You've picked a project, but have you actually picked the right tool for it? In this module, you'll compare how Jupyter Notebook and PyCharm each extend their core capabilities through plugins, built-in performance profiling, and support for machine learning work.

You've got the basics down, but real projects eventually push past what either tool does out of the box. In this module, you'll see how Jupyter Notebook and PyCharm each extend their core capabilities through plugins and extensions, built-in performance profiling, and support for machine learning work.

Knowing a tool's features is one thing, but recognizing yourself in how it's actually used is another. In this module, you'll look at the kinds of developers, researchers, and teams who gravitate toward Jupyter Notebook or PyCharm so you can see where you fit.

You've built a real framework for choosing between two of Python's most popular development tools, but frameworks only matter once you actually use them. In this module, you'll apply what you now know about Jupyter Notebook and PyCharm to your own next project and walk away with a clear plan for evaluating any Python tool you encounter going forward.