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.

Analysts and data scientists with a deep understanding of multiple analytical programming languages find themselves at an advantage in today’s data landscape. The prevailing dialogue around this kind of multi-lingual approach, especially with SQL and Python, typically portrays the languages as complementary, yet functionally discrete. But there are actually many overlapping functions that can be accomplished by both SQL and Python.

So you've built a software product, and you have some users. You're generating data about how users interact with your product. You want to share this data with those users. You want to do so in a way that benefits both your users and your company.

As an analyst or data scientist, it’s becoming increasingly advantageous to have a deep understanding of multiple analytical programming languages. There's a growing number of people using SQL and Python in a hybrid-fashion for data analysis. But, the dialogue around the usage of these two languages tends to portray them as complementary, but functionally discrete.