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The official Jupyter Conference
Aug 21-22, 2018: Training
Aug 22-24, 2018: Tutorials & Conference
New York, NY
Catherine Ordun

Catherine Ordun
Senior Data Scientist, Booz Allen Hamilton

Website | @nudro

Catherine Ordun is a Washington, DC-based senior data scientist at Booz Allen Hamilton. Catherine’s background is in biology, public health, and business. A self-taught Python programmer, she has led data science work across the US government, including intelligence and public health agencies and the DoD. She serves on the Women in Data Science Committee at Booz Allen, has presented to the National Academy of Medicine, and led her team to the top three in a Health and Human Services opioid codeathon. Catherine is a two-time recipient of the Women of Color (WoC) award and is currently a program reviewer for SciPy2018. She is passionate about machine learning, has recently started participating in Kaggle challenges, and has started an internal firm-wide machine intelligence meetup.

Sessions

11:55am–12:35pm Friday, August 24, 2018
The Jupyter Notebook as a transparent way to document machine learning model development: A case study from a US defense agency
JupyterCon Business Summit, Training and education, Usage and application
 Location: Concourse A: Business Summit Level: Beginner
Catherine Ordun (Booz Allen Hamilton)
Average rating: *****
(5.00, 5 ratings)
Many US government agencies are just getting started with machine learning. As a result, data scientists need to de-"black box" models as much as possible. One simple way to do this is to transparently show how the model is coded and its results at each step. Notebooks do just this. Catherine Ordun walks you through a notebook built for RNNs and explains how government agencies can use notebooks. Read more.