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Software Engineering Principles in Python

4.1+
17 reviews
Beginner

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

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4 Hours15 Videos51 Exercises
37,564 Learners

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Course Description

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.
  1. 1

    Software Engineering & Data Science

    Free

    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!

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    Python, data science, & software engineering
    50 xp
    The big ideas
    50 xp
    Python modularity in the wild
    100 xp
    Introduction to packages & documentation
    50 xp
    Installing packages with pip
    50 xp
    Leveraging documentation
    100 xp
    Conventions and PEP 8
    50 xp
    Using pycodestyle
    100 xp
    Conforming to PEP 8
    100 xp
    PEP 8 in documentation
    100 xp
  2. 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.

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    Writing your first package
    50 xp
    Minimal package requirements
    50 xp
    Naming packages
    100 xp
    Recognizing packages
    100 xp
    Adding functionality to packages
    50 xp
    Adding functionality to your package
    100 xp
    Using your package's new functionality
    100 xp
    Making your package portable
    50 xp
    Writing requirements.txt
    100 xp
    Installing package requirements
    50 xp
    Creating setup.py
    100 xp
    Listing requirements in setup.py
    100 xp
  3. 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.

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    Adding classes to a package
    50 xp
    Writing a class for your package
    100 xp
    Using your package's class
    100 xp
    Adding functionality to classes
    50 xp
    Writing a non-public method
    100 xp
    Using your class's functionality
    100 xp
    Classes and the DRY principle
    50 xp
    Using inheritance to create a class
    100 xp
    Adding functionality to a child class
    100 xp
    Using your child class
    100 xp
    Multilevel inheritance
    50 xp
    Exploring with dir and help
    100 xp
    Creating a grandchild class
    100 xp
    Using inherited methods
    100 xp
  4. 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.

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    Documentation
    50 xp
    Identifying good comments
    100 xp
    Identifying proper docstrings
    100 xp
    Writing docstrings
    100 xp
    Readability counts
    50 xp
    Using good function names
    100 xp
    Using good variable names
    100 xp
    Refactoring for readability
    100 xp
    Unit testing
    50 xp
    Using doctest
    100 xp
    Using pytest
    100 xp
    Documentation & testing in practice
    50 xp
    Documenting classes for Sphinx
    100 xp
    Identifying tools
    50 xp
    Final Thoughts
    50 xp

In the following tracks

Data EngineerPython ProgrammerPython Programming

Collaborators

Collaborator's avatar
Chester Ismay
Collaborator's avatar
Adrián Soto

Prerequisites

Python Data Science Toolbox (Part 1)Introduction to Shell
Adam Spannbauer HeadshotAdam Spannbauer

Machine Learning Engineer at Eastman

Adam is a machine learning engineer at Eastman Chemical Company. His work history has had a focus on NLP projects using open source data science tools such as Python, R, and Shiny. He stays active in the open source community on GitHub, mostly working on side-projects involving computer vision. Adam holds degrees from Maryville College and the University of Tennessee.
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Don’t just take our word for it

*4.1
from 17 reviews
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  • Lucas M.
    3 months

    fun and practical

  • Aleksandr B.
    8 months

    It’s helpful at all

  • Muhammad M.
    8 months

    Excellent

  • Nicolas K.
    8 months

    at times it was a bit confusing. It would be great if soe graphics and segmentation of code are indicated

  • Maciej G.
    10 months

    Course was good quality, showing proper usage of packets and modules,

"fun and practical"

Lucas M.

"It’s helpful at all"

Aleksandr B.

"Excellent"

Muhammad M.

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