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It exhibits stationarity and multi-model patterns of naturally occurring time series, a classical data science pattern, that has a causative relationship with historical events, world macroeconomics, agriculture, and other worldly events.","brand":"Nova","offers":[{"title":"Default Title","offer_id":65419938824541,"sku":null,"price":152.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9798891132535.jpg?v=1789462095"},{"product_id":"9781536110715","title":"Data Structures \u0026 Transmission","description":"The recent advances in computer networks and the widespread use of the Internet, together with other developments in telecommunications technology have made it possible to send messages and exchange information around the whole world. The high variety and the large amount of data exchanged across communication networks have increased over the last few years. This means the threat of interception during data transmission has become a major concern. Important research aimed at designing algorithms to help prevent interception and enhance data security is currently of primary relevance. This advanced technology requires new and efficient encryption methodologies. These algorithms can assure security for fast evolving communication and storage applications that must be secured against intrusion threats, which unfortunately are increasing in sophistication and frequency. This book analyses new research on the technology and applications of data structures and data transmission.","brand":"Nova Science Publishers, Inc","offers":[{"title":"Default Title","offer_id":65422048362845,"sku":null,"price":73.49,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781536110715.jpg?v=1789491449"},{"product_id":"9781536128277","title":"Data Storage","description":"Data Storage: Systems, Management and Security Issues begins with a chapter comparing digital or electronic storage systems, such as magnetic, optical, and flash, with biological data storage systems, like DNA and human brain memory. In the main part of the chapter, the following quantitative storage traits are discussed: data organisation, functionality, data density, capacity, power consumption, redundancy, integrity, access time, data transfer rate. Afterwards, various facets of data warehouses as well as the necessity for security measures are reviewed. Because the significance of security tools is greater than ever before, the pertinent strategies and economics are discussed. The final chapter supplements this by discussing media and storage systems reliability and confidentiality in order to make a greater claim about storage security. Confidentiality, integrity and availability are three aspects of security identified as ones that should be preserved during data transmission, processing and storage.","brand":"Nova Science Publishers, Inc","offers":[{"title":"Default Title","offer_id":65422514946397,"sku":null,"price":73.49,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781536128277.jpg?v=1789301644"},{"product_id":"9781536179255","title":"Medical Knowledge Extraction from Big Data","description":"Data mining refers to the activity of going through big data sets to look for relevant information. As human health care data are the most difficult of all data to collect and their primary direction is the treatment of patients, and secondarily dealing with research, almost the only vindication for collecting medical data is to benefit the disease. All data miners should take into account that Medical Knowledge Extraction is internally connected with the Evidence-Based Medical approach because it uses data for already treated or not patients and there are times that opposites to Guideline Based medical practice. Additonally all researchers should be aware when are dealing with medical databases they may face the possibility that their work will never be accepted or even used from health care professionals if all these obligations will not be correctly addressed from the early beginning. In the present book, one can find after the three introductory chapters, a number of successfully evaluated applications that have been developed after mining approaches in Big or smaller amount (according to the application) of medical Data in different fields of every day clinical practice from teams of experts. The challenging adventure of Medical Knowledge Extraction can be followed by ambitious researchers finally resulting in a successful decision support system, that some times is so novel that it will provide new directions for basic or clinical research further that the existed. At least this procedure will save the experience of the best doctors on duty and will help young residents to be better and better.","brand":"Nova Science Publishers, Inc","offers":[{"title":"Default Title","offer_id":65424337863005,"sku":null,"price":106.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781536179255.jpg?v=1789331400"},{"product_id":"9781536193336","title":"A Closer Look at Big Data Analytics","description":"Big Data Analytics is a field that dissects, efficiently extricates data from, or in any case manages informational indexes that are excessively huge or complex to be managed by customary information preparing application programming. Information with numerous cases (lines) offers more noteworthy factual force, while information with higher multifaceted nature may prompt a higher bogus disclosure rate. Enormous information challenges incorporate catching information, information stockpiling, information investigation, search, sharing, move, representation, and questioning, refreshing, data security and data source. Large information was initially connected with three key ideas: volume, variety and velocity. Consequently, huge information regularly incorporates information with sizes that surpass the limit of conventional programming to measure inside a satisfactory time and worth. Current utilisation of the term enormous information will in general allude to the utilisation of predictive analytics, user behaviour analytics, or certain other progressed information investigation techniques that concentrate an incentive from information, and sometimes to a specific size of informational index. There is little uncertainty that the amounts of information now accessible are undoubtedly enormous, however that is not the most important quality of this new information biological system. Investigation of informational indexes can discover new relationships to spot business patterns or models. Researchers, business-persons, clinical specialists, promoting and governments consistently meet challenges with huge informational collections in territories including Internet look, fintech, metropolitan informatics, and business informatics. Researchers experience constraints in e-Science work, including meteorology, genomics, connectomics, complex material science reproductions, science and ecological exploration. The main objective of this book is to write about issues, challenges, opportunities, and solutions in novel research projects about big data in various domains. The topics of interest include, but are not limited to: efficient storage, management and sharing large scale of data; novel approaches for analysing data using big data technologies; implementation of high performance and\/or scalable and\/or real-time computation algorithms for analysing big data; usage of various data sources like historical data, social networking media, machine data and crowd-sourcing data; using machine learning, visual analytics, data mining, spatio-temporal data analysis and statistical inference in different domains (with large scale datasets); Legal and ethical issues and solutions for using, sharing and publishing large datasets; and the results of data analytics, security and privacy issues.","brand":"Nova Science Publishers, Inc","offers":[{"title":"Default Title","offer_id":65424885219677,"sku":null,"price":152.