In this post, Don Miner covers how to identify, evaluate, prioritize, and pick which data science problems to work on next. The entire process is aimed to help you make your decisions, but also share them in a way to effectively level-set with the rest of your organization.

Learning rates are a hyper-parameter that controls how much we are adjusting the weights of our network with respect the loss gradient. This post explores the topic or learning rates and how to apply them in deep learning practices.

A very common scenario: a business runs tens and tens of A/B tests over the course of a year, and many of them “win”. Some tests get you 25% uplift in revenue, or even higher. Yet – when you roll out the change, the revenue does not increase 25%. And 12 months after running all those tests, the conversion rate is still pretty much the same. How come? The answer is this: your uplifts were imaginary.

Stories are how people make sense of the world, so it follows that they’d also be our way of making sense of data. As much as technology can facilitate data storytelling, it cannot perform the pivotal step of placing data into the human context. That’s our job. We’re such natural storytellers, in fact, that most of us already use a story format for presenting information.

We believe learning from data scientists who have hands-on experience in the field is a great way to advance your career. This directory is designed to make your life easier as it organizes the most useful articles written in 2017, where experienced data scientists share their lessons in building and shipping a machine learning application.