Explaining data visualization is not about syntax. It’s about the purpose of each diagram and what it brings in terms of data insights. It’s about the disadvantages of each diagram, i.e. when not to use them. These slides give an introduction to data visualization explaining even how to read and debug python code.
These slides can be used to create a narrative for:
- 📐 architects & engineers
- 👗🎨 designers
- 🌃 urban planners
- …people from humanitarian backgrounds even though examples are not tailored for them
What to expect:
- 🎨 visual explanation
- 🌃 AEC examples
Wrong place to look for:
- 🧾lots of text
- ➕math formulas
- ⭐ fancy buzzwords
- 🏃🏼♀️ learning shortcuts - quality is a thing here:)
This material can be presented with exercises on Airbnb Open Data dataset. How to choose an appropriate diagram.
Most common packages:
matplotlib
seaborn
plotly
folium
d3js
leaflet
Suggested steps:
- presentation ~20min
- explaining colab - optional ~5min
- explaining git - semioptional ~10min
- exercise: data visualization on Airbnb Open Data dataset + explanation of why these insights are interesting
- work with the dataset in a notebook 👇🏼
Note: this tutorial has been used as a learning material at IAAC, in MACAD bootcamp.