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781536193336.jpg?v=1789337374"},{"product_id":"9781590338025","title":"Technology Supporting Business Solutions","description":"The explosive growth of the Internet and the web have created an ever-growing demand for web-based information systems, and ever-growing challenges for Information Systems Engineering. Some of them include the emerging web services technology, database technologies and application integration, as well as data analysis and knowledge discovery. This book is a showcase of recent, significant advances in web-based information systems as well as data integration and analysis. It provides an overview of various technologies used for building innovative information systems applied to real business solutions. It includes eight chapters that are divided into five parts, namely: web services, database technologies, data and application integration, data analysis and knowledge discovery, and recommended bibliography. The material presented in these chapters will help the reader have an overall idea of the research that is being carried out in universities and companies to develop today's innovative business solutions.","brand":"Nova Science Publishers, Inc (US)","offers":[{"title":"Default Title","offer_id":65425796890973,"sku":null,"price":63.74,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781590338025.jpg?v=1789354936"},{"product_id":"9781604563924","title":"Cognitive Sciences Research Progress","description":"This book presents new research on cognitive science which is most simply defined as the scientific study either of mind or of intelligence. It is an interdisciplinary study drawing from relevant fields including psychology, philosophy, neuroscience, linguistics, anthropology, computer science, biology, and physics. There are several approaches to the study of cognitive science. These approaches may be classified broadly as symbolic, connectionist, and dynamic systems. Symbolic holds that cognition can be explained using operations on symbols, by means of explicit computational theories and models of mental (but not brain) processes analogous to the workings of a digital computer. Connectionist (subsymbolic) holds that cognition can only be modelled and explained by using artificial neural networks on the level of physical brain properties. Hybrid systems hold that cognition is best modelled using both connectionist and symbolic models, and possibly other computational techniques. Dynamic Systems hold that cognition can be explained by means of a continuous dynamical system in which all the elements are interrelated, like the Watt Governor. The essential questions of cognitive science seem to be: What is intelligence? and How is it possible to model it computationally?","brand":"Nova Science Publishers, Inc (US)","offers":[{"title":"Default Title","offer_id":65426789859677,"sku":null,"price":63.74,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781604563924.jpg?v=1789376072"},{"product_id":"9781611228625","title":"Data Management in the Semantic Web","description":"Effective and efficient data management is vital to today's applications. Traditional data management mainly focuses on information procession involving data within a single organisation. Data are unified according to the same schema and there exists an agreement between the interacting units as to the correct mapping between these concepts. Nowadays, data management systems have to handle a variety of data sources, from proprietary ones to data publicly available. Investigating the relevance between data for information sharing has become an essential challenge for data management. This book explores the technology and application of semantic data management by bringing together various research studies in different subfields.","brand":"Nova Science Publishers, Inc (US)","offers":[{"title":"Default Title","offer_id":65427774734685,"sku":null,"price":155.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781611228625.jpg?v=1789399706"},{"product_id":"9781624175824","title":"Data Security, Data Mining \u0026 Data Management","description":"In this book, the authors discuss the new technologies and challenges of data security, data mining and data management. Topics include clustering algorithms in radiobiology and DNA damage quantification; data mining for searching genomic information; data management in the semantic web; and how fragile data security can be when the system architecture, authorization and validation is founded on a personal identification number (PIN).","brand":"Nova Science Publishers, Inc (US)","offers":[{"title":"Default Title","offer_id":65430572007773,"sku":null,"price":92.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781624175824.jpg?v=1789232927"},{"product_id":"9781633214170","title":"Electronic Health Records","description":"Electronic health records (EHRs) play an important role in optimizing the health care provided to active duty servicemembers and veterans. When a servicemember leaves military service by way of discharge, separation, or retirement he or she may become eligible for VA benefits and services including VA health care. Transitioning their health care information from one large health care system (Department of Defense; DOD) to the other (Department of Veterans Affairs; VA) involves coordination of data and information between DOD and VA. Longstanding concern that this exchange be effective has been expressed in many quarters, including Congress. The purpose of this book is to provide a background on the long-standing efforts in sharing health information between DOD and VA. The book also describes changes to the integrated electronic health record system and evaluates the departments' current plans; and determines whether the departments are effectively collaborating on management of the program.","brand":"Nova Science Publishers, Inc (US)","offers":[{"title":"Default Title","offer_id":65433138102621,"sku":null,"price":63.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781633214170.jpg?v=1789252532"},{"product_id":"9781633219274","title":"Air Transport Safety","description":"This book is composed of thirteen chapters. Chapter 1 provides a short overview of the Air Transport System, from both the micro- and macro-level structure. It covers the descriptions and definitions of the main system elements: air carriers, airports and air navigation service providers, as well as personnel, equipment, procedures and the environment. Chapter 2 introduces the reader to the basic concepts in air transport risk and safety. This chapter covers the definitions of safety, hazards, risk, incidents and accidents. It further explains safety criteria, safety barriers, safety regulatory requirements, and finally it compares Safety I and Safety II concepts. Chapter 3 covers the field of Air Transport Safety Metrics and Records. Here safety metrics, accident statistics and safety records are explained and illustrated. Finally, a safety comparison of transport modes is made. Chapter 4 presents Sources of Accident\/Incident Information. Explained here is how safety-related events (incidents and accidents) are investigated and what the phases of the investigation process are, as well as how safety information is collected. Chapter 5 describes the main safety issues in contemporary air transport. They are grouped into three sets: airport, air navigation service providers and air carriers' safety issues.","brand":"Nova Science Publishers, Inc (US)","offers":[{"title":"Default Title","offer_id":65433166938461,"sku":null,"price":159.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781633219274.jpg?v=1789254751"},{"product_id":"9781634620093","title":"Versión en español de la Guía DAMA de los fundamentos para la gestión de datos (DAMA-DMBOK)","description":"Text in Spanish.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433171558749,"sku":null,"price":38.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634620093.jpg?v=1789255163"},{"product_id":"9781634620123","title":"Data Resource Understanding","description":"Are you struggling to understand the data you need to support your business activities? Are you frustrated over data that do not answer your questions or provide the wrong answers to your questions? Are you worried that your organisation is not adequately supporting its citizens or customers? Are you concerned over civil or criminal liability for the quality and use of your data? If the answer to any of these questions is Yes, they you need to read \"Data Resource Understanding\" to help you and everyone in your organisation thoroughly understand the data they need to support the business activities. Most public and private sector organisations have no formal method for thoroughly understanding the data needed to support their business activities. They seldom have a method that begins with the organisation's perception of the business world and continues through a formal Data Resource Development Cycle to produce a high quality, thoroughly understood data resource that fully supports the organisation's current and future business information demand. Data Resource Data provided the complete detailed data resource model for understanding and managing data as a critical resource of the organisation. Data Resource Understanding is the companion book to Data Resource Data. It provides a detailed explanation of how to thoroughly understand an organisation's data resource and to document that understanding with Data Resource Data. Together they provide an organisation with the foundation for properly managing their data as a critical resource. Like in \"Data Resource Simplexity\", Michael Brackett draws on over half a century of data management experience, in a wide variety of different public and private sector organisations, to understand and document an organisation's data resource. He leverages theories, concepts, principles, and techniques from many different and varied disciplines, such as human dynamics, mathematics, physics, chemistry, philosophy, and biology, and applies them to the process of formally documenting an organisation's data resource.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433171591517,"sku":null,"price":30.