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Synopsis
Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success--and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.
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 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 code base
- Learn how to 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
Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.
À propos de l'auteur
Catherine Nelson is a freelance data scientist and writer. Previously, she was a Principal Data Scientist at SAP Concur, where she developed production machine learning applications and created innovative new business travel features. She's also coauthor of O'Reilly's Building Machine Learning Pipelines.
Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.
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Software Engineering for Data Scientists: From Notebooks to Scalable Systems
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ISBN 10 : 1098136209
ISBN 13 : 9781098136208
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Software Engineering for Data Scientists: From Notebooks to Scalable Systems
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Software Engineering for Data Scientists: From Notebooks to Scalable Systems (Paperback or Softback)
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Software Engineering for Data Scientists : From Notebooks to Scalable Systems
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ISBN 13 : 9781098136208
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Software Engineering for Data Scientists
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Paperback. Etat : New. Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success-and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, clearly explaining how to apply the best practices from software engineering to data science.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 programmingClearly and skillfully document your codePackage and share your codeIntegrate data science code with a larger codebaseWrite APIsCreate secure codeApply best practices to common tasks such as testing, error handling, and loggingWork more effectively with software engineersWrite more efficient, maintainable, and robust code in PythonPut your data science projects into productionAnd more. N° de réf. du vendeur LU-9781098136208
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Software Engineering for Data Scientists: From Notebooks to Scalable Systems
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Paperback. Etat : new. Paperback. Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success--and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science. 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 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 code base Learn how to 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 This practical book bridges the gap between data science and software engineering, clearly explaining how to apply the best practices from software engineering to data science. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781098136208
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Software Engineering for Data Scientists: From Notebooks to Scalable Systems
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