[Turing Data Story] Greedy optimisation / generating colours

[jack89roberts]

Story description

Please provide a high level description of the Turing Data Story
A clear and concise description of what the data story is going to be about.

This could be part 1 of a series of optimisation-related stories, where parts 2/3 would be #177. The plan would be to discuss greedy optimisation, using distinctipy (https://github.com/alan-turing-institute/distinctipy), a Python package for generating visually distinct colours, as an example. Might use this notebook from a tech talk a lifetime ago as a starting point: https://github.com/alan-turing-institute/DataScienceSkills/blob/master/distinctipy/generating_colours.ipynb

Which datasets will you be using in this Turing Data Story?
Cite the dataset that are going to be used in this story (these of course can change whilst developing the story).

Could be no datasets (is that a problem?)

Additional context
Add any other context or screenshots about the story here.

Ethical guideline

Ideally a Turing Data Story has these properties and follows the 5 safes framework.

• The analysis you produce is openly available and reproducible.
• Any data used are open and have an explicit licence, provenance and attribution.
• Any data used are not personal data (i.e. the data is anonymous or anonymised).
• Any linkage of datasets in your data story does not lead to an increased risk of the personal identification of individuals.
• The Story must be truthful and clear about any limitations of analysis (and potential biases in data).
• The Story will not lead to negative social outcomes, such as (but not limited to) increasing discrimination or injustice.

Current status

• Scoping of the story
• Write story outline
• Add material to the story notebook
• Combine materials into a readable story
• Proofread
• Request reviews
• Address reviews
• Merge to master branch.

Updates