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634620123.jpg?v=1789255173"},{"product_id":"9781634620741","title":"Object-Role Modeling Fundamentals","description":"Object-Role Modeling (ORM) is a fact-based approach to data modelling that expresses the information requirements of any business domain simply in terms of objects that play roles in relationships. All facts of interest are treated as instances of attribute-free structures known as fact types, where the relationship may be unary (eg: Person smokes), binary (eg: Person was born on Date), ternary (eg: Customer bought Product on Date), or longer. Fact types facilitate natural expression, are easy to populate with examples for validation purposes, and have greater semantic stability than attribute-based structures such as those used in Entity Relationship Modeling (ER) or the Unified Modeling Language (UML). All relevant facts, constraints and derivation rules are expressed in controlled natural language sentences that are intelligible to users in the business domain being modeled. This allows ORM data models to be validated by business domain experts who are unfamiliar with ORM's graphical notation. For the data modeler, ORM's graphical notation covers a much wider range of constraints than can be expressed in industrial ER or UML class diagrams, and thus allows rich visualisation of the underlying semantics. Suitable for both novices and experienced practitioners, this book covers the fundamentals of the ORM approach. Written in easy-to-understand language, it shows how to design an ORM model, illustrating each step with simple examples. Each chapter ends with a practical lab that discusses how to use the freeware NORMA tool to enter ORM models and use it to automatically generate verbalisations of the model and map it to a relational database.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433171624285,"sku":null,"price":30.74,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634620741.jpg?v=1789255183"},{"product_id":"9781634620826","title":"Data Model Scorecard","description":"Data models are the main medium used to communicate data requirements from business to IT, and within IT from analysts, modelers, and architects, to database designers and developers. Therefore it's essential to get the data model right. But how do you determine right? That's where the Data Model Scorecard® comes in. The Data Model Scorecard is a data model quality scoring tool containing ten categories aimed at improving the quality of your organization's data models. Many of my consulting assignments are dedicated to applying the Data Model Scorecard to my client's data models  I will show you how to apply the Scorecard in this book. This book, written for people who build, use, or review data models, contains the Data Model Scorecard template and an explanation along with many examples of each of the ten Scorecard categories. There are three sections: In Section I, Data Modeling and the Need for Validation, receive a short data modeling primer in Chapter 1, understand why it is important to get the data model right in Chapter 2, and learn about the Data Model Scorecard in Chapter 3. In Section II, Data Model Scorecard Categories, we will explain each of the ten categories of the Data Model Scorecard. There are ten chapters in this section, each chapter dedicated to a specific Scorecard category: Chapter 4: Correctness. Chapter 5: Completeness. Chapter 6: Scheme. Chapter 7: Structure. Chapter 8: Abstraction. Chapter 9: Standards. Chapter 10: Readability. Chapter 11: Definitions. Chapter 12: Consistency. Chapter 13: Data. In Section III, Validating Data Models, we will prepare for the model review (Chapter 14), cover tips to help during the model review (Chapter 15), and then review a data model based upon an actual project (Chapter 16).","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433173262685,"sku":null,"price":38.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634620826.jpg?v=1789255202"},{"product_id":"9781634620864","title":"Fact Oriented Modeling with FCO-IM","description":"This book offers a complete basic course in Fully Communication Oriented Information Modeling (FCO-IM), a Fact Oriented Modeling (FOM) data modeling technique. The book is suitable for self-study by beginner FCO-IM modelers, whether or not experienced in other modeling techniques. An elaborate case study is used as illustration throughout the book. The book also illustrates how data models in other techniques can be derived from an elementary FCO-IM model. The context of fact oriented modeling is given as well, and perspectives on information modeling indicate related areas of application and further reading. Fact Oriented Modeling methods (like FCO-IM) have three major advantages over other data modeling techniques: FCO-IM captures business semantics. The meaning of facts is captured by incorporating into the model expressions of concrete facts in clear sentences, which are understood by both domain experts and information modelers. FCO-IM includes a detailed working procedure that tells you exactly how to make a data model. Many techniques are clear about what is to be modeled, but few offer a detailed set of guidelines and checks that tell you how to draw up, check and validate your model. FCO-IM focuses on elementary facts, avoiding premature clustering of facts (in entities) but also avoiding considering only incomplete fragments of facts (attributes). From an elementary model, data models in other techniques can be automatically derived (ERM, UML, Data Vault, Star Schema, and Relational and NoSQL databases).","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433173328221,"sku":null,"price":46.49,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634620864.jpg?v=1789255212"},{"product_id":"9781634620901","title":"Data Modeling Master Class Training Manual","description":"This is the 6th 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 modeling 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: Explain data modeling components and identify them on your projects by following a question-driven approach; Demonstrate reading a data model of any size and complexity with the same confidence as reading a book; Validate any data model with key \"settings\" (scope, abstraction, timeframe, function, and format) as well as through the Data Model Scorecard®; Apply requirements elicitation techniques including interviewing, artifact analysis, prototyping, and job shadowing; Build relational and dimensional conceptual and logical data models, and know the tradeoffs on the physical side for both RDBMS and NoSQL solutions; Practice finding structural soundness issues and standards violations; Recognize when to use abstraction and where patterns and industry data models can give us a great head start; Use a series of templates for capturing and validating requirements, and for data profiling; Evaluate definitions for clarity, completeness, and correctness; Leverage the Data Vault and enterprise data model for a successful enterprise architecture.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174344029,"sku":null,"price":149.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634620901.jpg?v=1789255222"},{"product_id":"9781634620925","title":"Data Modeling Made Simple with Embarcadero ER\/Studio Data Architect","description":"Build a working knowledge of data modeling concepts and best practices, along with how to apply these principles with ER\/Studio. This second edition includes numerous updates and new sections including an overview of ER\/Studio's support for agile development, as well as a description of some of ER\/Studio's newer features for NoSQL, such as MongoDB's containment structure. You will build many ER\/Studio data models along the way, applying best practices to master these ten objectives: 1. Know why a data model is needed and which ER\/Studio models are the most appropriate for each situation. 2. Understand each component on the data model and how to represent and create them in ER\/Studio. 3. Know how to leverage ER\/Studio's latest features including those assisting agile teams and forward and reverse engineering of NoSQL databases. 4. Know how to apply all the foundational features of ER\/Studio. 5. Be able to build relational and dimensional conceptual, logical, and physical data models in ER\/Studio. 6. Be able to apply techniques such as indexing, transforms, and forward engineering to turn a logical data model into an efficient physical design. 7. Improve data model quality and impact analysis results by leveraging ER\/Studio's lineage functionality and compare\/merge utility. 8. Be able to apply ER\/Studio's data dictionary features 9. Learn ways of sharing the data model through reporting and through exporting the model in a variety of formats. 