Learn to code with Python

Learn to master basic programming tasks from scratch with real-life, scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to gain proficiency quickly. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving on to the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualization, this textbook also discusses the use of Jupyter Notebooks to build rich-media, shareable documents for scientific analysis. The second edition features a new chapter on data analysis with the pandas library and comprehensive updates, and new exercises and examples. A final chapter introduces more advanced topics such as floating-p

"Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are

Learn to master basic programming tasks from scratch with real-life scientifically relevant examples and solutions drawn from both science and engineering. Students and researchers at all levels are increasingly turning to the powerful Python programming language as an alternative to commercial packages and this fast-paced introduction moves from the basics to advanced concepts in one complete volume, enabling readers to quickly gain proficiency. Beginning with general programming concepts such as loops and functions within the core Python 3 language, and moving onto the NumPy, SciPy and Matplotlib libraries for numerical programming and data visualisation, this textbook also discusses the use of IPython notebooks to build rich-media, shareable documents for scientific analysis. Including a final chapter introducing challenging topics such as floating-point precision and algorithm stability, and with extensive online resources to support advanced study, this textbook represents a targe

Want to kill it at your job interview in the tech industry? Want to win that coding competition? Learn all the algorithmic techniques and programming skills you need from two experienced coaches, problem setters, and jurors for coding competitions. The authors highlight the versatility of each algorithm by considering a variety of problems and show how to implement algorithms in simple and efficient code. Readers can expect to master 128 algorithms in Python and discover the right way to tackle a problem and quickly implement a solution of low complexity. Classic problems like Dijkstra's shortest path algorithm and Knuth-Morris-Pratt's string matching algorithm are featured alongside lesser known data structures like Fenwick trees and Knuth's dancing links. The book provides a framework to tackle algorithmic problem solving, including: Definition, Complexity, Applications, Algorithm, Key Information, Implementation, Variants, In Practice, and Problems. Python code included in the book

Python is today's fastest growing programming language. This engaging and refreshingly different guide breaks down the skills into clear step-by-step chunks and explains the theory using brief easy-to-understand language. Rather than bamboozling readers with pages of mind-numbing technical jargon, this book includes 150 practical challenges, putting the power in the reader's hands. Through creating programs to solve these challenges the reader will quickly progress from mastering the basics to confidently using subroutines, a graphical user interface, and linking to external text, csv and SQL files. This book is perfect for anyone who wants to learn how to program with Python. In particular, students starting out in computer science and teachers who want to improve their confidence in Python will find here a set of ready-made challenges for classroom use.

Scientific Python is a significant public domain alternative to expensive proprietary software packages. This book teaches from scratch everything the working scientist needs to know using copious, downloadable, useful and adaptable code snippets. Readers will discover how easy it is to implement and test non-trivial mathematical algorithms and will be guided through the many freely available add-on modules. A range of examples, relevant to many different fields, illustrate the language's capabilities. The author also shows how to use pre-existing legacy code (usually in Fortran77) within the Python environment, thus avoiding the need to master the original code. In this new edition, several chapters have been re-written to reflect the IPython notebook style. With an extended index, an entirely new chapter discussing SymPy and a substantial increase in the number of code snippets, researchers and research students will be able to quickly acquire all the skills needed for using Python e

Do you need to produce successful creative ideas in advertising? If so, then you need this book. For the first time, the secret of inventing new creative campaigns is unlocked, and practical tools are presented to allow quick production of creative ideas in marketing communications. Along with over 100 advertisement examples and numerous case studies, you also get a systematic analysis of the creation aspect of advertising, together with a taste of the real world of advertising and what makes it work. Marketing professionals in companies will learn what to expect from their agencies, whilst agencies will be able to explain their work to clients in an analytic language that is easily understood. This is essential reading for advertising professionals working for agencies and in marketing and communication departments. It is also a useful tool for students of advertising, marketing, communication, and management, from introductory level up to research faculty.

Do you need to produce successful creative ideas in advertising? If so, then you need this book. For the first time, the secret of inventing new creative campaigns is unlocked, and practical tools are presented to allow quick production of creative ideas in marketing communications. Along with over 100 advertisement examples and numerous case studies, you also get a systematic analysis of the creation aspect of advertising, together with a taste of the real world of advertising and what makes it work. Marketing professionals in companies will learn what to expect from their agencies, whilst agencies will be able to explain their work to clients in an analytic language that is easily understood. This is essential reading for advertising professionals working for agencies and in marketing and communication departments. It is also a useful tool for students of advertising, marketing, communication, and management, from introductory level up to research faculty.

