Home-court advantage in the NBA For obvious reasons, 2020 has not been the best of years, and alongside the terrible societal and economical turmoil created by the COVID-19 pandemic, the sports and entertainment industry has also undergone some dramatic transformations. Of all major sports leagues, the NBA was able to adapt ... [Read more...]

Rental Prices in the US As a fortunate resident of New York City, I get to enjoy the many activities offered by a city that never really turns off its lights. The price to pay for such entertainment, however, are the crazy rental fees. I have stopped counting the number ... [Read more...]

If you have previously attempted to analyze NFL data, it is likely that you have tried to scrape ESPN or football-reference, which provides a wealth on statistics surrounding game data. However, if you ever wanted to obtain truly in-depth data, then it... [Read more...]

Many companies across a multitude of industries are currently maintaining data pipelines used to ingest and analyze large data streams. In effect, the proper implementation of such pipelines belongs to the realm of “data engineering”, and represent... [Read more...]

Not too long ago, I came across a random tweet pointing to a GitHub repository full of miscallaneous datasets. I was imemdiately excited by the various data science and visualization problems tha I could take on and immediately decided to get my hand... [Read more...]

Who lies the most in US politics? Most Americans, and anyone that follows US politics, will be aware of the tremendous changes and volatility that has struck the US political landscape in the past year. The ascent of Donald Trump from a billionaire entertainer to a fully fledged presidential candidate, ... [Read more...]

Using docker to facilitate your data science pipelines Until recently, and like many other fellow data scientists I have talked to, I built data science pipelines on my local machine or a remote host while relying on virtual environments. In doing so, I ensured some degree of replicability by keeping ... [Read more...]

Recently, professional sports associations and teams have made big strides towards leveraging data to inform both personel and on-the-field decision making. While the four major leagues (NBA, NFL, MLB, NHL) vary in terms of where they are in that process, most people would argue that the NBA is at the ... [Read more...]

Scraping presidential transcripts To begin, we must scrape the content of all presidential speeches recorded in American history. To do that, I’ll rely on the very handy BeautifulSoup library, and eventually store all data in a pandas dataframe that will be persisted in a pickle file. # import required libraries ... [Read more...]

If you have ever played around with Algorithms & Data Structures, then you most likely have heard of Leetcode.com, which contains a number of famous (or infamous) of technical questions. One of my favorite in there is the graph clone question, which can be shortly stated as:

Clone an undirected graph. Each node in the graph contains a label and a list of its neighbors.

Like most ... [Read more...]

I recently discovered the enigma.io resource, a repository of freely available public with the following goals (as stated on their website): The volume of data created by governments and businesses is growing exponentially. Organizations struggle ju... [Read more...]

In part I and II of this post series on NBA game dynamics, I explored both the average score differential and proportion of time spent in the lead for each NBA team. Interestingly, this proved to not be a very reliable proxy for overall performance and number of wins accrued ... [Read more...]

In part I of this post series on NBA game dynamics, I explored the average score differential that teams faced over the course of games. Interestingly, this proved to not be a very reliable proxy for overall performance and number of wins accrued over an entire season. To continue, I ... [Read more...]