10.Leverage ER\/Studio's naming functionality to improve naming consistency, including the new Automatic Naming Translation feature. This book contains four sections: Section I introduces data modeling and the ER\/Studio landscape. Learn why data modeling is so critical to software development and even more importantly, why data modeling is so critical to understanding the business. You will learn about the newest features in ER\/Studio (including features on big data and agile), and the ER\/Studio environment. By the end of this section, you will have created and saved your first data model in ER\/Studio and be ready to start modeling in Section II! Section II explains all of the symbols and text on a data model, including entities, attributes, relationships, domains, and keys. By the time you finish this section, you will be able to read' a data model of any size or complexity, and create a complete data model in ER\/Studio. Section III explores the three different levels of models: conceptual, logical, and physical. A conceptual data model (CDM) represents a business need within a defined scope. The logical data model (LDM) represents a detailed business solution, capturing the business requirements without complicating the model with implementation concerns such as software and hardware. The physical data model (PDM) represents a detailed technical solution. The PDM is the logical data model compromised often to improve performance or usability. The PDM makes up for deficiencies in our technology. By the end of this section you will be able to create conceptual, logical, and physical data models in ER\/Studio. Section IV discusses additional features of ER\/Studio. These features include data dictionary, data lineage, automating tasks, repository and portal, exporting and reporting, naming standards, and compare and merge functionality.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174376797,"sku":null,"price":46.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634620925.jpg?v=1789255232"},{"product_id":"9781634620963","title":"Machine Learning and Data Science","description":"A practitioner's tools have a direct impact on the success of his or her work. This book will provide the data scientist with the tools and techniques required to excel with statistical learning methods in the areas of data access, data munging, exploratory data analysis, supervised machine learning, unsupervised machine learning and model evaluation. Machine learning and data science are large disciplines, requiring years of study in order to gain proficiency. This book can be viewed as a set of essential tools we need for a long-term career in the data science field  recommendations are provided for further study in order to build advanced skills in tackling important data problem domains. The R statistical environment was chosen for use in this book. R is a growing phenomenon worldwide, with many data scientists using it exclusively for their project work. All of the code examples for the book are written in R. In addition, many popular R packages and data sets will be used.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174409565,"sku":null,"price":38.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634620963.jpg?v=1789255242"},{"product_id":"9781634621007","title":"Data Resource Guide","description":"Are you struggling to find the data that you need to support your business activities?  Are you concerned that people may be using the wrong data for their business activities?  Are you having difficulty understanding the data that you do find in your data resource?  Are you frustrated over documenting that understanding in a manner that is readily accessible to anyone in the organisation?  If the answer to any of these questions is Yes, then you need to read \"Data Resource Guide\" to help identify, understand, access, and use the appropriate data. Most public and private sector organisations today have no formal, single location for the complete documentation of their data resource that is readily available to everyone in the organisation.  Many organisations do not even have a concept of how to design, develop, or manage a single repository containing an understanding all the data available to the organisation.  Yet they are staking their business on those data. \"Data Resource Data\" provided the complete data resource model for an organisation's Data Resource Data.  Data Resource Understanding provided a detailed description of how to thoroughly understand an organisation's data resource through those Data Resource Data.  Now, \"Data Resource Guide\" provides the detailed specifications for developing a simple, inexpensive, and effective way to document the data resource understanding and make that understanding readily available to anyone in the organisation. Michael Brackett draws on over half a century of data management experience to complete two trilogies for formally managing an organisation's data as a critical resource.  The Data Architecture Trilogy describes the development of a single organisation wide data architecture for an organization.  The Data Understanding Trilogy describes the acquisition and documentation of understanding about all the data at an organisation's disposal.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174442333,"sku":null,"price":30.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634621007.jpg?v=1789255251"},{"product_id":"9781634621045","title":"Object-Role Modeling Workbook","description":"Object-Role Modeling (ORM) is a fact-based approach to data modelling that expresses the information requirements of any business domain simply in terms of objects that play roles in relationships. All facts of interest are treated as instances of attribute-free structures known as fact types, where the relationship may be unary (eg: Person smokes), binary (eg: Person was born on Date), ternary (eg: Customer bought Product on Date), or longer. Fact types facilitate natural expression, are easy to populate with examples for validation purposes, and have greater semantic stability than attribute-based structures such as those used in Entity Relationship Modeling (ER) or the Unified Modeling Language (UML). All relevant facts, constraints and derivation rules are expressed in controlled natural language sentences that are intelligible to users in the business domain being modelled. This allows ORM data models to be validated by business domain experts who are unfamiliar with ORM's graphical notation. For the data modeller, ORM's graphical notation covers a much wider range of constraints than can be expressed in industrial ER or UML class diagrams, and thus allows rich visualisation of the underlying semantics. Written as a sequel to the author's previous book \"Object-Role Modeling Fundamentals\", this book briefly reviews the fundamentals of ORM, and then discusses additional topics such as model reports generation, vocabulary glossaries, relational mapping options, annotated relational schemas, schema optimisation, and data modelling patterns. Written in easy-to-understand language, it illustrates each topic with simple examples, and explains how to use the freeware NORMA tool to implement the ideas discussed. The book also includes many practical exercises to promote expertise in the techniques covered, with answers provided to all the exercise questions.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174475101,"sku":null,"price":30.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634621045.jpg?v=1789255261"},{"product_id":"9781634621090","title":"NoSQL \u0026 SQL Data Modeling","description":"How do we design for data when traditional design techniques cannot extend to new database technologies? In this era of big data and the Internet of Things, it is essential that we have the tools we need to understand the data coming to us faster than ever before, and to design databases and data processing systems that can adapt easily to ever-changing data schemas and ever-changing business requirements. There must be no intellectual disconnect between data and the software that manages it. It must be possible to extract meaning and knowledge from data to drive artificial intelligence applications. Novel NoSQL data organization techniques must be used side-by-side with traditional SQL databases. Are existing data modeling techniques ready for all of this? The Concept and Object Modeling Notation (COMN) is able to cover the full spectrum of analysis and design. A single COMN model can represent the objects and concepts in the problem space, logical data design, and concrete NoSQL and SQL document, key-value, columnar, and relational database implementations. COMN models enable an unprecedented level of traceability of requirements to implementation. COMN models can also represent the static structure of software and the predicates that represent the patterns of meaning in databases. This book will teach you: the simple and familiar graphical notation of COMN with its three basic shapes and four line styles; how to think about objects, concepts, types, and classes in the real world, using the ordinary meanings of English words that arent tangled with confused techno-speak; how to express logical data designs that are freer from implementation considerations than is possible in any other notation; how to understand key-value, document, columnar, and table-oriented database designs in logical and physical terms; how to use COMN to specify physical database implementations in any NoSQL or SQL database with the precision necessary for model-driven development. A quick reference guide to COMN is included in an appendix. The full notation reference is available at http:\/\/www.tewdur.com\/","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174507869,"sku":null,"price":30.74,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634621090.jpg?v=1789255270"},{"product_id":"9781634621175","title":"Data Lake Architecture","description":"Organizations invest incredible amounts of time and money obtaining and then storing big data in data stores called data lakes. But how many of these organizations can actually get the data back out in a useable form? Very few can turn the data lake into an information gold mine. Most wind up with garbage dumps. Data Lake Architecture will explain how to build a useful data lake, where data scientists and data analysts can solve business challenges and identify new business opportunities. Learn how to structure data lakes as well as analog, application, and text-based data ponds to provide maximum business value. Understand the role of the raw data pond and when to use an archival data pond. Leverage the four key ingredients for data lake success: metadata, integration mapping, context, and metaprocess. Bill Inmon opened our eyes to the architecture and benefits of a data warehouse, and now he takes us to the next level of data lake architecture.