One of the growth areas for Python over the last few years has been in the area of data science: Tools such as Jupyter (aka “IPython Notebook“), along with NumPy and Pandas, have made Python one of the main languages that people use when doing data science. But how does a data scientist use Jupyter?  How does a data-science firm use it to promote collaboration and exploration? And where are notebooks note less preferable than traditional Python modules? In this talk, Brian Lange provides a high-level overview of where and how Jupyter notebooks are used in his data-science firm, discussing their relative advantages and disadvantages in consulting and training.

Time: 23 minutes