I’ll begin this post with a vulnerable statement about a topic which has almost 0 consequence on life and whose weight is only felt by me: I cannot figure out how to win at fantasy football.

Hey there. My intended audience for this post is someone who is a decision maker at an organization,

On whence someone asked me “now that I am finished with class, what books should I read and videos should I watch in order to learn more about this field?” and I answered “get ready to drink from the firehose.”

At any company there are at least two problems that are incredibly novel that you as a Data Scientist could unlock. Beyond that though, there are a number of common problems that seem to span across any company in any industry.

I generally think that my preparation of a company before I go into an interview is a distinguishing and standout trait.

Here’s a list of questions that I’ve experienced in interviews or have asked in interviews.

If you’re diligently following along, you may have seen that I’m doing a series on machine learning models from scratch.

One of the best ways to forget what you’ve done is to not catalog it somehow. Munger says that knowledge must hang on a mental latticework in your head, but I say that we should just let the internet remember everything for us. To that end, I’m going to talk about how to deploy a knowledge repository, using the open source tool that the titans over at AirBnB open sourced.

Tomorrow begins a new cohort in the part-time data science class I teach at General Assembly. This bootcamp thing is fantastic. Note that these views are mine, not necessarily those of Generaly Assembly.

I’ve been reading some really great papers lately. I am especially curious about the practical applications of abstractive text summarization.

Hello world! I am making this to catalog projects, musings, and other things which are important to only me.