All Data Scientists Should Learn Software Engineering Principles // Catherine Nelson // Podcast #245
Skills:
ML Pipelines70%
Why All Data Scientists Should Learn Software Engineering Principles // MLOps podcast #245 with Catherine Nelson, a freelance Data Scientist.
A big thank you to @latticeflowfor sponsoring this episode! LatticeFlow AI - https://latticeflow.ai/
// Abstract
Data scientists have a reputation for writing bad code. This quote from Reddit sums up how many people feel: “It's honestly unbelievable and frustrating how many Data Scientists suck at writing good code.” But as data science projects grow, and because the job now often includes deploying ML models, it's increasingly important for DSs to learn fundamental SWE principles such as keeping your code modular, making sure it is readable by others, and so on. The exploratory nature of DS projects means that you can't be sure where you will end up at the start of a project, but there's still a lot you can do to standardize the code you write.
// Bio
Catherine Nelson is the author of "Software Engineering for Data Scientists", a guide for data scientists who want to level up their coding skills, published by O'Reilly in May 2024. She is currently consulting for GenAI startups and providing mentorship and career coaching to data scientists. Previously, she was a Principal Data Scientist at SAP Concur. She has extensive experience deploying NLP models to production and evaluating ML systems, and she is also co-author of the book "Building Machine Learning Pipelines", published by O'Reilly in 2020. 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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// Related Links
Software Engineering for Data Scientists book by Catherine Nelson:
https://learning.oreilly.com/library/view/software-engineering-for/9781098136192/
https://www.amazon.com/Software-Engine
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