Learning to program isn't just learning the details of a programming language: to become a good programmer you have to become expert at debugging, testing, writing clear code and generally unsticking yourself when you get stuck, while to do well in a programming course you have to learn to score highly in coursework and exams. Featuring tips, stories and explanations of key terms, this book teaches these skills explicitly. Examples in Python, Java and Haskell are included, helping you to gain transferable programming skills whichever language you are learning. Intended for students in Higher or Further Education studying early programming courses, it will help you succeed in, and get the most out of, your course, and support you in developing the software engineering habits that lead to good programs.

Programming Languages: An Active Learning Approach introduces students to three programming paradigms: object-oriented/imperative languages using C++ and Ruby, functional languages using Standard ML, and logic programming using Prolog. This interactive textbook is intended to be used in and outside of class. Each chapter follows a pattern of presenting a topic followed by a practice exercise or exercises that encourage students to try what they have just read. This textbook is best-suited for students with a 2-3 course introduction to imperative programming. Key Features: (1) Accessible structure guides the student through various programming languages. (2) Seamlessly integrated practice exercises. (3) Classroom-tested. (4) Online support materials. Advance praise: “The Programming Languages book market is overflowing with books, but none like this. In many ways, it is precisely the book I have been searching for to use in my own programming languages course. One of the main challenges I perpetually face is how to teach students to program in functional and logical languages, but also how to teach them about compilers. This book melds the two approaches very well.” -- David Musicant, Carleton College

Learn the principles of good software design and then turn those principles into great code. This book introduces you to software engineering — from the application of engineering principles to the development of software. You'll see how to run a software development project, examine the different phases of a project, and learn how to design and implement programs that solve specific problems. This book is also about code construction — how to write great programs and make them work. This new third edition is revamped to reflect significant changes in the software development landscape with updated design and coding examples and figures. Extreme programming takes a backseat, making way for expanded coverage of the most crucial agile methodologies today: Scrum, Lean Software Development, Kanban, and Dark Scrum. Agile principles are revised to explore further functionalities of requirement gathering. The authors venture beyond imperative and object-oriented languages, exploring the realm of scripting languages in an expanded chapter on Code Construction. The Project Management Essentials chapter has been revamped and expanded to incorporate "SoftAware Development” to discuss the crucial interpersonal nature of joint software creation. Whether you're new to programming or have written hundreds of applications, in this book you'll re-examine what you already do, and you'll investigate ways to improve. Using the Java language, you'll look deeply into coding standards, debugging, unit testing, modularity, and other characteristics of good programs. You Will Learn Modern agile methodologies How to work on and with development teams How to leverage the capabilities of modern computer systems with parallel programming How to work with design patterns to exploit application development best practices How to use modern tools for development, collaboration, and source code controls Who This Book Is For Early career software developers, or upper-level students in software engineering courses

Computer Science

This book describes how model selection and statistical inference can be founded on the shortest code length for the observed data, called the stochastic complexity. This generalization of the algorithmic complexity not only offers an objective view of statistics, where no prejudiced assumptions of 'true' data generating distributions are needed, but it also in one stroke leads to calculable expressions in a range of situations of practical interest and links very closely with mainstream statistical theory. The search for the smallest stochastic complexity extends the classical maximum likelihood technique to a new global one, in which models can be compared regardless of their numbers of parameters. The result is a natural and far reaching extension of the traditional theory of estimation, where the Fisher information is replaced by the stochastic complexity and the Cramer-Rao inequality by an extension of the Shannon-Kullback inequality. Ideas are illustrated with applications from parametric and non-parametric regression, density and spectrum estimation, time series, hypothesis testing, contingency tables, and data compression.