Python

In this post, we’ll analyze reviews and ratings from Beeradvocate.com to understand what drives satisfaction amongst beer drinkers worldwide. Prost!

College rankings are a standard input for most students when choosing a school. But to what extent does a college’s rank relate to how much a graduate makes 10 years into their career? We’ll answer this question by web scraping data from a variety of online sources with R and Python, and then build a model to understand which factors matter most to post-college pay.

Portland, Oregon is home to some of the best watering holes in America. With so many places to quaff a West Coast Style IPA or glass of Pinot Noir, choosing which to visit (and in which order) can be a daunting task. To address this question, we’ll leverage some classic optimization techniques to minimize the total distance travelled between the top bars in Portland for a truly “optimal” Pub Crawl.

Keras is quickly becoming the go-to prototyping solution for computer vision problems, and this post provides an overview of how to rapidly build a Convolutional Neural Network in R with the Keras library.

Aaron Rodgers or Tom Brady? Carson Wentz or Drew Brees? Choosing the right Fantasy Football QB each week is challenging. To remove some of the guesswork from the decision-making process, I devised an approach that’s worked well over the past few seasons. Read on to learn more about using the Beta Distribution to pick your weekly starting QB.

Drafting a rookie in Fantasy Football can be a risky move, but it can pay huge dividends if you happen to snag a diamond in the rough. After accounting for a player’s draft position, do physical attributes (height/weight) and combine performance (40 yard dash, bench press, etc.) provide any additional explanatory power of points scored during a player’s first NFL season? I’ll explore this question for rookie Running Backs and Wide Receivers.

This post focuses on some of my favorite things – football and forecasting – and will outline how to leverage external regressors when creating forecasts. We’ll do some web scraping in R and Python to create our dataset, and then forecast how many people will visit Tom Brady’s Wikipedia page.

Exception handling is a critical component of any data science workflow. You write code. It breaks. You build logic to deal with the exceptions. Repeat. From my experience, one of point of confusion for new R users is how to handle exceptions, which is a bit more intuitive in Python. Accordingly, this post provides a practical overview of how to handle exceptions in R by first illustrating the concept in Python.