Rocks, data science, and breaking into Machine Learning

Meet Catherine Nelson, Principal Data Scientist at SAP Concur and author of the upcoming O'Reilly book "Software Engineering for Data Scientists". Join us as we talk about Catherine's amazing career journey as she pivoted from geophysicist to working on setting the standard for building machine learning pipelines. According to Catherine, it all starts with how you prepare and train your data!  

 

Resources:
Building Machine Learning Pipelines → https://goo.gle/3nLBpDI 

Software Engineering for Data Scientists → https://goo.gle/3Kz3F5u 

TensorFlow Meets → https://goo.gle/43a8yZN 

Twitter →https://goo.gle/3m8b0zq 

LinkedIn →https://goo.gle/3ZJmd7o 

 

Guest bio:

Catherine Nelson is a data scientist and author of the upcoming O'Reilly book "Software Engineering for Data Scientists". She is a Principal Data Scientist at SAP Concur, where she explores innovative ways to deliver production machine learning applications which improve a business traveler's experience. Her key focus areas range from ML explainability and model analysis to privacy-preserving ML. She is also co-author of the O'Reilly publication "Building Machine Learning Pipelines", and she is an organizer for Seattle PyLadies, supporting women who code in Python. In her previous career as a geophysicist she studied ancient volcanoes and explored for oil in Greenland. Catherine has a PhD in geophysics from Durham University and a Masters of Earth Sciences from Oxford University.

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