You, my fellow data nerd, have learnt quite a bit about Data Science already.

While others may get a confused look on their face when someone mentions a "Random Forest" you think back fondly on the last time you imported it from sklearn. If someone asked you to join some tables together in SQL you may think about whether you'll need a "LEFT or INNER" join to tackle this in the best way.

Now you're thinking: 🤔 he's right, I am awesome.

And while I agree with you, in your next interview for a Full Stack Data Science position you get asked: "How are you going to leverage data science to make a measurable impact on my company?"

You know you shouldn't respond with "I'll use ML, or I'll join tables together". But how should you respond, more importantly how would you actually pull it off?

Let me tell you.

Before trying to answer that question you would need to get more business context about their company. You'd need to understand where they currently sit on the data hierachy of needs (don't know what this is, no worries, I'll cover it in detail). And you'd need to have a good understanding of each technical area of Full Stack Data Science.

You'd need to know the impact potential, effort requirements, and dependencies for each area, not to mention if you want to pull it off you'll need to know how to implement solutions in each area. Finally you'd need to capable of effectively communicating any solutions back to that stakeholder.

Now you might be thinking: no one can do all of those things, 🦄 do not exist.

But that's exactly why I'm writing this, to tell you that if you want to strap a horn to your forehead: I can help.