Bloomberg takes a look at the unique role of data science in professional basketball.

From the description:

With her PhD in math, Ivana Seric had expected to wind up with a career in academia—but thanks to the growing use of statistical analysis in the NBA, she took a job with the Philadelphia 76ers instead. As a data scientist, she helps the team’s coaches devise smarter strategies to win.

Here’s a great exploration of Bayes’ Theorem and how to use it in real world problems.

Bayes’ theorem is a way to figure out conditional probability. Conditional probability is the probability of an event happening, given that it has some relationship to one or more other events. For example, your probability of getting a parking space is connected to the time of day you park, where you park, and what conventions are going on at any time. Bayes’ theorem is slightly more nuanced. In a nutshell, it gives you the actual probability of an event given information about tests.

Gradient Descent is the workhorse behind much of Machine Learning. When you fit a machine learning method to a training dataset, you’re almost certainly using Gradient Descent.

The process can optimize parameters in a wide variety of settings. Since it’s so fundamental to Machine Learning, Josh Starmer of StatQuest decided to make a “step-by-step” video that shows exactly how it works.

Heads up: there is some singing.

I’m often asked where’s the best place to get started in data science and AI. My answer is almost always the same: statistics. Statistics is the bedrock of data science and it’s a core pillar of AI. You could make the argument that statistics make up the core for understanding reality itself, but I have not had enough coffee yet to engage in such philosophical banter.

Here’s a great video on Probability vs. Likelihood. In common conversation we tend to use these words interchangeably. However, statisticians make a clear distinction that is important to understand if you want to follow their logic. Like most of statistics, they are both super simple and easy to get mixed up. F