About Brandon Rohrer

Brandon Rohrer is a Senior Data Scientist at Facebook, specializing in predictive modeling of complex systems, algorithm design, and general purpose machine learning.

Brandon Rohrer Posts (10)

  • Gold BlogData Science and the Imposter Syndrome - 15 Sep 2017
    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.
  • Silver Blog, March 2017How to Get a Data Science Job: A Ridiculously Specific Guide - 07 Mar 2017
    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.
  • Gold BlogHow Bayesian Inference Works - 15 Nov 2016
    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.
  • How Convolutional Neural Networks Work - 31 Aug 2016
    Get an overview of what is going on inside convolutional neural networks, and what it is that makes them so effective.
  • Data Science for Beginners 2: Is your data ready? - 28 Jul 2016
    This second video and write-up in the Data Science for Beginners series discusses what is required of your data before it can be useful.
  • Data Science for Beginners 1: The 5 questions data science answers - 26 Jul 2016
    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 to Data Science - 11 Apr 2016
    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.
  • What questions can data science answer? - 01 Jan 2016
    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.
  • 5 Criteria To Determine If Your Data Is Ready For Serious Data Science - 21 Dec 2015
    If your data is a large, relevant, accurate, connected, and you also have a sharp question, you ready to do some serious data science. If you’re weak on 1-2 points, don’t worry. But if most criteria are not true, you need to do more preparation.
  • What Types of Questions Can Data Science Answer - 29 Sep 2015
    Data science has enabled us to solve complex and diverse problems by using machine learning and statistic algorithms. Here we have enumerated the common applications of supervised, unsupervised and reinforcement learning techniques