>but most data data scientists' day-to-day consists of chaining together libraries and building nice dashboards. This - though I'd add that data science is als

>but most data data scientists' day-to-day consists of chaining together libraries and building nice dashboards. This - though I'd add that data science is also about statistical intuition, knowing what questions to ask of the data and how to get sensible results. It's not essential to understand that PCA involves finding the eigenvectors of the covariance matrix, but it is essential to know when PCA would be useful, and common gotchas that might make it irrelevant.