کتاب مهندسی نرم‌افزار برای دانشمندان داده

کد خوب چیست؟

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

تحلیل عملکرد کد

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

استفاده موثر از ساختارهای داده

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

برنامه‌نویسی شی‌گرا و تابعی

Object-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

خطاها، لاگ‌گیری و دیباگ کردن

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

فرمت‌دهی، لینتینگ و بررسی نوع داده

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

تست کردن کد

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

طراحی و بازنویسی کد

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

مستندسازی

Documentation Within the Codebase • Names • Comments • Docstrings • Readmes, Tutorials, and Other Longer Documents • Documentation in Jupyter Notebooks • Documenting Machine Learning Experiments • Key Takeaways

اشتراک‌گذاری کد: کنترل نسخه، وابستگی‌ها و بسته‌بندی

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

رابط‌های برنامه‌نویسی کاربردی (API)

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

اتوماسیون و استقرار

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

امنیت

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

کار در حوزه نرم‌افزار

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

گام‌های بعدی

The Future of Code • Your Future in Code • Thank You