• What is a normal day in the life Talking to customers/stakeholders/consumers. Understanding the problem they're trying to solve. A lot of times, they think you can do magical things and you have to explain to them why their proposition is not possible. On the other hand, you get to show people that a lot of gain can be had by doing pretty basic analysis or through simple models.

From a technical perspective, a very large portion of your time is spent trying to gather clean data. It's not a pretty world out there, and that's one of main things data science courses gloss over. Data bases are often poorly implemented, and may have a lot of errors. Your models are only as good as the data you're feeding into them, so this becomes a critical chokepoint that can make or break your project.

• How data scientists differentiate after a few years of experience You can deploy to production and scale. Building a prototype that gets high accuracy is a wholly different skill set from being able to productionize something and make it resilient enough for users to depend on as part of their workflow day after day.

• Most essential skills

  • Empathy. This shit is weird and people don't get it. You have to be patient enough to be able to explain what's possible and what you need from them. On the same token, data science projects relies heavily upon subject matter expertise: no matter how hot I may be deploying deep learning models on autoscaling Awesome Cloud Architecture, it means jack shit if I don't understand the underlying problem at hand.

  Top DS myths

  What we are actually looking for in candidates

  Top things we don't care about, but others think we do in recruiting

  Or anything in between!