MIT calls this “the missing semester of your CS education”
What data science and software engineering have in common is writing code. But while code is the main outcome of software engineering, data science projects typically end with models, results, and reports. Consequently, in data science the quality, structure, and delivery of code is often an afterthought at best.
The implicit expectation with data science projects is that the results reported at the end can be trusted.
This means that if someone asked you to re-run your or somebody else’s analysis, you would be able to obtain the same results, regardless of how much time has passed since you first performed the analysis.
Similarly, if you are developing a component for a product, the implicit expectation is that component you developed represents the best possible performance given what is reasonably possible within the requirements of the product.
These statements may seem obvious, but satisfying both expectations can be quite difficult.
If you don’t believe me, think about your past projects.
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