Welcome to this self-study module on the fundamentals of scientific computing in numpy.
In the areas such as scientific computing, machine learning, HPC, or simulation, numpy has become the core library when using python to solve the problem (and most people do this in python). Because of this, it is absolutely essentially to be familiar with NumPy at least from a user's perspective. In layman's terms you need to know how to not shoot yourself in the foot with numpy while writing a program that uses numpy arrays (or numpy-like arrays) to represent data. This is where these notebooks come in.
Each section of this module comes in a dedicated jupyter notebook. Solutions for each exercise can be found right underneath the exercise (simply expand the spoiler/details).
We also have a small test that asks you to solve four problems/exercises
specific to each section. This is meant as a help for you to estimate which
sections of the course might benefit you the most. You can find the test
in SelfAssessment.ipynb.
Currently the following sections are available:
- Array Creation, Indexing and Slicing
- Vectorization and the Functional API
- Broadcasting and Fancy Indexing
- Memory Layout and Numpy Views