Software Engineering for Data Scientists: From Notebooks to Scalable Systems

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Publisher ‏ : ‎ O’Reilly Media; 1st edition (May 21, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 257 pages
ISBN-10 ‏ : ‎ 1098136209
ISBN-13 ‏ : ‎ 978-1098136208
Item Weight ‏ : ‎ 14.7 ounces
Dimensions ‏ : ‎ 7 x 0.55 x 9.19 inches

Software engineering is an essential skill for data scientists looking to scale their projects and handle large datasets efficiently. While many data scientists are familiar with tools like Jupyter notebooks for exploratory analysis, transitioning to scalable systems requires a deeper understanding of software engineering principles.

In this post, we’ll explore the key concepts data scientists need to know to build scalable systems, from designing software architecture to optimizing code for performance. We’ll cover topics such as version control, code testing, and deployment strategies that are crucial for managing complex data pipelines and machine learning models.

By mastering software engineering principles, data scientists can take their projects to the next level and build robust, efficient systems that can handle a wide range of data processing tasks. Whether you’re working with big data, streaming data, or complex machine learning models, a solid foundation in software engineering will help you tackle these challenges with confidence.

Stay tuned for more insights on how data scientists can leverage software engineering principles to build scalable systems and take their projects to new heights.
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