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Most books that use MATLAB are aimed at readers who know how to program. This book is for people who have never programmed before. As a result, the order of presentation is unusual. The book starts with scalar values and works up to vectors and matrices very gradually. This approach is good for beginning programmers, because it is hard to understand composite objects until you understand basic programming semantics. But there are problems: The MATLAB documentation is written in terms of matrices, and so are the error messages. To mitigate this problem, the book explains the necessary vocabulary early and deciphers some of the messages that beginners find confusing. Many of the examples in the first half of the book are nonstandard MATLAB. I address this problem in the second half by translating the examples into a more idiomatic style. The book puts a lot of emphasis on functions, in part because they are an important tool for controlling program complexity, and also because they are useful for working with MATLAB tools like fzero and ode45. I assume that readers know calculus, differential equations, and physics, but not linear algebra. I explain the math as I go along, but the descriptions might not be enough for someone who hasn't seen the material before. There are small exercises within each chapter, and a few larger exercises at the end of some chapters.

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How to Think Like a Computer Scientist: Learning with Pythonis an introduction to programming using Python.
 Subjects:
 Computing
 Keywords:
 Computer programming Programming languages (Electronic computers) Textbooks Python (Computer program language)
 Resource Type:
 ebook

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Think Raku is an introduction to computer science and programming intended for people with little or no experience. This aim of this book is not primarily to teach Raku, but instead to teach the art of programming, using the Raku language. After having completed this book, you should hopefully be able to write programs to solve relatively difficult problems in Raku, but my main aim is to teach computer science, software programming, and problem solving rather than solely to teach the Raku language itself. Think Raku is a free book available under a Creative Commons license. Readers are free to copy and distribute the text; they are also free to modify it, which allows them to adapt the book to different needs, and to help develop new material.
 Subjects:
 Computing
 Keywords:
 Computer programming Programming languages (Electronic computers) Textbooks
 Resource Type:
 ebook

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Data structures and algorithms are among the most important inventions of the last 50 years, and they are fundamental tools software engineers need to know. But in my opinion, most of the books on these topics are too theoretical, too big, and too bottomup: Too theoretical: Mathematical analysis of algorithms is based on simplifying assumptions that limit its usefulness in practice. Many presentations of this topic gloss over the simplifications and focus on the math. In this book I present the most practical subset of this material and eliminate the rest. Too big: Most books on these topics are at least 500 pages, and some are more than 1000. By focusing on the topics I think are most useful for software engineers, I kept this book under 250 pages. Too bottomup: Many data structures books focus on how data structures work (the implementations), with less about how to use them (the interfaces). In this book, I go “top down”, starting with the interfaces. Readers learn to use the structures in the Java Collections Framework before getting into the details of how they work. Finally, many present this material out of context and without motivation: it’s just one damn data structure after another! I try to alleviate the boredom by organizing the topics around an application—web search—that uses data structures extensively, and is an interesting and important topic in its own right. This application also motivates some topics that are not usually covered in an introductory data structures class, including persistent data structures, with Redis, and streaming algorithms. This book also presents basic aspects of software engineering practice, including version control and unit testing. Each chapter ends with an exercise that allows readers to apply what they have learned. Each exercise includes automated tests that check the solution. And for most exercises, I present my solution at the beginning of the next chapter. This book is intended for college students in computer science and related fields, as well as professional software engineers, people training in software engineering, and people preparing for technical interviews. I assume that the reader knows Java at an intermediate level, but I explain some Java features along the way, and provide pointers to supplementary material. People who have read Think Java or Head First Java are prepared for this book.
 Subjects:
 Computing
 Keywords:
 Java (Computer program language) Data structures (Computer science) Textbooks
 Resource Type:
 ebook

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The goal of this book is to teach you to think like a computer scientist. I like the way computer scientists think because they combine some of the best features of Mathematics, Engineering, and Natural Science. Like mathematicians, computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating trade offs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.The single most important skill for a computer scientist is problemsolving. By that I mean the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problemsolving skills. That’s why this chapter is called “The way of the program.”
 Subjects:
 Computing
 Keywords:
 Computer programming Programming languages (Electronic computers) C (Computer program language) Textbooks
 Resource Type:
 ebook

