Computer Exercises in Complex Systems

Instructions:

If you are just starting, read about Getting Started in Python.
From the list below, click on one of the links to a course exercise of interest.
Follow the instructions on the exercise page. Often there will be some background material (e.g., a research article) for you to read to understand the scientific or computational context of the exercise. In addition to the background material, exercises will generally consist of an exercise file with instructions and a Python hints file. You will generally seek to fill out the skeletal hints file, based on the instructions in the exercise file. When in doubt about how to proceed, just ask.
The most common error is trying to fill in the hints file without looking at the html/pdf instructions; the second most common error is moving on too quickly in your code development, so that a previously written function is not fully tested before it is used in some other function.
A common theme is that after you write a program to generate some data, you will need to figure out what the data means. Don't rush over this part. This is really the most important part of using computers to do science. Using the ipython notebook interface to document your analysis and show your results is a good way of bringing all that work together in one place.
There is no need to do the exercises in any order. All students should first complete one or more of the "warmup exercises" before proceeding on to one of the main course modules.

Warm-up exercises

Estimating pi via Monte Carlo
Prime Numbers
Random Text Generation
Complex Networks