Think Complexity: Complexity Science and Computational Modeling

Expand your Python skills by working with data structures and algorithms in a refreshing context―through an eye-opening exploration of complexity science. Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of exercises, case studies, and easy-to-understand explanations.

You’ll 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

Allen Downey is an Associate Professor of Computer Science at the Olin College of Engineering. He has taught computer science at Wellesley College, Colby College and U.C. Berkeley. He has a Ph.D. in Computer Science from U.C. Berkeley and Master’s and Bachelor’s degrees from MIT.