As data scientists, it can be downright impossible to drill into messy data. Fortunately, there's a new R package that helps us focus on a "high-density region". It's called ggdensity.

Machine learning is great... until you have to explain it. Stakeholders are normally non-technical, C-suites that ultimately want to know what the model does for the business. And how it helps increase revenue or decrease costs. A new R package, modelDown can help.

Learn how to do survival analysis in R in under 10-minutes. Plus get 3 bonuses to take your survival plots to the NEXT LEVEL. Let's go!

If you are looking to learn about the most overlooked R package that can help you get through a job interview AND you probably don't know it yet, you've come to the right place, my friend! And, if you want a job in data science, I'm going to show you how THIS R package can help you get through an interview with 5 lines of code.

Big data? Lot's of time series? Traditionally you'd use ggplot facets. But that only works for a few datasets. Enter trelliscopejs. It's a game changer!

The data science career path is demystified in this article showing you 2 case studies and a ton of research on how to set your career up for going from $75,000 to $150,000 in 1-year.

Which skills are important to becoming a data scientist? How to pick a language? How to learn the skills? These questions and many more are answered in this post.

The modelStudio library offers an interactive studio for developing and exploring explainable AI visualizations.

I'm super impressed by the radiant R package. With no prior exposure to radiant, I was able to complete a short business analytics report in under 10-minutes.

I'm super excited to introduce a new R package that makes it painless for data scientists to create a professional.

I'm super excited to introduce the new Modeltime Backend for Spark. Let's use it to perform forecasting with tidymodels.

The ggplot2 package is an essential tool in every data scientists toolkit. Today we show you how to use ggplot2 to make a professional heatmap that organizes customers by their sales purchasing habits.