A gentle introduction to our forecasting package, Modeltime. Modeltime extends the Tidymodels ecosystem for time series forecasting. Learn how to forecast with ARIMA, Prophet, and linear regression time series models.

The plotnine library is a powerful python visualization library based on R's ggplot2 package. In this tutorial, we show you how to make a great-looking correlation plot.

I'm super excited to introduce the new parallel processing functionality in modeltime. It's perfect for speeding up hyperparameter tuning of forecast models using parallel processing.

grafify offers 19 plotting functions that make it quick and easy to make great-looking plots in R.

The siuba python library brings the power of R's dplyr and the tidyverse to Python. Gain access to functions like group_by(), mutate(), summarize(), and more!

Pandas Profiling is an awesome python package for Exploratory Data Analysis (EDA). It extends pandas for statistical summaries including correlations, missing values, distributions, and descriptive statistics. It's great for understanding Data Quality too!

gghalves is a new R package that makes it easy to compose your own half-plots using ggplot2

Python for Data Science Automation is an innovative course designed to teach data analysts how to convert business processes to python-based data science automations.

Now you can edit data in R using a GUI that is reminiscent of Excel.

Marginal distributions can now be made in R using ggside, a new ggplot2 extension.

Now you can make publication-ready storyboards. Patchwork makes it simple to combine separate ggplots into the same graphic.

I love ggplot2 for plotting. The grammar of graphics allows us to add elements to plots. Tables seem to be forgotten in terms of an intuitive grammar with tidy data philosophy - Until now.