Think Complexity: Complexity Science And Computational Modeling
Publish Date: 2012-03-12
Author: Allen B. Downey
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Expand your Python skills by working with data structures and algorithms in a refreshing contextthrough an eye-opening exploration of complexity science. Whether youre an intermediate-level Python programmer or a student of computational modeling, youll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanations.
Youll work with graphs, algorithm analysis, scale-free networks, and cellular automata, using advanced features that make Python such a powerful language. Ideal as a text for courses on Python programming and algorithms, Think Complexity will also help self-learners gain valuable experience with topics and ideas they might not encounter otherwise.
Work with NumPy arrays and SciPy methods, basic signal processing and Fast Fourier Transform, and hash tables
Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines
Get starter code and solutions to help you re-implement and extend original experiments in complexity
Explore the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, and other topics
Examine case studies of complex systems submitted by students and readers