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  • Writer's pictureChelsea Wilkinson

Think like a data scientist

I don’t pretend to be a data scientist, which makes being the co-founder of a data science consultancy interesting - but that’s a story for another day!

For the past 9 mths, I’ve been avidly reading all things data. Following a friend’s recommendation (thanks Kerry Millar), I recently began #TheAlignmentProblem by Brian Christian.

What can I say?

I’m fascinated.

Frightened. Appalled. Inspired. And I’m only ~80 pages in!

The opening section covers representation, focussing on the bias in ML systems - making the point that algorithms are only as good as their data.

Citing the Google Photos classification example, amongst others, the author drums home ‘if a certain type of data is underrepresented or absent from the training data but present in the real world, then all bets are off’.

But this isn’t intended to be a book review.

Rather, a call to action.

Everyone should be reading ‘all things data’, not just data scientists. Algorithms are already woven into our worlds. We all need to #ThinkLikeADataScientist. Business (especially) needs to better understand AI’s inherent limitations to benefit from its expansive solutions.


Originally posted on LinkedIn:


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