Instructor Notes

Overall approach

  • This module is for complete beginners. Each Python notebook takes 15 to 25 minutes and covers one idea, with an exercise and its solution at the end. In a live session, teach the episode page briefly, then let learners work through the notebooks while helpers circulate.
  • Suggested timing for a full day: Introduction 30 min; Python Basics (notebooks 2a to 2f) 2.5 hours; Working with Data (3a, 3b) 1 hour; Geospatial Python Basics (4a to 4c) 1.5 hours. Getting Started with QGIS (about 1.75 hours) works well as a separate session.
  • Ask everyone to Save a copy in Drive before running anything.
  • The examples use Indiana counties and Lafayette food stores throughout, so the same data reappears in later modules (Data Collection, Case Studies).
  • The Python notebooks were rewritten for beginners from the Workshop 2026 Session B notebook (intro_python.ipynb), which stays in its own repository unchanged.

Episode by episode

Introduction

  • The “Which tool?” challenge is a good opener: there is no single right answer for the visual check.

Python Basics

  • Normalize errors early: run the deliberate SyntaxError and NameError cells in notebook 2a together and read the last line aloud.
  • The “numbers stored as text” idea (2b) comes back in every Census notebook and in the join notebook (4c).

Working with Data

  • Point out that describe() averages the FIPS codes (3a). It is a memorable way to show why identifiers should be text.
  • The log scale in the scatter plot (3b) is often new; show the same chart without it first.

Geospatial Python Basics

  • The wrong-CRS demonstration in 4b (Indiana ends up near the equator) makes the difference between set_crs() and to_crs() stick.
  • In 4c, the failing join is deliberate. Let learners read the error before showing the fix.
  • 58 of the 64 food stores lie inside the city limits; all 17 supermarkets do.

Getting Started with QGIS

  • Moved here from the Data Visualization module in October 2026. Data is loaded from this repository’s data/ folder by URL.
  • The OpenStreetMap basemap needs an internet connection; on a slow network, skip it.
  • Loading from a web address (Layer > Add Layer > Add Vector Layer > Protocol: HTTP(S)) avoids file downloads and path problems. If a network blocks it, the same GeoJSON files can be downloaded from the repository’s data/ folder and loaded as files.
  • In QGIS, the OpenStreetMap basemap works normally. (In Colab it does not, which is why the notebooks use an Esri basemap.)
  • Learners often press OK instead of Apply while comparing classification modes; encourage Apply so the dialog stays open.
  • Remind learners to rename layers before making a legend.

Common problems

Symptom Likely cause Fix
NameError A cell was skipped Runtime > Run before
Colab forgot variables The session disconnected Reconnect and run all cells from the top
HTTPError or timeout when reading data Network hiccup Rerun the cell
A merge fails with “object and int64 columns” ID types differ Convert both with .astype(str).str.zfill(5)
QGIS layer shows nothing Off screen, or behind the basemap Right-click > Zoom to Layer; drag the layer above the basemap