

Duration | 9 hours | 4.5 hours | 23.5 hours |

Rating | 4.5/5 | 4.5 /5 | 4.6 /5 |

Student Enrollments | 1,23,961 | 189,894 | 247,283 |

Instructors | 365 Careers | Chris Haroun | Chris Haroun |

Register Here | Apply Now! | Apply Now! | Apply Now! |

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