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 problem-solving. 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 problem-solving skills. That’s why this chapter is called “The way of the program.”
The single most important skill for a computer scientist is problem solving. The goal of this book is to teach you to think like a computer scientist.
Python is a fun and extremely easy-to-use programming language that has steadily gained in popularity over the last few years. Developed over ten years ago by Guido van Rossum, Python's simple syntax and overall feel is largely derived from ABC, a teaching language that was developed in the 1980's. However, Python was also created to solve real problems and it borrows a wide variety of features from programming languages such as C++, Java, Modula-3, and Scheme. Because of this, one of Python's most remarkable features is its broad appeal to professional software developers, scientists, researchers, artists, and educators. 278 page pdf file.
The Little Book of Semaphores is a free (in both senses of the word) textbook that introduces the principles of synchronization for concurrent programming.In most computer science curricula, synchronization is a module in an Operating Systems class. OS textbooks present a standard set of problems with a standard set of solutions, but most students don't get a good understanding of the material or the ability to solve similar problems.The approach of this book is to identify patterns that are useful for a variety of synchronization problems and then show how they can be assembled into solutions. After each problem, the book offers a hint before showing a solution, giving students a better chance of discovering solutions on their own.The book covers the classical problems, including "Readers-writers," "Producer-consumer", and "Dining Philosophers." In addition, it collects a number of not-so-classical problems, some written by the author and some by other teachers and textbook writers. Readers are invited to create and submit new problems.
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.
The book is appropriately comprehensive, covering the basics as well as interesting and important applications of Bayesian methods.
This book is about complexity science, data structures and algorithms, intermediate programming in Python, and the philosophy of science. This book focuses on discrete models, which include graphs, cellular automata, and agent-based 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.
The examples and supporting code for this book are in Python. You should know core Python and you should be familiar with object-oriented features, at least using objects if not defining your own. If you are not already familiar with Python, you might want to start with my other book, Think Python, which is an introduction to Python for people who have never programmed, or Mark Lutz’s Learning Python, which might be better for people with programming experience.
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 bottom-up:
*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 bottom-up: 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.
I have made difficult decisions about what to leave out, but I have made some compromises. I include a few topics that most readers will never use, but that they might be expected to know, possibly in a technical interview. For these topics, I present both the conventional wisdom as well as my reasons to be skeptical.
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.
Think Java is an introduction to Java programming for beginners. It is tailored for students preparing for the Computer Science Advanced Placement (AP) Exam, but it is for anyone who wants to learn Java.
The goal of this book is to teach you to think like a computer scientist. This way of thinking combines 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 tradeoffs among alternatives. Like scientists, they observe the behavior of complex systems, form hypotheses, and test predictions.
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.
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.