Want to get better at data science? Click here to get notified when I release new content on both Medium and my personal website where I publish exclusive posts. I remember a feeling of utter confusion when I first learned vector spaces in my first course of Linear Algebra. What’s a space? And what are these vectors, really? Lists? Functions? Kittens? And how the hell does that relate to all this data that I analyze for some scientific endeavor…

Want to get better at data science? Click here to get notified when I release new content on both Medium and my personal website where I publish exclusive posts. Doing good science is hard and a lot of experiments fail. Although the scientific method helps to reduce uncertainty and lead to discoveries, its path is full of potholes. In this post, you’ll learn about common p-value misinterpretations, p-hacking, and the problem with performing multiple hypothesis tests. Of course, not…

Want to get better at data science? Click here to get notified when I release new content on both Medium and my personal website where I publish exclusive posts. “Prove it” is a phrase thrown around with glee. But rarely are you really challenging a friend to provide a rigorous proof of some statement you disagree with. In science, there is no proof. Instead, the scientific method encourages you to form a hypothesis, collect data, and test that hypothesis…

Want to get better at data science? Click here to get notified when I release new content on both Medium and my personal website where I publish exclusive posts. Docker is hot in the developer world and although data scientists aren’t strictly software developers, Docker has some very useful features for everything from data exploration and modeling to deployment. And since major services like AWS support Docker containers, it’s even easier to implement Continuous Integration Continuous Delivery with Docker…

Want to get better at data science? Click here to get notified when I release new content on both Medium and my personal website where I publish exclusive posts. A Few Useful Things to Know about Machine Learning is a high-level machine learning paper written by Pedro Domingos of the computer science and engineering department at the University of Washington. His paper details some useful machine learning guidelines and the following are some highlights I took from it.

Want to get better at data science? Click here to get notified when I release new content on both Medium and my personal website where I publish exclusive posts. Awhile ago, I had AWS set up to provide me a unique URL that I could navigate to and use Jupyter Notebooks. I admired the convenience and the ability to just start a computation and close my laptop knowing full well my computations continued working away. However, using an AWS…