| Summary: | Python is a dynamically typed language--types are evaluated by the interpreter as the program is executed. This gives developers a lot of flexibility but it also makes Python fragile and prone to run-time errors! In this course, you will learn how to add type annotations to your Python code. Adding types makes your program more reliable and less likely to fail at run time, your code will be more readable and easier to collaborate with, and you will be able to write less trivial unit tests. What you'll learn and how you can apply it Understand the difference between dynamically and statically typed language, and compiled vs. interpreted languages, and how to use typing to gain the best of both worlds. Be able to use basic typing in Python to annotate variables, functions and classes, and use Mypy to type check code. Understand advanced topics such as generics, type narrowing, and exhaustiveness checking, as well as best practices for configuring Mypy and adopting typing in existing projects. This course is for you because... You're a programmer working with Python. You're looking to improve the reliability of your code. You're interested in ways to prevent runtime errors in your code. You're in a team of developers and you care about the readability of your code. Prerequisites: Basic knowledge of Python Basic knowledge of system design Recommended follow-up: Data Management at Scale, 2nd Edition (book)
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