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174573405,"sku":null,"price":19.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634621175.jpg?v=1789255291"},{"product_id":"9781634621267","title":"Getting in Front on Data","description":"This book lays out the roles everyone, up and down the organisation chart, can and must play to ensure that data is up to the demands of its use, in day-in, day-out work, decision-making, planning, and analytics. By now, everyone knows that bad data extorts an enormous toll, adding huge (though often hidden) costs, and making it more difficult to make good decisions and leverage advanced analyses.  While the problems are pervasive and insidious, they are also solvable!  As Tom Redman, the Data Doc explains, the secret lies in getting the right people in the right roles to get in front of the management and social issues that lead to bad data in the first place. Everyone should see himself or herself in this book.  We are all both data customers and data creators -- after all, we use data created by others and create data used by others.   And all of us must step up to these roles. As data customers, we must clarify our most important needs and communicate them to data creators. As data creators, we must strive to meet those needs by finding and eliminating the root causes of error. This book proposes new roles for data professionals as: embedded data managers, in helping data customers and creators complete their work, DQ team leads, in connecting customers and creators, pulling the entire program together, and training people on their new roles, data maestros, in providing deep expertise on the really tough problems, chief data architects, in establishing common data definitions, and technologists, in increasing scale and decreasing unit cost.  The book introduces a new role, the data provocateur, the motive force in attacking data quality properly!  This book urges everyone to unleash their inner provocateur.  Finally, it crystallises what senior leaders must do if their entire organisations are to enjoy the benefits of high-quality data!","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174704477,"sku":null,"price":30.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634621267.jpg?v=1789255312"},{"product_id":"9781634621304","title":"Julia for Data Science","description":"Master how to use the Julia language to solve business critical data science challenges. After covering the importance of Julia to the data science community and several essential data science principles, we start with the basics including how to install Julia and its powerful libraries. Many examples are provided as we illustrate how to leverage each Julia command, dataset, and function.  Specialised script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover: 1. An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia; 2. Options for Julia IDEs; 3. Programming structures and functions; 4. Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data pre-processing; 5. Data visualisation and some simple yet powerful statistics for data exploration purposes; 6. Dimensionality reduction and feature evaluation; 7. Machine learning methods, ranging from unsupervised (different types of clustering) to supervised ones (decision trees, random forests, basic neural networks, regression trees, and Extreme Learning Machines); 8. Graph analysis including pinpointing the connections among the various entities and how they can be mined for useful insights.  Each chapter concludes with a series of questions and exercises to reinforce what you learned. The last chapter of the book will guide you in creating a data science application from scratch using Julia.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174737245,"sku":null,"price":34.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634621304.jpg?v=1789255321"},{"product_id":"9781634621472","title":"Growing Business Intelligence","description":"How do we enable our organisations to enjoy the often significant benefits of BI and analytics, while at the same time minimising the cost and risk of failure? In this book, I am not going to try to be prescriptive; I wont tell you exactly how to build your BI environment. Instead, I am going to focus on a few core principles that will enable you to navigate the rocky shoals of BI architecture and arrive at a destination best suited for your particular organisation. Some of these core principles include: Have an overarching strategy, plan, and roadmap. Recognise and leverage your existing technology investments. Support both data discovery and data reuse. Keep data in motion, not at rest. Separate information delivery from data storage. Emphasise data transparency over data quality. Take an agile approach to BI development. This book will show you how to successfully navigate both the jungle of BI technology and the minefield of human nature. It will show you how to create a BI architecture and strategy that addresses the needs of all organisational stakeholders. It will show you how to maximise the value of your BI investments. It will show you how to manage the risk of disruptive technology. And it will show you how to use agile methodologies to deliver on the promise of BI and analytics quickly, succinctly, and iteratively. This book is about many things. But principally, its about success. The goal of any enterprise initiative is to succeed and to derive benefit -- benefit that all stakeholders can share in. I want you to be successful. I want your organisation to be successful. This book will show you how. This book is for anyone who is currently or will someday be working on a BI, analytics, or Big Data project, and for organisations that want to get the maximum amount of value from both their data and their BI technology investment. This includes all stakeholders in the BI effort -- not just the data people or the IT people, but also the business stakeholders who have the responsibility for the definition and use of data. There are six sections to this book: In Section I, What Kind of Garden Do You Want?, we will examine the benefits and risks of Business Intelligence, making the central point that BI is a business (not IT) process designed to manage data assets in pursuit of enterprise goals. We will show how data, when properly managed and used, can be a key enabler of several types of core business processes. The purpose of this section is to help you define the particular benefit(s) you want from BI. In Section II, Building the Bones, we will talk about how to design and build out the hardscape (infrastructure) of your BI environment. This stage of the process involves leveraging existing technology investments and iteratively moving toward your desired target state BI architecture.  In Section III, From the Ground Up, we explore the more detailed aspects of implementing your BI operational environment. In Section IV, Weeds, Pests and Critters, we talk about the myriad of things that can go wrong on a BI project, and discuss ways of mitigating these risks. In Section V, The Sustainable Garden, we talk about how to create a BI infrastructure that is easy and inexpensive to maintain. Finally, Section VI presents a case study illustrating the principles of this book, as applied to a fictional manufacturing company (the Blue Moon Guitar Company).","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174802781,"sku":null,"price":34.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634621472.jpg?v=1789255341"},{"product_id":"9781634621526","title":"Integrating Hadoop","description":"This book leverages the discipline of data integration and applies it to the Hadoop open-source software framework for storing data on clusters of commodity hardware. It is packed with the need-to-know for managers, architects, designers, and developers responsible for populating Hadoop in the enterprise, allowing you to harness big data and do it in such a way that the solution: Complies with (and even extends) enterprise standards; Integrates seamlessly with the existing information infrastructure; Fills a critical role within enterprise architecture. The book covers the gamut of the setup, architecture and possibilities for Hadoop in the organisation, including: Supporting an enterprise information strategy; Organising for a successful Hadoop rollout; Loading and extracting of data in Hadoop; Managing Hadoop data once it is in the cluster; Utilising Spark, streaming data, and master data in Hadoop processes -- examples are provided to reinforce concepts.