Cultures of a data science team

Here are a few links that are worth your time:

  • If you’re build­ing a data sci­ence team, read The Two Cultures of ML Systems to learn about the pit­falls in pro­duc­tion­ising data sci­ence. Every para­graph makes a very per­tin­ent point.
  • You can im­prove the wis­dom of crowds. Ask people to vote. Also ask what oth­ers will vote for. Pick the an­swer that is more pop­ular than people pre­dict. (Nature: A Solution to the Single Question Crowd Wisdom Problem. The full text is not open.)

For our tech­nic­ally minded friends, here are a few more:

  • The Data Stack is a col­lec­tion of tools used in the data sci­ence eco­sys­tem, ran­ging from data sourcing to pro­cessing to ana­lys­is to visu­al­isa­tion
  • This is a short in­tro­duc­tion to an­om­aly de­tec­tion. When ex­plor­ing data, an­om­alies and out­liers in­vari­ably pro­duce in­ter­est­ing stor­ies
  • TensorFlow 1.0 is out. It’s faster, and fea­tures an easy to use in­ter­face. The Python API looks more like NumPy

Most of the above work on Python.

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