A no-nonsense introduction to software design using the Python programming language. Written for people with no programming experience, this book starts with the most basic concepts and gradually adds new material. Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. The focus is on the programming process, with special emphasis on debugging. The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practise each new concept. Exercise solutions and code examples are available from thinkpython.com, along with Swampy, a suite of Python programs that is used in some of the exercises.

A no-nonsense introduction to software design using the Python programming language. Written for people with no programming experience, this book starts with the most basic concepts and gradually adds new material. Some of the ideas students find most challenging, like recursion and object-oriented programming, are divided into a sequence of smaller steps and introduced over the course of several chapters. The focus is on the programming process, with special emphasis on debugging. The book includes a wide range of exercises, from short examples to substantial projects, so that students have ample opportunity to practise each new concept. Exercise solutions and code examples are available from thinkpython.com, along with Swampy, a suite of Python programs that is used in some of the exercises.

Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned with text analysis (natural language processing). Many of the basic analysis methods developed are now readily available as Python implementations. This Element will teach you when to use which method, the mathematical background of how it works, and the Python code to implement it.

Do you have a biological question that could be readily answered by computational techniques, but little experience in programming? Do you want to learn more about the core techniques used in computational biology and bioinformatics? Written in an accessible style, this guide provides a foundation for both newcomers to computer programming and those interested in learning more about computational biology. The chapters guide the reader through: a complete beginners' course to programming in Python, with an introduction to computing jargon; descriptions of core bioinformatics methods with working Python examples; scientific computing techniques, including image analysis, statistics and machine learning. This book also functions as a language reference written in straightforward English, covering the most common Python language elements and a glossary of computing and biological terms. This title will teach undergraduates, postgraduates and professionals working in the life sciences how t

JavaTech is a practical introduction to the Java programming language with an emphasis on the features that benefit technical computing. After presenting the basics of object-oriented programming in Java, it examines introductory topics such as graphical interfaces and thread processes. It goes on to review network programming and develops Web client-server examples for tasks such as monitoring remote devices. The focus then shifts to distributed computing with RMI. Finally, it examines how Java programs can access the local platform and interact with hardware. Topics include combining native code with Java, communication via serial lines, and programming embedded processors. An extensive web site supports the book with additional instructional materials. JavaTech demonstrates the ease with which Java can be used to create powerful network applications and distributed computing applications. It will be used as a textbook for programming courses, and by researchers who need to learn Jav

JavaTech is a practical introduction to the Java programming language with an emphasis on the features that benefit technical computing. After presenting the basics of object-oriented programming in Java, it examines introductory topics such as graphical interfaces and thread processes. It goes on to review network programming and develops Web client-server examples for tasks such as monitoring remote devices. The focus then shifts to distributed computing with RMI. Finally, it examines how Java programs can access the local platform and interact with hardware. Topics include combining native code with Java, communication via serial lines, and programming embedded processors. An extensive web site supports the book with additional instructional materials. JavaTech demonstrates the ease with which Java can be used to create powerful network applications and distributed computing applications. It will be used as a textbook for programming courses, and by researchers who need to learn Jav

All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.

All social and policy researchers need to synthesize data into a visual representation. Producing good visualizations combines creativity and technique. This book teaches the techniques and basics to produce a variety of visualizations, allowing readers to communicate data and analyses in a creative and effective way. Visuals for tables, time series, maps, text, and networks are carefully explained and organized, showing how to choose the right plot for the type of data being analysed and displayed. Examples are drawn from public policy, public safety, education, political tweets, and public health. The presentation proceeds step by step, starting from the basics, in the programming languages R and Python so that readers learn the coding skills while simultaneously becoming familiar with the advantages and disadvantages of each visualization. No prior knowledge of either Python or R is required. Code for all the visualizations are available from the book's website.

Many leading experts contribute to this follow-up to An Introduction to Reservoir Simulation using MATLAB/GNU Octave: User Guide for the MATLAB Reservoir Simulation Toolbox (MRST). It introduces more advanced functionality that has been recently added to the open-source MRST software. It is however a self-contained introduction to a variety of modern numerical methods for simulating multiphase flow in porous media, with applications to geothermal energy, chemical enhanced oil recovery (EOR), flow in fractured and unconventional reservoirs, and in the unsaturated zone. The reader will learn how to implement new models and algorithms in a robust, efficient manner. A large number of numerical examples are included, all fully equipped with code and data so that the reader can reproduce the results and use them as a starting point for their own work. Like the original textbook, this book will prove invaluable for researchers, professionals and advanced students using reservoir simulation me

