Search results for "Data Learning Press"
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Personalized Machine Learning
Every day we interact with machine learning systems offering individualized predictions for our entertainment, social connections, purchases, or health. These involve several modalities of data, from sequences of clicks to text, images, and social interactions. This book introduces common principles and methods that underpin the design of personalized predictive models for a variety of settings and modalities. The book begins by revising 'traditional' machine learning models, focusing on adapting them to settings involving user data, then presents techniques based on advanced principles such as matrix factorization, deep learning, and generative modeling, and concludes with a detailed study of the consequences and risks of deploying personalized predictive systems. A series of case studies in domains ranging from e-commerce to health plus hands-on projects and code examples will give readers understanding and experience with large-scale real-world datasets and the ability to design models and systems for a wide range of applications.
R 2,091.92
Practical DataOps
Gain a practical introduction to DataOps, a new discipline for delivering data science at scale inspired by practices at companies such as Facebook, Uber, LinkedIn, Twitter, and eBay. Organizations need more than the latest AI algorithms, hottest tools, and best people to turn data into insight-driven action and useful analytical data products. Processes and thinking employed to manage and use data in the 20th century are a bottleneck for working effectively with the variety of data and advanced analytical use cases that organizations have today. This book provides the approach and methods to ensure continuous rapid use of data to create analytical data products and steer decision making.Practical DataOps shows you how to optimize the data supply chain from diverse raw data sources to the final data product, whether the goal is a machine learning model or other data-orientated output. The book provides an approach to eliminate wasted effort and improve collaboration between data producers, data consumers, and the rest of the organization through the adoption of lean thinking and agile software development principles.This book helps you to improve the speed and accuracy of analytical application development through data management and DevOps practices that securely expand data access, and rapidly increase the number of reproducible data products through automation, testing, and integration. The book also shows how to collect feedback and monitor performance to manage and continuously improve your processes and output. What You Will LearnDevelop a data strategy for your organization to help it reach its long-term goalsRecognize and eliminate barriers to delivering data to users at scaleWork on the right things for the right stakeholders through agile collaborationCreate trust in data via rigorous testing and effective data managementBuild a culture of learning and continuous improvement through monitoring deployments and measuring outcomesCreate cross-functional self-organizing teams focused on goals not reporting linesBuild robust, trustworthy, data pipelines in support of AI, machine learning, and other analytical data productsWho This Book Is ForData science and advanced analytics experts, CIOs, CDOs (chief data officers), chief analytics officers, business analysts, business team leaders, and IT professionals (data engineers, developers, architects, and DBAs) supporting data teams who want to dramatically increase the value their organization derives from data. The book is ideal for data professionals who want to overcome challenges of long delivery time, poor data quality, high maintenance costs, and scaling difficulties in getting data science output and machine learning into customer-facing production.
R 1,118.73
R 916.00
Database Development For Dummies
Powerful, low-cost database development tools make it possible for virtually anybody to create their own database—and this hands-on guide makes it fun and easy Databases permeate every nook and cranny of our highly connected, information-intensive world, from ATMs to personal finance, online shopping, and networked information management. Databases have become so integral to the business environment that, nowadays, it's next to impossible to stay competitive without the assistance of some sort of database technology—no matter what type or size of business you run. But developing your own database can be tricky. Whether you want to keep records for a small business or run a large e-commerce website, developing the right database system can be a major challenge. Which is where Database Development For Dummies comes in. From data modeling methods and development tools to Internet accessibility and security, this comprehensive guide shows you, step-by-step and with illustrations, everything you need to know about building a custom system from the ground up. You'll learn to: Model data accuratelyDesign a reliable functional databaseDeliver robust relational databases on time and on budgetBuild a user-friendly database applicationPut your database on the Web The book acquaints you with the most popular data modeling methods and shows you how to systematically design and develop a system incorporating a database and one or more applications that operate on it. Here's more of what you'll discover in the book: Database architecture and how it has evolvedHow database technology affects everyday lifeA structured approach to database developmentHow to create an appropriate data modelHow to develop a reliable relational designThe complexities that you're likely to encounter in designing a database and how to simplify themImplementing your design using Microsoft Access 2000, SQL Server, and other powerful database development toolsDatabase securityTen rules to know when creating a databaseAnother ten rules to know when creating a database application If you need a database tailored to you and your company's current and future data storage and management needs, this is the book for you. Get Database Development For Dummies and discover what it takes to design, develop, and implement a sophisticated database system.