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433174835549,"sku":null,"price":19.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634621526.jpg?v=1789255350"},{"product_id":"9781634621946","title":"Data Modeling Master Class Training Manual","description":"This 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.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433176113501,"sku":null,"price":149.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634621946.jpg?v=1789255387"},{"product_id":"9781634622370","title":"Analytics","description":"Learn how big data and other sources of information can be transformed into valuable knowledge -- knowledge that can create incredible competitive advantage to propel a business toward market leadership. Learn through examples and experience exactly how to pick projects and build analytics teams that deliver results. Know the ethical and privacy issues, and apply the three-part litmus test of context, permission, and accuracy. Without a doubt, data and analytics are the new source of competitive advantage, but how do executives go from hype to action? Thats the objective of this book -- to assist executives in making the right investments in the right place and at the right time in order to reap the full benefits of data analytics.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433177489757,"sku":null,"price":23.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634622370.jpg?v=1789255434"},{"product_id":"9781634622424","title":"Introduction to Data Management Functions \u0026 Tools","description":"This textbook is for the IDMA 201 course in the IDMA Associate Insurance Data Manager (AIDM) designation program. This course defines data management, describes the functions of data managers, provides the business case for data management and introduces the student to concepts and tools used by data managers. Whether you are an actuary, a claims professional, business analyst, or almost any of the other key functions, knowledge of data management can help you do your job better and help you prepare, understand, and protect the raw material -- the data -- so critical to your organisation. IDMA courses, workshops, and forums are highly recommended for a broad audience including new hires, IT and data modeling professionals who want to broaden their knowledge of the business side of insurance data management, anyone who manages and governs data in the industry (statistical, or management information data), and anyone who needs to use or communicate good quality data\/information -- from actuaries to underwriters, and claims and analytics professionals. Students who complete the four IDMA-developed courses and successfully pass the examinations are awarded an Associate Insurance Data Manager (AIDM) designation.  The IDMA courses may be taken in any order; there are no prerequisites.  However, the courses are numbered to indicate a recommended sequence. For details on the designation requirements, please refer to the IDMA Website at www.IDMA.org.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433177522525,"sku":null,"price":110.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634622424.jpg?v=1789255442"},{"product_id":"9781634622493","title":"Introduction to Data Management Functions \u0026 Tools","description":"This study guide is for the IDMA 201 course in the IDMA Associate Insurance Data Manager (AIDM) designation program. This course defines data management, describes the functions of data managers, provides the business case for data management and introduces the student to concepts and tools used by data managers. Whether you are an actuary, a claims professional, business analyst, or almost any of the other key functions, knowledge of data management can help you do your job better and help you prepare, understand, and protect the raw material-the data-so critical to your organisation. IDMA courses, workshops, and forums are highly recommended for a broad audience including new hires, IT and data modeling professionals who want to broaden their knowledge of the business side of insurance data management, anyone who manages and governs data in the industry (statistical, or management information data), and anyone who needs to use or communicate good quality data\/information -- from actuaries to underwriters, and claims and analytics professionals. Students who complete the four IDMA-developed courses and successfully pass the examinations are awarded an Associate Insurance Data Manager (AIDM) designation.  The IDMA courses may be taken in any order; there are no prerequisites.  However, the courses are numbered to indicate a recommended sequence. For details on the designation requirements, please refer to the IDMA Website at www.IDMA.org.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433177555293,"sku":null,"price":61.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634622493.jpg?v=1789255452"},{"product_id":"9781634622561","title":"Data Science","description":"Master the concepts and strategies underlying success and progress in data science. From the author of the bestsellers, Data Scientist and Julia for Data Science, this book covers four foundational areas of data science. The first area is the data science pipeline including methodologies and the data scientists toolbox. The second are essential practices needed in understanding the data including questions and hypotheses. The third are pitfalls to avoid in the data science process. The fourth is an awareness of future trends and how modern technologies like Artificial Intelligence (AI) fit into the data science framework. Targeted towards data science learners of all levels, this book aims to help the reader go beyond data science techniques and obtain a more holistic and deeper understanding of what data science entails. With a focus on the problems data science tries to solve, this book challenges the reader to become a self-sufficient player in the field.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433177620829,"sku":null,"price":34.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634622561.jpg?v=1789255462"},{"product_id":"9781634624237","title":"Quantitative Analysis for System Applications","description":"As data holdings get bigger and questions get harder, data scientists and analysts must focus on the systems, the tools and techniques, and the disciplined process to get the correct answer, quickly! Whether you work within industry or government, this book will provide you with a foundation to successfully and confidently process large amounts of quantitative data. Here are just a dozen of the many questions answered within these pages: What does quantitative analysis of a system really mean? What is a system? What are big data and analytics? How do you know your numbers are good? What will the future data science environment look like? How do you determine data provenance? How do you gather and process information, and then organize, store, and synthesize it? How does an organisation implement data analytics? Do you really need to think like a Chief Information Officer? What is the best way to protect data? What makes a good dashboard? What is the relationship between eating ice cream and getting attacked by a shark? The nine chapters in this book are arranged in three parts that address systems concepts in general, tools and techniques, and future trend topics. Systems concepts include contrasting open and closed systems, performing data mining and big data analysis, and gauging data quality. Tools and techniques include analyzing both continuous and discrete data, applying probability basics, and practicing quantitative analysis such as descriptive and inferential statistics. Future trends include leveraging the Internet of Everything, modeling Artificial Intelligence, and establishing a Data Analytics Support Office (DASO). Many examples are included that were generated using common software, such as Excel, Minitab, Tableau, SAS, and Crystal Ball. While words are good, examples can sometimes be a better teaching tool. For each example included, data files can be found on the companion website. Many of the data sets are tied to the global economy because they use data from shipping ports, air freight hubs, largest cities, and soccer teams. The appendices contain more detailed analysis including the 10 Ts for Data Mining, Million Row Data Audit (MRDA) Processes, Analysis of Rainfall, and Simulation Models for Evaluating Traffic Flow.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433179291997,"sku":null,"price":46.