课程

Software Engineering Principles in Python

基础技能水平

更新时间 2025年11月

Learn about modularity, documentation, and automated testing to help you solve data science problems more quickly and reliably.

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PythonProgramming4 小时15 视频51 练习4,100 经验值64,497成就声明

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课程描述

Data scientists can experience huge benefits by learning concepts from the field of software engineering, allowing them to more easily reutilize their code and share it with collaborators. In this course, you'll learn all about the important ideas of modularity, documentation, & automated testing, and you'll see how they can help you solve Data Science problems quicker and in a way that will make future you happy. You'll even get to use your acquired software engineering chops to write your very own Python package for performing text analytics.

先决条件

Introduction to Functions in Python

1

Software Engineering & Data Science

Why should you as a Data Scientist care about Software Engineering concepts? Here we'll cover specific Software Engineering concepts and how these important ideas can revolutionize your Data Science workflow!

Python, data science, & software engineering

50 经验值

The big ideas

50 经验值

Python modularity in the wild

100 经验值

Introduction to packages & documentation

50 经验值

Installing packages with pip

50 经验值

Leveraging documentation

100 经验值

Conventions and PEP 8

50 经验值

Using pycodestyle

100 经验值

Conforming to PEP 8

100 经验值

PEP 8 in documentation

100 经验值

2

Writing a Python Module

Become a fully fledged Python package developer by writing your first package! You'll learn how to structure and write Python code that you can be installed, used, and distributed just like famous packages such as NumPy and Pandas.

Writing your first package

50 经验值

Minimal package requirements

50 经验值

Naming packages

100 经验值

Recognizing packages

100 经验值

Adding functionality to packages

50 经验值

Adding functionality to your package

100 经验值

Using your package's new functionality

100 经验值

Making your package portable

50 经验值

Writing requirements.txt

100 经验值

Installing package requirements

50 经验值

Creating setup.py

100 经验值

Listing requirements in setup.py

100 经验值

3

Utilizing Classes

Object Oriented Programming is a staple of Python development. By leveraging classes and inheritance your Python package will become a much more powerful tool for your users.

Adding classes to a package

50 经验值

Writing a class for your package

100 经验值

Using your package's class

100 经验值

Adding functionality to classes

50 经验值

Writing a non-public method

100 经验值

Using your class's functionality

100 经验值

Classes and the DRY principle

50 经验值

Using inheritance to create a class

100 经验值

Adding functionality to a child class

100 经验值

Using your child class

100 经验值

Multilevel inheritance

50 经验值

Exploring with dir and help

100 经验值

Creating a grandchild class

100 经验值

Using inherited methods

100 经验值

4

Maintainability

You've now written a fully functional Python package for text analysis! To make maintaining your project as easy as possible we'll leverage best practices around concepts such as documentation and unit testing.

Documentation

50 经验值

Identifying good comments

100 经验值

Identifying proper docstrings

100 经验值

Writing docstrings

100 经验值

Readability counts

50 经验值

Using good function names

100 经验值

Using good variable names

100 经验值

Refactoring for readability

100 经验值

Unit testing

50 经验值

Using doctest

100 经验值

Using pytest

100 经验值

Documentation & testing in practice

50 经验值

Documenting classes for Sphinx

100 经验值

Identifying tools

50 经验值

Final Thoughts

50 经验值

Software Engineering Principles in Python

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