商品描述
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 programming
- Clearly and skillfully document your code
- Package and share your code
- Integrate data science code with a larger codebase
- 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
商品描述(中文翻譯)
資料科學是以程式碼進行的。能夠撰寫可重複、穩健、可擴展的程式碼是資料科學專案成功的關鍵，對於那些從事生產程式碼的人來說更是必不可少。這本實用書籍填補了資料科學與軟體工程之間的鴻溝，清楚解釋了如何將軟體工程的最佳實踐應用於資料科學。書中提供了以Python為例的示例，使用了流行的套件，如NumPy和pandas。如果你想寫出更好的資料科學程式碼，這本指南涵蓋了你需要的基本主題（這些主題通常在入門資料科學或編程課程中缺失），包括如何：理解資料結構和物件導向編程、清晰且熟練地記錄你的程式碼、打包和分享你的程式碼、將資料科學程式碼與更大的程式碼庫整合、撰寫API、創建安全程式碼、將最佳實踐應用於常見任務，如測試、錯誤處理和日誌記錄、與軟體工程師更有效地合作、在Python中撰寫更高效、可維護和穩健的程式碼、將你的資料科學專案投入生產等等。