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634624237.jpg?v=1789255549"},{"product_id":"9781634627733","title":"The Rosedata Stone","description":"Creating a precise diagram of business terms within your projects is a simple yet powerful communication tool for project managers, data governance professionals, and business analysts.Similar to how the Rosetta Stone provided a communication tool across multiple languages, the Rosedata Stone provides a communication tool across business languages. The Rosedata Stone, called the Business Terms Model (BTM) or the Conceptual Data Model, displays the achievement of a Common Business Language of terms for a particular business initiative. With more and more data being created and used, combined with intense competition, strict regulations, and rapid-spread social media, the financial, liability, and credibility stakes have never been higher and therefore the need for a Common Business Language has never been greater. Appreciate the power of the BTM and apply the steps to build a BTM over the books five chapters: 1. Challenges. Explore how a Common Business Language is more important than ever with technologies like the Cloud and NoSQL, and Regulations such as the GDPR. 2. Needs. Identify scope and plan precise, minimal visuals that will capture the Common Business Language. 3. Solution. Meet the BTM and its components, along with the variations of relational and dimensional BTMs. Experience how several data modeling tools display the BTM, including CaseTalk, ER\/Studio, erwin DM, and Hackolade. 4. Construction. Build operational (relational) and analytics (dimensional) BTMs for a bakery chain. 5. Practice. Reinforce BTM concepts and build BTMs for two of your own initiatives alongside a real example.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433181225309,"sku":null,"price":19.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634627733.jpg?v=1789255636"},{"product_id":"9781634628136","title":"Julia for Machine Learning","description":"Unleash the power of Julia for your machine learning tasks. We reveal why Julia is chosen for more and more data science and machine learning projects, including Julias ability to run algorithms at lightning speed. Next, we show you how to set up Julia and various IDEs such as Jupyter. Afterward, we explore key Julia libraries, which are useful for data science work, including packages related to visuals, data structures, and mathematical processes. After building a foundation in Julia, we dive into machine learning, with foundational concepts reinforced by Julia use cases. The use cases build upon each other, reaching the level where we code a machine learning model from scratch using Julia. All of these use cases are available in a series of Jupyter notebooks. After covering dimensionality reduction methods, we explore additional machine learning topics, such as parallelization and data engineering. Although knowing how to use Julia is essential, it is even more important to communicate our results to the business, which we cover next, including how to work efficiently with project stakeholders. Our Julia journey then ascends to the finer points, including improving machine learning transparency, reconciling machine learning with statistics, and continuing to innovate with Julia.  The final chapters cover future trends in the areas of Julia, machine learning, and artificial intelligence. We explain machine learning and Bayesian Statistics hybrid systems, and Julias Gen language. We share many resources so you can continue to sharpen your Julia and machine learning skills. Each chapter concludes with a series of questions designed to reinforce that chapters material, with answers provided in an appendix. Other appendices include an extensive glossary, bridge packages between Julia and other programming languages, and an overview of three data science-related heuristics implemented in Julia, which arent in any of the existing packages.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433181323613,"sku":null,"price":38.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634628136.jpg?v=1789255662"},{"product_id":"9781634628242","title":"DAMA-DMBOK, Italian Version","description":"Text in Itlaian: Il Data Management Body of Knowledge (DAMA-DMBOK2) presenta una vista complessiva delle sfide, complessitá e valore di unefficace gestione dei dati. Le organizzazioni odierne riconoscono che la gestione dei dati è fondamentale per il loro successo. Riconoscono il valore dei loro dati e cercano di sfruttare tale valore. Con l'aumento della nostra capacitá di creare e sfruttare i dati, aumenta anche la necessitá di pratiche affidabili di gestione dei dati. La seconda edizione della Guida di DAMA International al Data Management Body of Knowledge (DAMA-DMBOK2) aggiorna e accresce il DMBOK1, che ha avuto grande successo. Libro di riferimento accessibile e autorevole scritto da pensatori leader del settore e ampiamente recensito dai membri DAMA, il DMBOK2 riunisce materiali che descrivono in modo esaustivo le sfide del data management e come affrontarle: Definendo una serie di principi guida per il data management e descrivendo come questi principi possono essere applicati all'interno delle aree funzionali del data management. Fornendo un framework funzionale per limplementazione dellenterprise data management, includendo pratiche ampiamente adottate, metodi e tecniche, funzioni, ruoli deliverable e metriche. Stabilendo un vocabolario comune per i concetti del data management e fornendo il fondamento delle best practice per i data management professional. DAMA-DMBOK2 offre a professionisti del data management e dellIT, a executive, knowledge workers, educatori, e ricercatori un framework per gestire i propri dati e far maturare linfrastruttura dellinformazione, basato su questi principi: I Dati sono un asset con proprietá uniche. Il valore dei dati può e deve essere espresso in termini economici. Gestire i dati significa gestire la qualitá dei dati. Servono i metadati per gestire i dati. Serve pianificazione per gestire i dati. Il Data management è cross-funzionale e richiede una serie di skill ed expertise. Il Data management richiede una prospettiva enterprise. Il Data management deve tenere conto di una serie di differenti prospettive. Il Data management è gestione del ciclo di vita dei dati. Diversi tipi di dati hanno diversi requisiti sul ciclo di vita. Gestire i dati include gestire i rischi associati ai dati. I requisiti del Data management devono guidare le decisioni dellinformation technology. Un data management efficace richiede commitment della leadership. I Capitoli includono: Data Management. Trattamento Etico dei Dati. Data Governance. Data Architecture. Data Modeling e Design. Data Storage e Operations. Data Security. Data Integration \u0026amp; Interoperability. Document e Content Management. Reference e Master Data. Data Warehousing e Business Intelligence. Metadata Management. Data Quality Management. Big Data e Data Science. Data Management Maturity Assessment. Organizzazione per il Data Management e Aspettative dei Ruoli. Data Management e Organizational Change Management. La standardizzazione delle discipline del data management aiuterá i data management professional a ottenere prestazioni più efficaci e coerenti. Consentirá, inoltre, ai responsabili dell'organizzazione di riconoscere il valore e i contributi delle attivitá di data management.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433181356381,"sku":null,"price":62.99,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634628242.jpg?v=1789255670"},{"product_id":"9781634628273","title":"Analytics Best Practices","description":"Deliver enterprise data analytics success by following Prashanths prescriptive and practical techniques. Today, organizations across the globe are looking at ways to glean insights from data analytics and make good business decisions. However, not many business enterprises are successful in data analytics. According to Gartner, 80% of analytics programs do not deliver business outcomes. Mckinsey consulting says, less than 20% of the companies have achieved analytics at scale.  So, how can a business enterprise avoid analytics failure and deliver business results? This book provides ten key analytics best practices that will improve the odds of delivering enterprise data analytics solutions successfully. It is intended for anyone who has a stake and interest in deriving insights from data analytics. The three key differentiating aspects of this book are: Practicality. This book offers prescriptive, superior, and practical guidance. Completeness. This book looks at data analytics holistically across the four key data analytics domains - data management, data engineering, data science, and data visualization. Neutrality. This book is technologically agnostic and looks at analytics concepts without any reference to commercial analytics products and technologies.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433181389149,"sku":null,"price":23.24,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634628273.jpg?v=1789255679"},{"product_id":"9781634628327","title":"Fifty Years of Relational, and Other Database Writings","description":"Fifty years of relational.  Its hard to believe the relational model has been around now for over half a century!  But it has -- it was born on 19 August 1969, when Codds first database paper was published.  And Chris Date has been involved with it for almost the whole of that time, working closely with Codd for many years and publishing the very first, and definitive, book on the subject in 1975.  