Transcript#
This transcript was generated automatically and may contain errors.
Is your data science project structure just a bunch of files in a folder? Let's fix that in 30 seconds.
First, separate your data from your code. Use a raw folder for untouched datasets and a process folder for clean datasets. Here we have our Quarto documents for analysis and creating a report. Here we also have a shell script, which processes individual reports. And we wrap it all up in a solid readme that explains what the project actually does.
Additional structure tips
You can do things like add a models folder, a source folder for reusable functions, requirements.txt file. Cookie Cutter Data Science offers a lot of great guidance on structuring your data science projects.
How do you improve your data science project structure? Let us know in the comments below.
