7 Top Books to Learn Software Engineering Principles for Data Science & AI

While expertise in algorithms and statistical methods is essential, understanding how to develop robust, scalable, and maintainable software systems is equally important for data scientists and AI practitioners. This article presents a curated selection of seven top books that focus on the intersection of software engineering and data science.

This resource is designed for data scientists, AI professionals, and engineers who seek to enhance their software engineering skills, allowing them to effectively design, deploy, and manage AI and data-driven systems. It is also valuable for those transitioning into the fields of data science and AI from a software engineering background, helping them bridge the gap between the two domains.

Table of Contents:

1. Software Engineering for Data Science
2. Building Machine Learning Powered Applications
3. Machine Learning Engineering with AWS
4. The Pragmatic Programmer: Your Journey to Mastery
5. Design Patterns: Elements of Reusable Object-Oriented Software
6. Data Science at Scale with Python and Dask
7. Designing Data-Intensive Applications

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