In this books title essay, Chris offers his own unique perspective (two chapters) on those fifty years.  No database professional can afford to miss this one of a kind history. But theres more to this book than just a little personal history.  Another unique feature is an extensive and in depth discussion (nine chapters) of a variety of frequently asked questions on relational matters, covering such topics as mathematics and the relational model; relational algebra; predicates; relation valued attributes; keys and normalization; missing information; and the SQL language.  Another part of the book offers detailed responses to critics (four chapters).  Finally, the book also contains the text of several recent interviews with Chris Date, covering such matters as RM\/V2, XML, NoSQL, The Third Manifesto, and how SQL came to dominate the database landscape.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433181454685,"sku":null,"price":30.74,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634628327.jpg?v=1789733847"},{"product_id":"9781634628440","title":"Data Modeling Made Simple with erwin DM","description":"Master erwin DM to deliver robust and precise designs for both operationaland analytical projects.  Steve and Jeff start from the basics, explaining data modeling concepts and how to get up and running with erwin DM (erwin  DM). Through a hands-on approach, businessanalysts, data professionals, and project  managers will learn  step-by-step how  to build effective conceptual, logical, and  physical data models. Complete the stages in identifying essential business requirements, designing   the   logical data model,transposing those logical modeling objects into physical tables and columns, and  evengenerating the implementation database scripts.This book contains seven parts. Part I provices a foundation in data medling and Part II a foundatin in erwin DM. Part III covers the design layer technique and its application using erwin DM, distinguishing conceptual, logical, physical and operational data models. Part IV covers entities, domains, attributes, key groups, validation rules, default rules and subject areas, along with how to implement them using erwin DM. Part V ecplains the physical data model and how to convert a logical data model to a physical data model in erwin DM. Decome confident creating tables, columns, indexes and views. Part VI reveals advanced features available within erwin DM, including user defined properties, naming standards, forward engineering, reverse engineering, complere compare, report designer and the bulk editor. Part VII explains several important tools to use in combination with erwin DM, including erwin DM NoSQL, erwin Data Catalog and erwin Data Literacy.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433182470493,"sku":null,"price":61.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634628440.jpg?v=1789255697"},{"product_id":"9781634628754","title":"Logic and Relational Theory","description":"This book is a revised, upgraded, and hugely improved version of an earlier one called Logic and Databases.  Although its effectively a brand new book, therefore, the following remarks from that earlier book are still relevant here.  First, logic and databases are inextricably intertwined.  The relational model itself is essentially just elementary logic, tailored to database needs.  Now, if youre a database professional, this wont be news to you -- but you still might not realize just how much everything we do in the database world is (or should be!) affected by logic.  Logic is fundamental, and everywhere. As a database professional, therefore, you owe it to yourself to understand the basics of formal logic, and you ought to be able to explain (and perhaps defend) the connections between formal logic and database technology.  And thats what this book is about.  What it does is show, through a series of partly independent, partly interrelated essays, just how various crucial aspects of database technology -- some of them very familiar, others maybe less so -- are solidly grounded in formal logic.  Overall, the goal is to help you realize the importance of logic in everything you do, and also, I hope, to help you see that logic can be fun.","brand":"Technics Publications LLC (US)","offers":[{"title":"Default Title","offer_id":65433182568797,"sku":null,"price":30.74,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634628754.jpg?v=1789255717"},{"product_id":"9781634635431","title":"SQL Server 2014","description":"Written for readers who have little or no previous experience with databases, SQL, or Microsoft SQL Server database software, this book provides a systematic approach to learning SQL (Structured Query Language) using SQL Server database software. It starts with simple SQL concepts, and progresses to more complex query development. Each chapter is written in a step-by-step manner and has examples that can be executed using SQL Server. 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Chapter Three discusses the intensive developing of Soft Computing systems especially Wavelet-Neuro-Fuzzy Systems (WNFS) in Dynamic Data Mining tasks, when the data are fed sequentially to the processing in on-line mode.","brand":"Nova Science Publishers, Inc","offers":[{"title":"Default Title","offer_id":65433956942173,"sku":null,"price":139.49,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781634851336.jpg?v=1789274440"},{"product_id":"9781685074494","title":"Machine Learning Algorithms for Engineering Applications","description":"Machine learning is a vital part of numerous academic and financial applications, in areas ranging from health care and treatment to finding relevant information in social networks. Large organisations thoughtfully apply machine learning algorithms with extensive research teams. The purpose of this book is to provide an intellectual introduction to statistical or machine learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical exercise. Statistical analysis is an integral part of machine learning and can be described as a form of it, often even utilising well-known and familiar techniques, that has a different focus than traditional analytical practice in applied disciplines. 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For the relevant stakeholders, such unauthorized alterations will be catalogued that the system is without the proper data integrity (DI) controls and would be deemed a noticeable deficiency. As a state or condition, e-records integrity is a measure of the validity and fidelity of related data. Based on the Universal Electronic Records Management (ERM) Requirements, Version 2.03 published by the National Archives and Records Administration (NARA), this book covers the requirements applicable to DI in the medicines' manufacturing practices regulations, pharmaceutical and biotechnological. This book presents the bounded characterization of e-records handling systems. The reader will have a reference of over fifty-three requirements that need to be agreed upon between the relevant stakeholders.","brand":"Nova Science Publishers, Inc","offers":[{"title":"Default Title","offer_id":65434354155869,"sku":null,"price":152.99,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781685075538.jpg?v=1789282187"},{"product_id":"9781685076504","title":"Predictive Analytics for Data Driven Decision Making","description":"Predictive analytics is an evolving field and has applications across all domains and sectors. This book will introduce to the reader the concept of predictive analytics and cover in detail the predictive analytic models, tools and techniques involved. The book will also cover the applications of predictive analytics in various domains including health care, banking, agriculture, retailing, sports and industries using smart grid and industrial drivers with real world scenarios. This book covers performance improvement and enhancement techniques with the aid of intelligent predictive analytical algorithms to predict future patterns. This would be a handy guide covering all steps from identification of the problem, preparing the data, model building and recommending solutions. 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The wide variety of topics it presents offers readers multiple perspectives on a variety of disciplines. The aim of this edited book is to publish the latest research advancements in the convergence of automation technology, artificial intelligence, biomedical engineering, and health informatics. This will help readers grasp the essence of the recent advances in this field.","brand":"Nova","offers":[{"title":"Default Title","offer_id":65434396819805,"sku":null,"price":106.49,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1041\/3893\/2573\/files\/9781685079888.jpg?v=1789284215"},{"product_id":"9781935504122","title":"DAMA Dictionary of Data Management","description":"A glossary of over 2,000 terms which provides a common data management vocabulary for IT and Business professionals, and is a companion to the DAMA Data Management Body of Knowledge (DAMA-DMBOK). 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