Linear algebra is something all mathematics undergraduates and many other students, in subjects ranging from engineering to economics, have to learn. The fifth edition of this hugely successful textbook retains the quality of earlier editions while at the same time seeing numerous minor improvements and major additions. The latter include: a new chapter on singular values and singular vectors, including ways to analyze a matrix of data; a revised chapter on computing in linear algebra, with professional-level algorithms and code that can be downloaded for a variety of languages; a new section on linear algebra and cryptography; and a new chapter on linear algebra in probability and statistics. A dedicated and active website also offers solutions to exercises as well as new exercises from many different sources (e.g. practice problems, exams, development of textbook examples), plus codes in MATLAB, Julia, and Python.

Complex Analysis with Mathematica offers a way of learning and teaching a subject that lies at the heart of many areas of pure and applied mathematics, physics, engineering and even art. This book offers teachers and students an opportunity to learn about complex numbers in a state-of-the-art computational environment. The innovative approach also offers insights into many areas too often neglected in a student treatment, including complex chaos and mathematical art. Thus readers can also use the book for self-study and for enrichment. The use of Mathematica enables the author to cover several topics that are often absent from a traditional treatment. Students are also led, optionally, into cubic or quartic equations, investigations of symmetric chaos and advanced conformal mapping. A CD is included which contains a live version of the book: in particular all the Mathematica code enables the user to run computer experiments.

This book offers a unique insight into a revolution in software development that allows model specifications to be fully and efficiently translated into code. Using the most widely adopted, industry standard, software modelling language, UML, the reader will learn how to build robust specifications based on OMG's Model Driven Architecture (MDA). From there, the authors describe the steps needed to translate the Executable UML (xUML) models to any platform-specific implementation. The benefits of this approach go well beyond simply reducing or eliminating the coding stage - it also ensures platform independence, avoids obsolescence (programming languages may change, the model doesn't) and allows full verification of the models by executing them in a test and debug xUML environment. This is an excellent reference for anyone embarking on what is surely the future of software development for medium and large scale projects.

Offering a new perspective, this textbook demystifies the operation of electric machines by providing an integrated understanding of electromagnetic fields, electric circuits, numerical analysis, and computer programming. It presents fundamental concepts in a rigorous manner, emphasising underlying physical modelling assumptions and limitations, and provides detailed explanations of how to implement the finite element method to explore these concepts using Python. It includes explanations of the conversion of concepts into algorithms, and algorithms into code, and examples building in complexity, from simple linear-motion electromagnets to rotating machines. Over 100 theoretical and computational end-of-chapter exercises test understanding, with solutions for instructors and downloadable Python code available online. Ideal for graduates and senior undergraduates studying electric machines, electric machine design and control, and power electronic converters and power systems engineerin

Connecting theory with practice, this systematic and rigorous introduction covers the fundamental principles, algorithms and applications of key mathematical models for high-dimensional data analysis. Comprehensive in its approach, it provides unified coverage of many different low-dimensional models and analytical techniques, including sparse and low-rank models, and both convex and non-convex formulations. Readers will learn how to develop efficient and scalable algorithms for solving real-world problems, supported by numerous examples and exercises throughout, and how to use the computational tools learnt in several application contexts. Applications presented include scientific imaging, communication, face recognition, 3D vision, and deep networks for classification. With code available online, this is an ideal textbook for senior and graduate students in computer science, data science, and electrical engineering, as well as for those taking courses on sparsity, low-dimensional str

Thoroughly updated with improved pedagogy, the fifth edition of this classic textbook continues to provide students with a clear and comprehensive introduction the fundamentals of the finite element method. New features include enhanced coverage of introductory topics in the context of simple 1D problems, providing students with a solid base from which to advance to 2D and 3D problems; expanded coverage of more advanced concepts, to reinforce students' understanding; over 30 additional solved problems; and downloadable MATLAB, Python, C, Javascript, Fortran and Excel VBA code packages, providing students with hands-on experience, and preparing them for commercial software. Accompanied by online solutions for instructors, this is the definitive text for senior undergraduate and graduate students studying a first course in the finite element method and finite element analysis, and for professional engineers keen to shore up their understanding of finite element fundamentals.

Using numerous examples with real data, this textbook closely integrates the learning of statistics with the learning of R. It is suitable for introductory-level learners, allows for curriculum flexibility, and includes, as an online resource, R-code script files for all examples and figures included in each chapter, for students to learn from and adapt and use in their future data analytic work. Other unique features created specifically for this textbook include an online R tutorial that introduces readers to data frames and other basic elements of the R architecture, and a CRAN library of datasets and functions that is used throughout the book. Essential topics often overlooked in other introductory texts, such as data management, are covered. The textbook includes online solutions to all end-of-chapter exercises and PowerPoint slides for all chapters as additional resources, and is suitable for those who do not have a strong background in mathematics.

