- 面向数据科学家的软件工程(影印版)(英文版)
- - 作者：(英)凯瑟琳·纳尔逊|责编:张烨
  - 出版社：东南大学
  - ISBN：9787576617672
  - 出版日期：2025/02/01
  - 页数：238
- 售价：39.6

内容大纲
    数据科学离不开代码。编写可复现、稳健、可伸缩代码的能力是数据科学项目成功的关键，对于那些和生产代码打交道的人来说，这一点至关重要。这本实用书籍填补了数据科学与软件工程之间的空白，清晰地解释了如何将软件工程的最佳实践应用于数据科学。
    本书提供的示例基于Python，取材于NumPy和pandas等流行的包。如果你想编写更好的数据科学代码，本指南涵盖了数据科学入门或编码课程中经常缺失的重要主题，包括如何：
    理解数据结构和面向对象编程
    清晰且熟练地记录代码
    打包并共享你的代码
    将数据科学代码集成到更大的代码库中
    学习编写API
    创建安全的代码
    将最佳实践应用于测试、错误处理、日志记录等常见任务
    更高效地与软件工程师合作
    编写更高效、可维护、稳健的Python代码
    将你的数据科学项目投入生产
    等等
作者介绍
凯瑟琳·纳尔逊（Catherine Nelson），是一名自由数据科学家和作家。此前，她曾担任SAP Concur的首席数据科学家，开发生产级机器学习应用，并创建了崭新的商务旅行功能。她还是O’Reilly出版的Building Machine Learning Pipelines一书的合著者。
目录
Preface
1. What Is Good Code?
  Why Good Code Matters
  Adapting to Changing Requirements
  Simplicity
    Don't Repeat Yourself (DRY)
    Avoid Verbose Code
  Modularity
  Readability
    Standards and Conventions
    Names
    Cleaning up
    Documentation
  Performance
  Robustness
    Errors and Logging
    Testing
  Key Takeaways
2. Analyzing Code Performance
  Methods to Improve Performance
  Timing Your Code
  Profiling Your Code
    cProfile
    line_profiler
    Memory Profiling with Memray
  Time Complexity
    How to Estimate Time Complexity
    Big O Notation
  Key Takeaways
3. Using Data Struaures Effeaively
  Native Python Data Structures
    Lists
    Tuples
    Dictionaries
    Sets
  NumPy Arrays
    NumPy Array Functionality
    NumPy Array Performance Considerations
    Array Operations Using Dask
    Arrays in Machine Learning
  pandas DataFrames
    DataFrame Functionality
    DataFrame Performance Considerations
  Key Takeaways
4. Object-Oriented Programming and Functional Programming
  Ob)ect-Oriented Programming
    Classes, Methods, and Attributes
    Defining Your Own Classes
    OOP Principles
   Functional Programming

    Lambda Functions and map()
    Applying Functions to DataFrames
  Which Paradigm Should I Use?
   Key Takeaways
5. trr0rs, togging, and Debugging
   Errors in Python
    Reading Python Error Messages
    Handling Errors
    Raising Errors
   Logging
    What to Log
    Logging Configuration
    How to Log
   Debugging
    Strategies for Debugging
    Tools for Debugging
  Key Takeaways
6. Code Formatting, Linting, and Type Checking
  Code Formatting and Style Guides
    PEP8
    Import Formatting
    Automatic Code Formatting with Black
  Linting
    Linting Tools
    Linting in Your IDE
  Type Checking
    Type Annotations
    Type Checking with mypy
  Key Takeaways
7. Testing Your Code
  Why You Should Write Tests
  When to Test
  How to Write and Run Tests
    A Basic Test
    Testing Unexpected Inputs
    Running Automated Tests with Pytest
  Types of Tests
    Unit Tests
    Integration Tests
  Data Validation
    Data Validation Examples
    Using Pandera for Data Validation
    Data Validation with Pydantic
   Testing for Machine Learning
    Testing Model Training
    Testing Model Inference
   Key Takeaways
8. Design and Refactoring
  Project Design and Structure
    Project Design Considerations

    An Example Machine Learning Project
  Code Design
    Modular Code
    A Code Design Framework
    Interfaces and Contracts
    Coupling
  From Notebooks to Scalable Scripts
    Why Use Scripts Instead of Notebooks?
    Creating Scripts from Notebooks
  Refactoring
    Strategies for Refactoring
    An Example Refactoring Workflow
   Key Takeaways
9. Documentation
  Documentation Within the Codebase
    Names
    Comments
    Docstrings
    Readmes, Tutorials, and Other Longer Documents
  Documentation in Jupyter Notebooks
  Documenting Machine Learning Experiments
  Key Takeaways
10. Sharing Your Code: Version Control, Dependencies, and Packaging
  Version Control Using Git
    How Does Git Work?
    Tracking Changes and Committing
    Remote and Local
    Branches and Pull Requests
  Dependencies and Virtual Environments
    Virtual Environments
    Managing Dependencies with pip
    Managing Dependencies with Poetry
  Python Packaging
    Packaging Basics
    pyproject.toml
    Building and Uploading Packages
  Key Takeaways
11. APIs
  Calling an API
    HTTP Methods and Status Codes
    Getting Data from the SDG API
  Creating Your Own API Using FastAPI
    Setting Up the API
    Adding Functionality to Your API
    Making Requests to Your API
  Key Takeaways
12. Automation and Deployment
  Deploying Code
  Automation Examples
    Pre-Commit Hooks

    GitHub Actions
  Cloud Deployments
    Containers and Docker
    Building a Docker Container
    Deploying an API on Google Cloud
    Deploying an API on Other Cloud Providers
  Key Takeaways
13. Security
  What Is Security?
  Security Risks
    Credentials, Physical Security, and Social Engineering
    Third-Party Packages
    The Python Pickle Module
    Version Control Risks
    API Security Risks
  Security Practices
    Security Reviews and Policies
    Secure Coding Tools
    Simple Code Scanning
  Security for Machine Learning
    Attacks on ML Systems
    Security Practices for ML Systems
    Key Takeaways
14. Working in Software
  Development Principles and Practices
    The Software Development Lifecycle
    Waterfall Software Development
    Agile Software Development
    Agile Data Science
  Roles in the Software Industry
    Software Engineer
    QA or Test Engineer
    Data Engineer
    Data Analyst
    Product Manager
    UX Researcher
    Designer
  Community
    Open Source
    Speaking at Events
    The Python Community
  Key Takeaways
15. Next Steps
  The Future of Code
  Your Future in Code
  Thank You
Index

内容大纲

作者介绍

目录

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