Using Jupyter to Take Your Data Science Workflow to the Next Level

Hunter Glanz
California Polytechnic State University


From state-of-the-art research to routine analytics, the Jupyter Notebook offers an unprecedented reporting medium. Historically, tables, graphics, and other types of output had to be created separately and then integrated into a report piece by piece, amidst the drafting of text. The Jupyter Notebook interface enables you to create code cells and markdown cells in any arrangement. Markdown cells allow all typical formatting. Code cells can run code in the document. As a result, report creation happens naturally and in a completely reproducible way. Handing a colleague a Jupyter Notebook file to be re-run or revised is much easier and simpler for them than passing along, at a minimum, two files:one for the code and one for the text. Traditional reports become dynamic documents that include both text and living SASĀ® code that is run during document creation. With the SAS kernel for Jupyter, you have the power to create these computational narratives and much more!