This is Data from the Trenches, and we’re here to take you down into the nitty gritty of the data science world. So welcome. We hope you’re ready to get your hands dirty (metaphorically speaking).
Who is down here in the trenches, you ask (i.e., why should you listen to what we have to say)? We are a group of data scientists with diverse backgrounds (researchers, entrepreneurs, engineers, bankers — the list goes on). We want to contribute to the data science community one post at a time by really delving deep into the details.
Our range will be sweeping; everything from the projects we see at different companies to our pet research projects to the data science challenges we take part in. We’ll cover a wide variety of topics, from an introduction to marketing attribution to a series of posts on recommender systems.
If you’re still reading but not convinced, let’s cut to the chase:
- We’ll provide the kinds of content we’re always excited to find — articles with clear, reusable, and commentable code, articles that “unpack” the packages we use (and explain why we use them), articles that track our data sources (and make the data available when we can).
- We contribute to open-source data science — the more we all share our projects and our code, the faster we can all iterate, improve, and innovate.
- We’re super open to public feedback on our work and thinking and are excited to engage with you.
We’ll strive to make the project and code easy to understand and its results reproducible. And rather than failing to mention how tedious a specific task was (or to mention the task at all), we’ll try hard to keep our posts intellectually honest: if the core part of the project was data cleaning or data labelling and not some fancy machine learning modeling, we’ll say so.
In short, we’re excited to be part of the move to make data science open-source, rigorous, and reproducible and to share the cool things we’ve learned along the way.
