One of the most important parts of a data scientist’s job is to be able to visualize and get insights from data. What if there was a simpler way to explore data? What if we could change input variables simply by sliding a mouse or we could control the degree of the polynomial by selecting from a drop-down menu, and then see the output change instantaneously? This would allow data scientists to quickly prototype their ideas. Interactive widgets in Jupyter Notebooks allow one to do exactly that. The interact function (ipywidgets.interact) automatically creates user interface controls to explore code and data interactively. In this talk, I will highlight the powerful capabilities that widgets and interactive functions provide, and how one can efficiently leverage them to build interactive Jupyter Notebooks.