Computer science Books

Book SynopsisThis is the seventh edition of the training manual for the Data Modeling Master Class that Steve Hoberman teaches onsite and through public classes. This text can be purchased prior to attending the Master Class, the latest course schedule and detailed description can be found on Steve Hoberman''s website, stevehoberman.com. The Master Class is a complete data modeling course, containing three days of practical techniques for producing conceptual, logical, and physical relational and dimensional and NoSQL data models. After learning the styles and steps in capturing and modelling requirements, you will apply a best practices approach to building and validating data models through the Data Model Scorecard. You will know not just how to build a data model, but how to build a data model well. Two case studies and many exercises reinforce the material and will enable you to apply these techniques in your current projects. Top 10 Objectives: 1. Explain data modeling components and identify them on your projects by following a question-driven approach; 2. Demonstrate reading a data model of any size and complexity with the same confidence as reading a book; 3. Validate any data model with key settings (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard; 4. Apply requirements elicitation techniques including interviewing, artefact analysis, prototyping, and job shadowing; 5. Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions; 6.Practice finding structural soundness issues and standards violations; 7. Recognise when to use abstraction and where patterns and industry data models can give us a great head start; 8. Use a series of templates for capturing and validating requirements, and for data profiling; 9. Evaluate definitions for clarity, completeness, and correctness ; 10. Leverage the Data Vault and enterprise data model for a successful enterprise architecture.

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Book SynopsisIn the academic field, engineers, scientists, educators, and students are faced with a persistent challenge: the gap between theoretical knowledge and practical implementation in solving real-world engineering problems. The scarcity of focused resources tailored to mastering MATLAB® and its specialized solvers for Ordinary Differential Equations (ODEs) and One-Dimensional Partial Differential Equations (1D PDEs) has left many individuals struggling to bridge this educational chasm. The disconnect between the theory learned in the classroom and the ability to effectively address engineering challenges in the real world has become a significant hurdle. The definitive solution to the academic conundrum of this lack of a focused resource is the book, ODE, BVP, and 1D PDE Solvers for Scientific and Engineering Problems with MATLAB Basics, which draws on years of teaching experience. This groundbreaking book provides a structured and holistic learning path designed to empower both novice learners and seasoned professionals. It takes readers on a comprehensive journey, commencing with the fundamentals of MATLAB® software and culminating in the mastery of its application in solving ODEs and 1D PDEs for a broad range of engineering problems. The book not only fills a void in academic literature by focusing specifically on solving engineering problems using MATLAB® solvers but also imparts a deep understanding of the software and its practical applications, comprehensive chapters encompass computational tools, numerical methods, and the hands-on use of ODE and 1D PDE solvers. This ensures that learners effectively bridge the gap between theory and real-world implementation. The real-world application examples drawn from various technical fields further illuminate the concepts, making ODE, BVP, and 1D PDE Solvers for Scientific and Engineering Problems with MATLAB Basics an indispensable resource for scholars aspiring to excel in natural sciences, life sciences, and modern industry. It empowers individuals to turn theoretical knowledge into practical solutions, equipping them to thrive in the demanding landscape of science and technology.

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Book SynopsisNow in its third edition, this dictionary contains 3,400 key terms, words, and phrases used in the day-to-day practice of project management. Along with traditional project management terms, it includes broader business terms to help seasoned managers and their successors navigate more easily the ubiquitous language of project-speak. Within the book, readers find the meaning of hundreds of technical terms and acronyms, with each entry providing important insight into some aspect of project management.

