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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.
PrefaceChapter 1: Complexity ScienceChapter 2: GraphsChapter 3: Analysis of AlgorithmsChapter 4: Small World GraphsChapter 5: Scale-Free NetworksChapter 6: Cellular AutomataChapter 7: Game of LifeChapter 8: FractalsChapter 9: Self-Organized CriticalityChapter 10: Agent-Based ModelsChapter 11: Case Study: SugarscapeChapter 12: Case Study: Ant TrailsChapter 13: Case Study: Directed Graphs and KnotsChapter 14: Case Study: The Volunteer’s DilemmaCall for SubmissionsReading ListColophon
m_k_o
Posted April 3, 2012
This one is not an easy one. Allen guides you through the various, complex, algorithms and data structures. This book is not for a beginners – you have to know Python already to solve exercises presented by author. The complexity of the book itself is also rather for slightly advanced developers. If you just start your journey with Python development it may be hard to follow.
What I liked, however, is the way Allen presents the material. He tries to show you different aspects of the development process and refers not only to computer science but to philosophy and mathematics as well. Even if you won’t be able to solve all the presented puzzles it is still worth getting through the book.
Few remarks regarding what I really liked in the book. First of all, Allen provides you with lots of references. So, if you are interested in particular topic, you have plenty of sources to start with. Secondly, Allen provides you with references to Wikipedia very often. This is not regarded usually as a good source among “university like people”, however I like this kind of approach a lot.
Overview
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 ...