How you structure code determines whether it runs at all, and whether you can maintain it later. In this module, you'll restructure common trouble spots, from looping over lists to ordering functions and untangling dependencies, so your programs run cleanly and stay easy to work with.

Your model is only as good as the data you feed it, and rushing past your data is where many projects quietly go wrong. In this module, you'll inspect, visualize, clean, and correctly update your data so the decisions and models that follow rest on an accurate foundation.

Your model's ability to generalize depends on the feature choices you make well before you tune any algorithm. In this module, you'll select features that will still be available on unseen data and prune redundant ones so your models generalize well and stay efficient.

Your data science practice grows stronger every time you spot and fix a mistake before it reaches your output. In this module, you'll identify trusted external resources, from official Python documentation to professional data science platforms, to sustain the habits that keep your Python data science work clean, reliable, and efficient.