What if you could turn your #datascience analysis into a web application? You can do exactly that with R Shiny. R Shiny is an amazing framework built to convert your data analysis into a web app - FAST! Create amazing applications your business can use in hours (not months!).

I'm super excited to introduce modeltime.h2o, an H2O AutoML backend for forecasting

Skimr is my go-to R package for fast data quality assessment, and Skimr is my first step in exploratory data analysis. Before I do anything else, I check data quality with skimr.

In Learning Labs PRO Episode 50, Matt tackles an in-depth tutorial on Hierarchical Forecasting using the M5 Forecasting Competition.

Did you know most Data Scientists spend 80% of their time just trying to understand and prepare data for analysis? R has an Insane Exploratory Data Analysis productivity-enhancer. It's called DataExplorer.

Many data scientists struggle with distributing their work, however, you can make that a problem of the past thanks to Shiny. Here are five reasons you should learn Shiny and why it is a game-changer for upskilling your career.

Shiny is an R web framework with a HUGE ECOSYSTEM of interactive widgets, themes, and customizable user interfaces called the Shinyverse. In this article, we use Shiny to make our R Markdown Report interactive.

Both R and Python are great. We’ll showcase some of the strengths of each language in this article by showcasing where the major development efforts are within each ecosystem.

Exploratory Data Analysis is what every data scientist does to understand actionable insights from the data. This process used to take forever. Not anymore...

Max Kuhn, from RStudio, discusses in-depth feature engineering for customer analytics. Watch Max and Matt tackle a tough feature engineering problem for customer analytics prediction.

I never thought I'd be able to make publication-ready statistical plots so easily. Seriously. Thanks to ggstatsplot.

In order to determine where you wish to set your career trajectory, you need to understand the grey area and differences between data scientists and data analysts.