Programming Best Practices For Data Science

Often, the entire data science life cycle ends up as an arbitrary mess of notebook cells in either a Jupyter Notebook or a single messy script. In addition, most data science problems require us to switch between data retrieval, data cleaning, data exploration, data visualization, and statistical / predictive modeling.
But there's a better way! In this post, I'll go over the two mindsets most people switch between when doing programming work specifically for data science: the prototype mindset and the production mindset.

If you find yourself writing cell after cell of Jupyter code, this is for you.


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