Advice for a Bad Career in Data Science

You nailed the interview and landed the hottest job in America; go you, now you’re a data scientist! As you come into work every day to incorporate data science into product development, or apply machine learning to solve business problems, it’s blue skies everywhere. There are so many possibilities it’s hard to know where to begin and how to spend your time.

Well, here are a few suggestions, broken down into the stages of a typical data science project, from my experience practically guaranteed to work every time.

Disclaimer: No data scientists were intentionally harmed. Any resemblance to your coworkers, past or present, or the project you’re working on is purely coincidental, although expected.

Picking your problem
Start working on whatever problem you want.
— Try to pick the least defined problem, hopefully one you vaguely understand. That way you’ll have plenty of room to explore, and you don’t have to worry about running out of things to try.
—If someone comes to you with a problem, use it as an opportunity to reframe it into something you want to work on.
— Don’t bother wasting your time asking anyone what would be useful or valuable for them, you know better anyway! They’re probably not going to have the answers. And really, the fewer people that know about your work the better, you don’t want them interrupting you.
— You shouldn’t have to explain yourself either, the value of data science is self-evident; everybody wants it! But if someone does ask you what you’re working on, a good rule of thumb is that the more you have to convince them that it’s useful, the more you’re on the right track and can rub it in their face later.
— The business will eventually find value in whatever you do, don’t worry about when or how yet.

Start working on any and every problem someone comes to you with.
— Best not to ask too many questions, they’re the expert after all. They should know exactly what they need, and they’ve already identified the right question to ask and the true causes of the problem. Anytime someone comes to you it means there’s a data science solution that’s worth building. You don’t want them thinking you’re not up to it. They may start doubting your commitment.
— You have to show them that with data science you can do anything. They’ve likely read the news recently, so reassure them AI can do whatever they want, probably more and better and faster and stronger and 24/7/365 with a smile.

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Introduction to Machine Opinings: Machine Learning and Philosophy

For as long as I can remember I’ve been interested in how we (as humans) know things. But more than that, I wanted to create things that know things, to build something that could learn, understand and interact naturally with us. Of course at first I didn’t know that what I was interested in was philosophy, specifically a branch called epistemology, and that creating intelligent machines was the aim of artificial intelligence (AI).

I remember as an undergraduate a philosophy professor said something that stuck with me – great philosophers aren’t those that have the answers, but those that ask important questions. Philosophy aims to understand the world around us, why we do what we do, how we know what we know; it’s not about having the right answer as much as to keep asking questions.

Historically most sciences start off as part of philosophy, and then once they become better understood split off into distinct subjects. The hard, scientific part, where hypotheses are conjectured and empirically evaluated, usually becomes associated with the science, and the squishier aspects remain in philosophy.

Computer Science and its AI subfield are no different. At first computer scientists like Turing and Von Neumann engaged both philosophical and technical aspects of AI. But today with the increasingly successful practical applications of machine learning, most AI practitioners, more accurately machine learning practitioners, focus on how to apply it to solve specific problems. This has led to considerable advancements in our scientific understanding, but without much consideration in the machine learning community for the societal understanding of the implications, or their relation to the vast heritage of philosophical ideas.

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