Learn all about modularity, documentation, & automated testing to help you solve Data Science problems quicker and more reliably.
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.
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!
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.
-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.
-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.