Learn and master Python on real world projects and join the Competition: Can you find the fastest Code?
In this course you learn how to solve real world problems with simulation techniques in Python.
You will be able to run complex simulations with basic Python code (standard library).
You will understand and experience the full power of the Numpy package and you will be able to significantly speed up your projects with vectorized Numpy code.
You are encouraged to actively participate by doing the quizzes and instructed interactive coding exercises on your own!
This course is designed to steadily grow by adding more and more real world problems, now starting with the Sticker Album challenge. Find the most efficient strategies to complete your Football World Cup Album or whatever Sticker Album / Trading Cards collection you can think of!
Another project is in preparation: Forget about Tinder, use Python and solve for the best strategies to find your princess / prince charming!
This course is about solving problems with thousands or even millions of simulations. So efficiency and speed is of essence! If you find a faster code, join the competition and post it!
Although this course is not intentionally designed as a Python Tutorial for Beginners, it however starts at zero and covers all the basics and prerequisites in the Basics sections. Hence, Beginners are very welcome, in particular those who want to minimize the time between learning the basics with toy examples and application in more complex real world situations. Advanced Pythonists can assess their skills and skip the Basics sections.
This course covers in particular:
-Data Types (Integers, Floats, Strings, Lists, Sets, Booleans, Numpy Arrays)
-Operators (Arithmetic, Comparison, Logical)
-if, elif, else statements
-for and while loops
-User defined Functions
-Numpy Package (Arrays, Random, Ufunc, Regression, Taylor Expansion)
-Vectorized coding with Numpy Arrays
-Evaluating Speed difference between basic Python code and vectorized Numpy code
-Visualization with matplotlib