Software Engineering for Data Scientists
O'Reilly Media (Verlag)
978-1-0981-3620-8 (ISBN)
Examples are provided in Python, drawn from popular packages such as NumPy and pandas. If you want to write better data science code, this guide covers the essential topics you need (and that are often missing from introductory data science or coding classes), including how to:
Understand data structures and object-oriented programming
Clearly and skillfully document your code
Package and share your code
Integrate data science code with a larger codebase
Write APIs
Create secure code
Apply best practices to common tasks such as testing, error handling, and logging
Work more effectively with software engineers
Write more efficient, maintainable, and robust code in Python
Put your data science projects into production
And more
Catherine Nelson 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. She has been recognized as a Google Developer Expert in machine learning. 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.
| Erscheinungsdatum | 30.04.2024 |
|---|---|
| Verlagsort | Sebastopol |
| Sprache | englisch |
| Maße | 178 x 233 mm |
| Einbandart | kartoniert |
| Themenwelt | Mathematik / Informatik ► Informatik ► Software Entwicklung |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| ISBN-10 | 1-0981-3620-9 / 1098136209 |
| ISBN-13 | 978-1-0981-3620-8 / 9781098136208 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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