Computational notebooks for data science have exploded in popularity in recent years, and there's a growing consensus that the notebook interface is the best environment to communicate the data science process and share its conclusions. We've seen this growth firsthand; notebook support in Mode quickly became one of our most adopted features since launched in 2016.

As an analyst or data scientist, developing a deep understanding of multiple analytical programming languages can create an advantage in today’s data landscape. The prevailing dialogue around a multi-lingual approach, especially with SQL and Python, typically portrays the languages as functionally discrete. But there are many functions that can be accomplished by both SQL and Python.

Python's powerful data analysis capabilities are loved the world over. When it comes time to share findings with your team, taking a few extra steps to prep your analysis for consumption can go a long way towards landing your recommendation. Here are four straightforward guides to get you started with some of the most common and useful functions to make analysis presentation-ready

It's time to add forecasting to your data team's tool kit. Mode notebooks now support Prophet, a powerful forecasting library for Python and R, built by the Core Data Science Team at Facebook. Prophet helps data teams provide high-quality forecasts alongside standard reporting, in a fraction of the time accurate forecasting previously required. To help you jump in, we’ve created a Getting Started guide, that explains how to use Prophet to solve common business forecasting problems.