توضیحات
This book bridges the gap between data science and software engineering, emphasizing the skills needed to develop reliable, production-ready data applications. It covers software engineering principles such as version control, testing, code quality, modular design, documentation, and collaboration. Readers learn how to structure projects, manage data pipelines, and implement best practices for reproducible and maintainable code. The book also highlights challenges specific to machine learning and data-driven systems, providing strategies to address them effectively. Ideal for data scientists, ML engineers, and analysts, it equips readers with the tools and knowledge to combine analytical insights with robust software development practices.
نقد و بررسیها
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