SAS Tutorial | Using Jupyter to Boost Your Data Science Workflow

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 SAS®, R, Python, or other code that is run during document creation. With Jupyter, you have the power to create these computational narratives and much more!

Presenter: Hunter Glanz, California Polytechnic State University
Session ID: 4732
Topic: Programming
Industry: Education, Non-Industry Specific
Audience: Analyst, Educator, Researcher
Skill Level: All

Content outline
00:00 – Welcome
01:15 – Themes of This Talk
01:47 – Historical Collaboration on Projects
04:53 – Historical Deficiencies
09:12 – Capabilities of Ideal Tool
10:02 – Cue the Jupyter Project
13:13 – Demo of Jupyter via SAS University Edition
37:45 – Project Jupyter Summary
42:14 – Conclusions

Additional resources
GitHub – https://github.com/hglanz/SGF2020_JupyterHOW_Glanz
View Proceedings – https://www.sas.com/content/dam/SAS/support/en/sas-global-forum-proceedings/2020/4732-2020.pdf
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