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 ( |
| 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 455 ( | INR 455 ( |
| 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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