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.

Networks are everywhere: networks of friends, transportation networks and the Web. Neurons in our brains and proteins within our bodies form networks that determine our intelligence and survival. This modern, accessible textbook introduces the basics of network science for a wide range of job sectors from management to marketing, from biology to engineering, and from neuroscience to the social sciences. Students will develop important, practical skills and learn to write code for using networks in their areas of interest - even as they are just learning to program with Python. Extensive sets of tutorials and homework problems provide plenty of hands-on practice and longer programming tutorials online further enhance students' programming skills. This intuitive and direct approach makes the book ideal for a first course, aimed at a wide audience without a strong background in mathematics or computing but with a desire to learn the fundamentals and applications of network science.

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

Master the usage of s-parameters in signal integrity applications and gain full understanding of your simulation and measurement environment with this rigorous and practical guide. Solve specific signal integrity problems including calculation of the s-parameters of a network, linear simulation of circuits, de-embedding, and virtual probing, all with expert guidance. Learn about the interconnectedness of s-parameters, frequency responses, filters, and waveforms. This invaluable resource for signal integrity engineers is supplemented with the open-source software SignalIntegrity, a Python package for scripting solutions to signal integrity problems.

This book provides a self-contained introduction to the simulation of flow and transport in porous media, written by a developer of numerical methods. The reader will learn how to implement reservoir simulation models and computational algorithms in a robust and efficient manner. The book contains a large number of numerical examples, all fully equipped with online code and data, allowing the reader to reproduce results, and use them as a starting point for their own work. All of the examples in the book are based on the MATLAB Reservoir Simulation Toolbox (MRST), an open-source toolbox popular popularity in both academic institutions and the petroleum industry. The book can also be seen as a user guide to the MRST software. It will prove invaluable for researchers, professionals and advanced students using reservoir simulation methods. This title is also available as Open Access on Cambridge Core.

This unique text provides a thorough, yet accessible, grounding in the mathematics, statistics, and programming that students need to master for coursework and research in climate science, meteorology, and oceanography. Assuming only high school mathematics, it presents carefully selected concepts and techniques in linear algebra, statistics, computing, calculus and differential equations within the context of real climate science examples. Computational techniques are integrated to demonstrate how to visualize, analyze, and apply climate data, with R code featured in the book and both R and Python code available online. Exercises are provided at the end of each chapter with selected solutions available to students to aid self-study and further solutions provided online for instructors only. Additional online supplements to aid classroom teaching include datasets, images, and animations. Guidance is provided on how the book can support a variety of courses at different levels, making i

This book explores the fundamentals of computer music and functional programming through the Haskell programming language. Functional programming is typically considered difficult to learn. This introduction in the context of creating music will allow students and professionals with a musical inclination to leverage their experience to help understand concepts that might be intimidating in more traditional computer science settings. Conversely, the book opens the door for programmers to interact with music by using a medium that is familiar to them. Readers will learn how to use the Euterpea library for Haskell (http://www.euterpea.com) to represent and create their own music with code, without the need for other music software. The book explores common paradigms used in algorithmic music composition, such as stochastic generation, musical grammars, self-similarity, and real-time interactive systems. Other topics covered include the basics of signal-based systems in Haskell, sound synt

Gain an intuitive understanding of jitter and phase noise with this authoritative guide. Leading researchers provide expert insights on a wide range of topics, from general theory and the effects of jitter on circuits and systems, to key statistical properties and numerical techniques. Using the tools provided in this book, you will learn how and when jitter and phase noise occur, their relationship with one another, how they can degrade circuit performance, and how to mitigate their effects - all in the context of the most recent research in the field. Examine the impact of jitter in key application areas, including digital circuits and systems, data converters, wirelines, and wireless systems, and learn how to simulate it using the accompanying Matlab code. Supported by additional examples and exercises online, this is a one-stop guide for graduate students and practicing engineers interested in improving the performance of modern electronic circuits and systems.

