A few months ago, I wrote about the Predictive Power Score (PPS): a handy metric to quickly explore and quantify the relationships in a dataset. As a social scientist, I was taught to use a correlation matrix to describe the relationships in a dataset. Yet, in my opinion, the PPS ...

For a year now, this course on Bayesian statistics has been on my to-do list. So without further ado, I decided to share it with you already. Richard McElreath is an evolutionary ecologist who is famous in the stats community for his work on Bayesian statistics. At the Max Planck ...

Data types are one of those things that you don’t tend to care about until you get an error or some unexpected results. It is also one of the first things you should check once you load a new data into pandas for further analysis. Chris Moffit In this ...

Jonas Kristoffer Lindeløv wrote a great visual explanation of how the most common statistical tests (t-test, ANOVA, ANCOVA, etc) are all linear models in the back-end. Jonas’ original blog uses R programming to visually show how the tests work, what the linear models look like, and how different approaches ...

Working with Python’s pandas library often? This resource will be worth its length in gold! Kevin Markham shares his tips and tricks for the most common data handling tasks on twitter. He compiled the top 100 in this one amazing overview page. Find the hyperlinks to specific sections below! Quicklinks ...

In this great tutorial for PyCon 2020, Eric Ma proposes a very simple framework for machine learning, consisting of only three elements: Model Loss function Optimizer By adjusting the three elements in this simple framework, you can build any type of machine learning program. In the tutorial, Eric shows you how ...

A/B testing is a method of comparing two versions of some thing against each other to determine which is better. A/B tests are often mentioned in e-commerce contexts, where the things we are comparing are web pages. Business leaders and data scientists alike face a difficult trade-off when ...

These days, I am often programming in multiple different languages for my projects. I will do some data generation and machine learning in Python. The data exploration and some quick visualizations I prefer to do in R. And if I’m feeling adventureous, I might add some Processing or JavaScript ...