Brandon Rohrer is a Staff Machine Learning Engineer at LinkedIn. Brandon's specialty is creating algorithms and computational methods. You can find examples of his work here.

Optimization problems are naturally described in terms of costs - money, time, resources - rather than benefits. In math it's convenient to make all your problems look the same before you work out a solution, so that you can just solve it the one time.

There are many wonderful things about data science. It’s extreme breadth is not one of them. The title of data scientist means something different at every company

You are not the only one who wonders how much longer they can get away with pretending to be a data scientist. You are not the only one who has nightmares about being laughed out of your next interview.

Job hunting is challenging and sometimes frustrating task and we all experience it in our career. Here we provide a very specific and practical guide to get your dream job in Data Science world.

Bayesian inference isn’t magic or mystical; the concepts behind it are completely accessible. In brief, Bayesian inference lets you draw stronger conclusions from your data by folding in what you already know about the answer. Read an in-depth overview here.

A series of videos and write-ups covering the basics of data science for beginners. This first video is about the kinds of questions that data science can answer.

A pocket guide overview of how to get started doing data science, with a focus on the practical, and with concrete steps to take to get moving right away.

There are only five questions machine learning can answer: Is this A or B? Is this weird? How much/how many? How is it organized? What should I do next? We examine these questions in detail and what it implies for data science.