Optical interferometry is a powerful technique to make images on angular scales hundreds of times smaller than is possible with the largest telescopes. This concise guide provides an introduction to the technique for graduate students and researchers who want to make interferometric observations and acts as a reference for technologists building new instruments. Starting from the principles of interference, the author covers the core concepts of interferometry, showing how the effects of the Earth's atmosphere can be overcome using closure phase, and the complete process of making an observation, from planning to image reconstruction. This rigorous approach emphasizes the use of rules-of-thumb for important parameters such as the signal-to-noise ratios, requirements for sampling the Fourier plane and predicting image quality. The handbook is supported by web resources, including the Python source code used to make many of the graphs, as well as an interferometry simulation framework, a

The European Molecular Biology Open Software Suite (EMBOSS) is a high quality, well documented package of open source software tools for molecular biology. EMBOSS includes extensive and extensible C programming libraries, providing a powerful and robust toolkit for developing new bioinformatics tools from scratch. The EMBOSS Developer's Guide is the official and definitive guide to developing software under EMBOSS. It includes comprehensive reference information and guidelines, including step-by-step instructions and real-world code examples: • Learn how to write fully-featured tools guided by the people who developed EMBOSS • Step-by-step guide to writing EMBOSS applications, illustrated with functional, deployed code • ACD file development - learn how to customise existing tools without coding, or design and write entirely new application interfaces • EMBOSS API programming guidelines - quickly master application development • Wrapping and porting applications under EMBOSS - learn ho

2D infrared (IR) spectroscopy is a cutting-edge technique, with applications in subjects as diverse as the energy sciences, biophysics and physical chemistry. This book introduces the essential concepts of 2D IR spectroscopy step-by-step to build an intuitive and in-depth understanding of the method. This unique book introduces the mathematical formalism in a simple manner, examines the design considerations for implementing the methods in the laboratory, and contains working computer code to simulate 2D IR spectra and exercises to illustrate involved concepts. Readers will learn how to accurately interpret 2D IR spectra, design their own spectrometer and invent their own pulse sequences. It is an excellent starting point for graduate students and researchers new to this exciting field. Computer codes and answers to the exercises can be downloaded from the authors' website, available at www.cambridge.org/9781107000056.

A selection from Arnold's writing on education, other than Culture and Anarchy. All the pieces stem from his work as Inspector of Schools: they illustrate his concern both with the principles that must be established as a basis for the education of an industrial democracy and his practical concern with the day-to-day running of schools. 'Democracy' was first published as the introduction to The Popular Education of France. It faces the fundamental political problems and outlines the general objectives of a state educational system. 'A French Eton' was the result of the same examination of French education to see what the British could learn from it; here he considers private education for the middle-classes. 'The twice-revised code' criticises the national Revised Code of 1862: a system founded on gross utilitarianism. Extracts from Arnold's reports as an inspector show the man of principle at work in particular circumstances and relating what he sees to what he would wish to see. The

Do you need to know how to write systems, services, and applications using the TinyOS operating system? Learn how to write nesC code and efficient applications with this indispensable guide to TinyOS programming. Detailed examples show you how to write TinyOS code in full, from basic applications right up to new low-level systems and high performance applications. Two leading figures in the development of TinyOS also explain the reasons behind many of the design decisions made and, for the first time, how nesC relates to and differs from other C dialects. Handy features such as a library of software design patterns, programming hints and tips, end-of-chapter exercises, and an appendix summarizing the basic application-level TinyOS APIs make this the ultimate guide to TinyOS for embedded systems programmers, developers, designers, and graduate students.

The sheer complexity of computer systems has meant that automated reasoning, i.e. the ability of computers to perform logical inference, has become a vital component of program construction and of programming language design. This book meets the demand for a self-contained and broad-based account of the concepts, the machinery and the use of automated reasoning. The mathematical logic foundations are described in conjunction with practical application, all with the minimum of prerequisites. The approach is constructive, concrete and algorithmic: a key feature is that methods are described with reference to actual implementations (for which code is supplied) that readers can use, modify and experiment with. This book is ideally suited for those seeking a one-stop source for the general area of automated reasoning. It can be used as a reference, or as a place to learn the fundamentals, either in conjunction with advanced courses or for self study.

Based around a theme of the construction of a game engine, this textbook is for final year undergraduate and graduate students, emphasising formal methods in writing robust code quickly. This book takes an unusual, engineering-inspired approach to illuminate the creation and verification of large software systems. Where other textbooks discuss business practices through generic project management techniques or detailed rigid logic systems, this book examines the interaction between code in a physical machine and the logic applied in creating the software. These elements create an informal and rigorous study of logic, algebra, and geometry through software. Assuming prior experience with C, C++, or Java programming languages, chapters introduce UML, OCL, and Z from scratch. Extensive worked examples motivate readers to learn the languages through the technical side of software science.