The BEST way to keep your code simple! (Abstract, but not too much)

Learn the fundamentals of abstraction in programming and how to write cleaner, more maintainable code! In this video, we explore how abstraction simplifies user experience by hiding unnecessary details, using real-world examples like dishwashers before diving into coding applications.

What You'll Learn:
The three rules of abstraction from Gentzkow and Shapiro's coding guide
How functions serve as the primary means of abstraction in programming
When to abstract (eliminate redundancy, improve clarity) and when NOT to abstract
Function scope and variable management
Practical examples using OLS estimation and experimental data analysis

Key Topics Covered:
Real-world abstraction examples (dishwashers as perfect abstractions)
Eliminating redundancy in treatment effect calculations
Improving code clarity through meaningful function names
Avoiding over-abstraction pitfalls
Function scope and variable naming conventions

Programming Languages Featured:
Julia code examples
Matrix algebra applications in econometrics

Three Rules of Abstraction:
Abstract to eliminate redundancy
Abstract to improve clarity
Otherwise don't abstract

Perfect for data scientists, econometricians, and anyone looking to improve their coding practices! Whether you're working with experimental data, running regressions, or just want cleaner code, this video provides practical guidance on when and how to use abstraction effectively.

Tyler Ransom is an Associate Professor of Economics at the University of Oklahoma. Subscribe for more videos on data science, econometrics, and research methods!
Editing credit: @neiljohnmanllios3064 ​