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Book SynopsisThis book will provide the business or IT professional with a practical working knowledge of data modelling concepts and best practices, and how to apply these principles with CA ERwin Data Modeler r8. You will build many CA ERwin data models along the way, mastering first the fundamentals and later in the book the more advanced features of CA ERwin Data Modeler. The book combines real-world experience and best practices with down to earth advice, humour, and even cartoons to help you master the following ten objectives: Understand the basics of data modelling and relational theory, and how to apply these skills using CA ERwin Data Modeler; Read a data model of any size and complexity with the same confidence as reading a book; Understand the difference between conceptual, logical, and physical models, and how to effectively build these models using CA ERwin''s Data Modelers Design Layer Architecture; Apply techniques to turn a logical data model into an efficient physical design and vice-versa through forward and reverse engineering, for both top down and bottom-up design; Learn how to create reusable domains, naming standards, UDPs, and model templates in CA ERwin Data Modeler to reduce modelling time, improve data quality, and increase enterprise consistency; Share data model information with various audiences using model formatting and layout techniques, reporting, and metadata exchange; Use the new workspace customisation features in CA ERwin Data Modeler r8 to create a workflow suited to your own individual needs; Leverage the new Bulk Editing features in CA ERwin Data Modeler r8 for mass metadata updates, as well as import/export with Microsoft Excel; Compare and merge model changes using CA ERwin Data Modelers Complete Compare features; Optimise the organisation and layout of your data models through the use of Subject Areas, Diagrams, Display Themes, and more. Section I provides an overview of data modelling: what it is, and why it is needed. The basic features of CA ERwin Data Modeler are introduced with a simple, easy-to-follow example. Section II introduces the basic building blocks of a data model, including entities, relationships, keys, and more. How-to examples using CA ERwin Data Modeler are provided for each of these building blocks, as well as ''real world'' scenarios for context. Section III covers the creation of reusable standards, and their importance in the organisation. From standard data modelling constructs such as domains to CA ERwin-specific features such as UDPs, this section covers step-by-step examples of how to create these standards in CA ERwin Data Modeling, from creation, to template building, to sharing standards with end users through reporting and queries. Section IV discusses conceptual, logical, and physical data models, and provides a comprehensive case study using CA ERwin Data Modeler to show the interrelationships between these models using CA ERwin''s Design Layer Architecture. Real world examples are provided from requirements gathering, to working with business sponsors, to the hands-on nitty-gritty details of building conceptual, logical, and physical data models with CA ERwin Data Modeler r8.

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Book SynopsisIs fast development the enemy of good development? Not necessarily. Agile development requires that databases are designed and built quickly enough to meet fast-based delivery schedules - but in a way that also delivers maximum business value and reuse. How can these requirements both be satisfied? This book, suitable for practitioners at all levels, will explain how to design and build enterprise-quality high-value databases within the constraints of an Agile project. Starting with an overview of the business case for good data management practices, the book defines the various stakeholder groups involved in the software development process, explains the economics of software development (including time to market versus time to money), and describes an approach to Agile database development based on the five PRISM principles. This book explains how to work with application developers and other stakeholders, examines critical issues in Agile Development and Data Management, and describes how developers and data professionals can work together to make Agile projects successful while delivering maximum value data to the enterprise. The book will serve as an excellent reference for application developers, data managers, DBAs, project managers, Scrum Masters and IT managers looking to get more value from their development efforts.

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Book SynopsisHere you will learn how to develop an attractive, easily readable, conceptual, business-oriented entity/relationship model, using a variation on the UML Class Model notation. This book has two audiences: Data modellers (both analysts and database designers) who are convinced that UML has nothing to do with them; and UML experts who don''t realise that architectural data modelling really is different from object modelling (and that the differences are important). David Hay''s objective is to finally bring these two groups together in peace. Here all modellers will receive guidance on how to produce a high quality (that is, readable) entity/relationship model to describe the data architecture of an organisation. The notation involved happens to be the one for class models in the Unified Modelling Language, even though UML was originally developed to support object-oriented design. Designers have a different view of the world from those who develop business-oriented conceptual data models, which means that to use UML for architectural modelling requires some adjustments. These adjustments are described in this book. David Hay is the author of Enterprise Model Patterns: Describing the World, a comprehensive model of a generic enterprise. The diagrams were at various levels of abstraction, and they were all rendered in the slightly modified version of UML Class Diagrams presented here. This book is a handbook to describe how to build models such as these. By way of background, an appendix provides a history of the two groups, revealing the sources of their different attitudes towards the system development process.

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Book SynopsisExploring how to conduct information system (IS) research using various approaches that are grounded in proven scientific methods, this book is primarily for consumers of IS research and for individuals involved in any type of academic research across various disciplines. It has been designed to enable IS researchers and consumers to make more professional and scientific decisions about evaluating, locating, reading, and critiquing the findings of scientific research. It has been shown that many IS research community lacks its own scientific theories and models. It has also been shown that workable research problem, purpose, method, design, instruments, samples, population, data collection, and processing used in other fields are also applicable to research in IS. Thus, the stages of scientific research from problem conception to analysis and interpretation will be discussed in the context of IS research.

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Book SynopsisThis book covers the fundamentals of digital signal processing (DSP) in a concise format, accessible to anyone with a technical background, enabling the reader for further DSP training, research, and development. The authors explore many subjects, including discrete time (digital) signals and systems, with emphasis on linear shift invariant (LSI) systems; Fourier and the z transforms; signal sampling and analog-to-digital (A/D) conversion. The book ends with examples of DSP techniques applications to practical problems from several areas.

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