Dive into Python's advanced possibilities, including algorithm analysis, graphs, scale-free networks, and cellular automata with this in-depth, hands-on guide.

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

The underlying idea and motive for the book is that the notion of complexity may humanize the social sciences, may conceive the complex human being as more human, and turn reality as assumed in our doing social science into a more complex, that is a richer reality for all. The main focus of this book is on new thinking in complexity, with complexity to be taken as derived from the Latin word complexus: ‘that which is interwoven.’ The trans-disciplinary approach advocated here will be trans-disciplinary in two ways: firstly, by going beyond the separate disciplines within the fields of both natural sciences and social sciences, and, secondly, by going beyond the separate cultures of the natural sciences and of the social sciences and humanities.

Complexity and nonlinearity are prominent features in the evolution of matter, life, and human society. Even our mind seems to be governed by the nonlinear dynamics of the complex networks in our brain. This book considers complex systems in the physical and biological sciences, cognitive and computer sciences, social and economic sciences, and philosophy and history of science. An in terdisciplinary methodology is introduced to explain the emergence of order in nature and mind and in the econ omy and society by common principles. These methods are sometimes said to foreshadow the new sciences of complexity characterizing the scientific deve10pment of the 21 st century. The book critically an alyzes the successes and limits of this approach, its sys tematic foundations, and its historical and philosophical background. An epilogue discusses new standards of eth ical behavior which are demanded by the complex prob lems of nature and mind, economy and society.

Currently used at many colleges, universities, and high schools, this hands-on introduction to computer science is ideal for people with little or no programming experience. The goal of this concise book is not just to teach you Java, but to help you think like a computer scientist. You’ll learn how to program—a useful skill by itself—but you’ll also discover how to use programming as a means to an end. Authors Allen Downey and Chris Mayfield start with the most basic concepts and gradually move into topics that are more complex, such as recursion and object-oriented programming. Each brief chapter covers the material for one week of a college course and includes exercises to help you practice what you’ve learned. Learn one concept at a time: tackle complex topics in a series of small steps with examples Understand how to formulate problems, think creatively about solutions, and write programs clearly and accurately Determine which development techniques work best for you, and practice the important skill of debugging Learn relationships among input and output, decisions and loops, classes and methods, strings and arrays Work on exercises involving word games, graphics, puzzles, and playing cards

The secrets to Apple's success and how to use them, from the Apple insider Ken Segall In Think Simple, Apple insider and New York Times bestselling author Ken Segall gives you the tools to Apple's success - and shows you how to use them. It's all about simplicity. Whether you're in a multinational corporation or a lean startup, this guide will teach you how to crush complexity and focus on what matters; how to perform better, faster and more efficiently. Combining his insight from Apple with examples from companies across industries all over the world - including Ben & Jerry's, Whole Foods, Intel and HyundaiCard - Segall provides a simple roadmap for any company to find success.

If you understand basic mathematics and know how to program with Python, you’re ready to dive into signal processing. While most resources start with theory to teach this complex subject, this practical book introduces techniques by showing you how they’re applied in the real world. In the first chapter alone, you’ll be able to decompose a sound into its harmonics, modify the harmonics, and generate new sounds. Author Allen Downey explains techniques such as spectral decomposition, filtering, convolution, and the Fast Fourier Transform. This book also provides exercises and code examples to help you understand the material. You’ll explore: Periodic signals and their spectrums Harmonic structure of simple waveforms Chirps and other sounds whose spectrum changes over time Noise signals and natural sources of noise The autocorrelation function for estimating pitch The discrete cosine transform (DCT) for compression The Fast Fourier Transform for spectral analysis Relating operations in time to filters in the frequency domain Linear time-invariant (LTI) system theory Amplitude modulation (AM) used in radio Other books in this series include Think Stats and Think Bayes, also by Allen Downey.