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The Inconvenient Truth About Data Science

I published this first on LinkedIn and received very positive feedback. If you like it too, go to the original post here and click on thumbs up or share on Twitter. Thanks!

  1. Data is never clean.
  2. You will spend most of your time cleaning and preparing data.
  3. 95% of tasks do not require deep learning.
  4. In 90% of cases generalized linear regression will do the trick.
  5. Big Data is just a tool.
  6. You should embrace the Bayesian approach.
  7. No one cares how you did it.
  8. Academia and business are two different worlds.
  9. Presentation is key – be a master of Power Point.
  10. All models are false, but some are useful.
  11. There is no fully automated Data Science. You need to get your hands dirty.

One response to “The Inconvenient Truth About Data Science”

  1. The Inconvenient Truth About Data Science | suboptimum

    […] by Kamil Bartocha, a few excellent points. I agree with every single one of […]

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