Python for Scientific Computing
This course discusses how Python can be utilized in scientific computing. The course starts by introducing some of the main Python tools for computing: Jupyter for interactive analysis, NumPy and SciPy for numerical analysis, Matplotlib for visualization, and so on.
High level language programming
difficulty:
beginner
maturity:
stable
Overview
The course is targeted towards these learner personas:
- A is a early career PhD researcher who has been using Python a bit, but is not sure what they know or don’t know. They want to be able to do their research more efficiently and make sure that they are using the right tools. A may know that numpy exists, etc. and could theoretically read some about it themselves, but aren’t sure if they are going in the right direction.
- A2 can use numpy and pandas, but have learned little bits here and there and hasn’t had a comprehensive introduction. They want to ensure they are using best practices. (Baseline of high-level packages)
- B is a mid-to-late undergraduate student who has used Python in some classes. They have possibly learned the syntax and enough to use it in courses, but in a course-like manner where they are expected to create everything themselves: they want to know how to reuse tools that already exist.
Learn More
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