Software Engineering for Data Scientists: From Notebooks to Scalable Systems

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
1144735708
Software Engineering for Data Scientists: From Notebooks to Scalable Systems

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
59.99 In Stock
Software Engineering for Data Scientists: From Notebooks to Scalable Systems

Software Engineering for Data Scientists: From Notebooks to Scalable Systems

by Catherine Nelson
Software Engineering for Data Scientists: From Notebooks to Scalable Systems
Add to Wishlist
Software Engineering for Data Scientists: From Notebooks to Scalable Systems

Software Engineering for Data Scientists: From Notebooks to Scalable Systems

by Catherine Nelson

Overview

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

Product Details

ISBN-13: 9781098136161
Publisher: O'Reilly Media, Incorporated
Publication date: 04/16/2024
Sold by: Barnes & Noble
Format: eBook
Pages: 260
File size: 5 MB

About the Author

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.

From the B&N Reads Blog
Page 1 of

Customer Reviews