Data Science 4 Buffett Value Investing

Learn how to invest like Buffett using data science.

Market volatility cannot be predicted, but it can be used to your advantage — as Warren Buffett put it: "Be fearful when others are greedy, and greedy when others are fearful." Leveraging data science, you'll learn practical, data-driven techniques for value investing inspired by Buffett's principles, including:

• The three great opportunities — outstanding businesses, rebounds, and high growth stocks — and how to screen for each.

• How to separate market noise from a stock's true value, like peeling an onion — and how to use the price regression model to classify stocks by their noise level.

• How to estimate a stock's intrinsic value by building data models and recognizing patterns through fundamental and benchmark analysis.

• How to decide when to buy or sell using margin of safety and statistical analysis.

• How to avoid common mistakes in dividend stock selection by choosing stocks with both high yield and price growth.

• How the stock market behaves, from a data science perspective.

• How to select markets and industries to invest in.

Teaching methods

You'll learn these techniques through case studies across diverse industries and companies, so you can confidently handle different situations. Source data and templates are provided, so you can easily apply the same analysis to industries and companies of your own interest.

About the instructor

The course is developed and taught by an award-winning instructor, Dr. Yao Zhao, the Rutgers Business School Dean's Research Professor. He has won numerous awards, including the National Science Foundation CAREER Award, the Dean's Meritorious Teaching Award, and 1st Prize in the INFORMS Case and Teaching Materials Competition. He has taught hundreds of thousands of students worldwide — online and offline, at the undergraduate, graduate, and executive levels.

Who is this course for?

The course is designed for the following audiences:

• Personal investors, including those pursuing long-term value investing as well as more active strategies like rebounds and high growth stocks.

• Value investors who aim to profit from long-term financial performance rather than short-term price fluctuations.

• Students and professionals who want to build practical data science skills applied to finance and investing.

• Anyone looking to apply data science and quantitative methods to real-world investment decisions, regardless of prior finance background.

Course requirements

No prior experience, coding, or technical skills are required.

• If you're a beginner, you can build up your knowledge step by step, starting from the basics.

• If you're already experienced, or interested in specific industries or stocks, take a look at the course outline and jump into the modules that suit your needs.