Yes, the pandemic sucks. But as someone who looks at data for a living, there are at least opportunities for growth, even in the worst of times!

One way you can grow as a data scientist is to look at the ways infection rates affect other related variables (just be sure to take a removed analytical approach, as putting emotion into this can be… less than ideal).

To begin, just install the covid19.analytics package made my Marcelo Ponce and Amit Sandhel using the following command:

install.packages("covid19.analytics")

Then all you have to do is load the library using:

library(covid19.analytics)

After you’ve…

Summary statistics are an essential first step to understanding your data. Luckily, the dplyr package has a super easy to use function that will allow you to quickly calculate summary stats. In this tutorial, we will:

- Review the components of the summarize() function
- Go over some use cases

We’ll be piping data so I’m going to assume you already have some familiarity with dplyr. You should also note that summarize() is the same as summarise(), so use whichever notation is most commonly used in your native language.

As always, load the dplyr package using:

library(dplyr)

In this tutorial…

The NBA is coming back.

At least, for a little while. We’ll see how well they hold up in the Orlando bubble surrounded by a pandemic hot zone.

But enough of the sadness. Let’s talk actual basketball. It feels like forever since live NBA action. The last games were played on March 11, but that might as well have been a year ago. I’m not even sure I remember how sweet a Luka step-back looks.

Do you?

To re-familiarize yourself with the NBA before play restarts, you should probably rewatch games on League Pass. …

I like to write. At least, that’s what I tell myself.

But I’m 100% guilty of not writing consistently.

I could blame it on a lot of things — both intrinsic and extrinsically motivated — but the reality is that I don’t write a lot because I’m scared of failing.

I’m scared of typos. I’m scared of not explaining my opinion correctly. I’m scared of being wrong.

I’m just scared.

Some of this is due to my anxious nature. The current pandemic climate doesn’t help. …

I love science. Truly and deeply love it.

But it’s not perfect.

In fact, one of the defining characteristics that makes the field so special is also one of its biggest weaknesses when it comes to knowledge dissemination.

Science is rigorous

One of the things I love most about science is that scientists care deeply about their projects. They read all the literature. They think long and hard about their projects. They use appropriate statistical techniques to make sure their results are valid.

But the meticulous nature of the field is also one of the reasons most scientists suck at writing. There’s already…

Learning how to do sports analytics in is a great way to learn data science and machine learning techniques. I recently ran across a Twitter thread that had a great bunch of recommendations for books to read on the subject. Here they are, outlined by category:

Sports Analytics (General)

• Calculated Bets: Computers, Gambling, and Mathematical Modeling to Win By Steven S. Skiena
• Handbook of Statistical Methods and Analysis in Sports
• Mathletics: How Gamblers, Managers, and Sports Enthusiasts Use Mathematics in Baseball, Basketball, and Football by Wayne L Winston
• Scorecasting: The Hidden Influences Behind How Sports are Played and Games are Won by Tobias…

Basketball | NBA

In this article you'll learn how to:
- install and load nbastatR
- get every NBA player loaded into R
- get a player's career statistics

The NBA season is on hold. Which, you could look at as a negative and just watch “The Last Dance” on repeat.

Or, you could look at this as an opportunity to finally learn how to do some data science on the NBA and really learn the history of the league and the players.

Install and load

There are a few R packages that that let you grab NBA data. I use the one called nbastatR because…