Learning

This page stores learning materials we've produced, alongside links to other sources of useful information as we come across them. We make no endorsement or guarantee of quality - you should use your judgement and your employer's policies to guide you in selecting learning materials - but if people within our community have found something useful, we want to share it.

The first key resource here is the video material we produced for the Quality LAC Data project, If you'd like to suggest or offer up other training materials for this page, just let us know!

Video course: LAC data validation in Python / Github 

Produced in 2021 for our Quality LAC Data project, this material breaks down into four modules of short videos (and accompanying documentation) introducing LA analysts to Python, Github, and the process of writing and testing data validation rules for our SSDA903 Validator tool.

D2I Learning / Quality LAC Data Module 1 - Introductions (youtube playlist)

D2I Learning / Quality LAC Data Module 1 - Introductions (download papers)

  • Module introduction

  • Frontend overview

  • Introduction to GitHub

  • Introduction to Replit

D2I Learning / Quality LAC Data Module 2 - Python (youtube playlist)

D2I Learning / Quality LAC Data Module 2 - Python (download papers)

  • Basic Python concepts

  • Python data structures

  • Python control flow

D2I Learning / Quality LAC Data Module 3 - Python for data (youtube playlist)

D2I Learning / Quality LAC Data Module 3 - Python for data (download papers)

  • Python libraries

  • Introduction to Pandas

  • Pandas data analysis

D2I Learning / Quality LAC Data Module 4 - Writing validation rules (youtube playlist)

D2I Learning / Quality LAC Data Module 4 - Writing validation rules (download papers)

  • The 903 Validator repository

  • Collaborating on GitHub

  • Writing a validation rule

D2I Learning / Quality LAC Data Module 5 - Writing advanced rules (youtube playlist)

D2I Learning / Quality LAC Data Module 5 - Writing advanced rules (download papers)

  • Grouping

  • Merging