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The goal of this book is to teach you to think like a computer scientist. I like the way computer scientists think because they combine some of the best features of Mathematics, Engineering, and Natural Science. Like mathematicians,computer scientists use formal languages to denote ideas (specifically computations). Like engineers, they design things, assembling components into systems and evaluating trade offs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.The single most important skill for a computer scientist is problemsolving. By that I mean the ability to formulate problems, think creatively about solutions, and express a solution clearly and accurately. As it turns out, the process of learning to program is an excellent opportunity to practice problemsolving skills. That’s why this chapter is called “The way of the program.”
 Subjects:
 Computing
 Keywords:
 Computer programming C++ (Computer program language) Programming languages (Electronic computers) Textbooks
 Resource Type:
 ebook

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Think DSP is an introduction to Digital Signal Processing in Python. The premise of this book (and the other books in the Think X series) is that if you know how to program, you can use that skill to learn other things. The author is writing this book because he thinks the conventional approach to digital signal processing is backward: most books (and the classes that use them) present the material bottomup, starting with mathematical abstractions like phasors.
 Subjects:
 Electrical Engineering and Computing
 Keywords:
 Signal processing  Digital techniques  Data processing Python (Computer program language) Textbooks
 Resource Type:
 ebook

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This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science: Data structures and algorithms: A data structure is a collection that contains data elements organized in a way that supports particular operations. For example, a dictionary organizes keyvalue pairs in a way that provides fast mapping from keys to values, but mapping from values to keys is generally slower. An algorithm is a mechanical process for performing a computation. Designing efficient programs often involves the coevolution of data structures and the algorithms that use them. For example, the first few chapters are about graphs, a data structure that is a good implementation of a graphnested dictionariesand several graph algorithms that use this data structure. Python programming: This book picks up where Think Python leaves off. I assume that you have read that book or have equivalent knowledge of Python. As always, I will try to emphasize fundmental ideas that apply to programming in many languages, but along the way you will learn some useful features that are specific to Python. Computational modeling: A model is a simplified description of a system that is useful for simulation or analysis. Computational models are designed to take advantage of cheap, fast computation. Philosophy of science: The models and results in this book raise a number of questions relevant to the philosophy of science, including the nature of scientific laws, theory choice, realism and instrumentalism, holism and reductionism, and Bayesian epistemology. This book focuses on discrete models, which include graphs, cellular automata, and agentbased models. They are often characterized by structure, rules and transitions rather than by equations. They tend to be more abstract than continuous models; in some cases there is no direct correspondence between the model and a physical system. Complexity science is an interdisciplinary fieldat the intersection of mathematics, computer science and physicsthat focuses on these kinds of models. That's what this book is about.
 Subjects:
 Computing
 Keywords:
 Computational complexity Python (Computer program language) Textbooks
 Resource Type:
 ebook

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Think Bayes is an introduction to Bayesian statistics using computational methods. The premise of this book, and the other books in the Think X series, is that if you know how to program, you can use that skill to learn other topics. Most books on Bayesian statistics use mathematical notation and present ideas in terms of mathematical concepts like calculus. This book uses Python code instead of math, and discrete approximations instead of continuous mathematics. As a result, what would be an integral in a math book becomes a summation, and most operations on probability distributions are simple loops. I think this presentation is easier to understand, at least for people with programming skills. It is also more general, because when we make modeling decisions, we can choose the most appropriate model without worrying too much about whether the model lends itself to conventional analysis. Also, it provides a smooth development path from simple examples to realworld problems.
 Subjects:
 Computing and Mathematics and Statistics
 Keywords:
 Bayesian statistical decision theory Python (Computer program language) Textbooks
 Resource Type:
 ebook

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Think Stats is an introduction to Probability and Statistics for Python programmers. Think Stats emphasizes simple techniques you can use to explore real data sets and answer interesting questions. The book presents a case study using data from the National Institutes of Health. Readers are encouraged to work on a project with real datasets. If you have basic skills in Python, you can use them to learn concepts in probability and statistics. Think Stats is based on a Python library for probability distributions (PMFs and CDFs). Many of the exercises use short programs to run experiments and help readers develop understanding.
 Subjects:
 Computing and Mathematics and Statistics
 Keywords:
 Textbooks Statistics  Computer programs
 Resource Type:
 ebook