Ever wonder how we work our data science magic? At Heat Seek, we’ve spent the entire summer delving into the data behind NYC’s heating crisis, and this week, we’re going to pull back the curtain and show you how it’s done (or at least how we’ve done it). We want to make sure everyone – from city officials to regular citizens – has the opportunity to follow along.
If it’s over your head, don’t worry about it. But you might be surprised. We’ve taken care to explain what we do in a way that’s understandable, even to tech beginners like our ED, Noelle. She confirmed: you can easily get the gist, even if you don’t know how to code.
And if data science is your thing – and let’s be honest, data nerds might be the only ones regularly following our blog anyway – this week you’re in for a treat.
Each analysis we produce is carefully constructed, and how we collect, analyze and visualize the data is important. In reality, there isn’t any one single method or technique. Our team, like any good data science team, uses a number of tools, methods and programming languages to extract meaningful information from a vast amount of data.
So hop on over to our github, where we’ve provided examples of our methods, and the detailed steps we’ve taken to construct our analyses and visualizations. Whether you’re a beginner or an experienced civic hacker, we invite you to explore the variety of datasets related to New York City’s heating crisis.
- Jesse
Lead data scientist at Heat Seek