What are the general "checklist" of data cleaning and pre-processing before doing any analysis or modeling?

I'm trying to be more systematic with how I work on my projects and have a structured workflow i follow rather than just taking a fuck-it-we-ball attitude and see what issues crop up later.

I am aware I should probably...:
- Check for missing values, are values missing not at random or completely at random? Decide on appropriate measures from there whether to impute, how if so, or simply remove.
- Look at the distribution of my features. Are there …

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