Python for Finance and Data Science

Learn Python for Finance and Data Science – Analyze Real Data, Build Portfolios, and Backtest Trading Strategies

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    1. What does this course cover?

    2. Disclaimer

    3. How to get the most of this course?

    4. Any questions or problems? Reach out!

    1. Download Anaconda & Set Up Jupyter Notebook

    2. Jupyter Notebook Basics

    1. Variables & Single Datatypes

    2. What you should NEVER do

    3. Typecasting & User Input

    4. Practice Time :-)

    5. Arithmetic Operators

    6. Comparison Operators / Logical Operators

    7. Indentations & If-Statements

    8. Practice Time II :-)

    9. Lists as objects with methods in Python

    10. List Slicing & Indexing

    11. Difference between lists & tuples

    12. Dictionaries

    13. For loops

    14. Combining lists & loops: List comprehension

    15. While loop

    16. Practice Time III :-)

    17. Practice your knowledge with a common Interview question!

    18. Functions

    1. Setting up a DataFrame and DataFrame properties

    2. Adding columns and using dictionaries for DataFrame initialization

    3. New columns based on calculations

    4. Data Selection with iloc

    5. Data Selection with loc

    6. Data Filtering with Boolean Masks and Boolean Indexing

    1. Pulling stock prices and OHLC data

    2. yfinance update 2025!

    3. Quick Recap on what we did in the last chapter

    4. Return calculation with shift and pct_change

    5. Important functions: diff, dropna, rolling

    6. Very important argument: axis=0 or axis=1

    7. nlargest and nsmallest

    8. Bringing together Dataframes: Concat

    9. Combining Time Series and OHLC in general

    10. Resampling Data

    11. Resampling OHLC Data

    12. Plotting in Pandas

    13. Iterating over a dataframe: Iterrows

    14. Performance Comparison: Iterrows vs. Vectorization

    15. Return calculation deep dive

    16. Practice Task: Plot the yearly returns of the S&P500

    17. Solution to the Practice Task: Plot yearly returns of the S&P500

    1. Portfolio Analysis Introduction

    2. Variance, Standarddeviation, Covariance and Correlation

    3. Portfolio Return and Risk

    4. Portfolio Expected Return and Portfolio Risk using Python

    5. Use the Dot Product to calculate Portfolio Return and Portfolio Risk

    6. Application to real data: Portfolio of Microsoft, Coca Cola and Tesla

    7. Efficient Frontier, Minimum Variance Portfolio and dominant Portfolios
  • 77 lessons
  • 9 hours of video content
  • Real-world projects
  • Hands-on practice
  • Taught by an Industry Expert