R 958.00
Introduction to Clustering Large and High-Dimensional Data
There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and the World Wide Web continue to grow exponentially, the ability to find useful information increasingly depends on the indexing infrastructure or search engine. Clustering techniques can be used to discover natural groups in data sets and to identify abstract structures that might reside there, without having any background knowledge of the characteristics of the data. Clustering has been used in a variety of areas, including computer vision, VLSI design, data mining, bio-informatics (gene expression analysis), and information retrieval, to name just a few. This book focuses on a few of the most important clustering algorithms, providing a detailed account of these major models in an information retrieval context. The beginning chapters introduce the classic algorithms in detail, while the later chapters describe clustering through divergences and show recent research for more advanced audiences.
R 2,001.85
Basic Concepts in Data Structures
Data structures are the fundamental building blocks of any computer program, used for storing, representing and manipulating data in a computer. This text presents the basic concepts of data structures as part of the art of writing computer programs. The author concentrates on the fundamentals of what should be done to solve a problem efficiently rather than technical implementation details. The text includes pseudocode and covers all the most common data structures, such as queues, stacks, trees, heaps, and hash tables, along with the basics of searching, sorting, and coding. This book can supplement any undergraduate algorithms or discrete math course and will also be accessible to students and researchers in other quantitative disciplines. No prerequisites beyond introductory programming and basic mathematics are required, and the accessible, conceptual presentation will appeal to undergraduates in many quantitative fields. Carefully designed problem sets with selected solutions will allow students to test their understanding.
R 1,869.00
Big Data MBA
Integrate big data into business to drive competitive advantage and sustainable success Big Data MBA brings insight and expertise to leveraging big data in business so you can harness the power of analytics and gain a true business advantage. Based on a practical framework with supporting methodology and hands-on exercises, this book helps identify where and how big data can help you transform your business. You'll learn how to exploit new sources of customer, product, and operational data, coupled with advanced analytics and data science, to optimize key processes, uncover monetization opportunities, and create new sources of competitive differentiation. The discussion includes guidelines for operationalizing analytics, optimal organizational structure, and using analytic insights throughout your organization's user experience to customers and front-end employees alike. You'll learn to “think like a data scientist” as you build upon the decisions your business is trying to make, the hypotheses you need to test, and the predictions you need to produce. Business stakeholders no longer need to relinquish control of data and analytics to IT. In fact, they must champion the organization's data collection and analysis efforts. This book is a primer on the business approach to analytics, providing the practical understanding you need to convert data into opportunity. Understand where and how to leverage big dataIntegrate analytics into everyday operationsStructure your organization to drive analytic insightsOptimize processes, uncover opportunities, and stand out from the restHelp business stakeholders to “think like a data scientist”Understand appropriate business application of different analytic techniques If you want data to transform your business, you need to know how to put it to use. Big Data MBA shows you how to implement big data and analytics to make better decisions.
R 958.00
Mobility Data
Mobility of people and goods is essential in the global economy. The ability to track the routes and patterns associated with this mobility offers unprecedented opportunities for developing new, smarter applications in different domains. Much of the current research is devoted to developing concepts, models, and tools to comprehend mobility data and make it manageable for these applications. This book surveys the myriad facets of mobility data, from spatio-temporal data modeling, to data aggregation and warehousing, to data analysis, with a specific focus on monitoring people in motion (drivers, airplane passengers, crowds, and even animals in the wild). Written by a renowned group of worldwide experts, it presents a consistent framework that facilitates understanding of all these different facets, from basic definitions to state-of-the-art concepts and techniques, offering both researchers and professionals a thorough understanding of the applications and opportunities made possible by the development of mobility data.
R 3,945.00
R 611.00
R 611.00
Data Science Landscape
The edited volume deals with different contours of data science with special reference to data management for the research innovation landscape. The data is becoming pervasive in all spheres of human, economic and development activity. In this context, it is important to take stock of what is being done in the data management area and begin to prioritize, consider and formulate adoption of a formal data management system including citation protocols for use by research communities in different disciplines and also address various technical research issues. The volume, thus, focuses on some of these issues drawing typical examples from various domains. The idea of this work germinated from the two day workshop on “Big and Open Data – Evolving Data Science Standards and Citation Attribution Practices”, an international workshop, led by the ICSU-CODATA and attended by over 300 domain experts. The Workshop focused on two priority areas (i) Big and Open Data: Prioritizing, Addressing and Establishing Standards and Good Practices and (ii) Big and Open Data: Data Attribution and Citation Practices. This important international event was part of a worldwide initiative led by ICSU, and the CODATA-Data Citation Task Group. In all, there are 21 chapters (with 21st Chapter addressing four different core aspects) written by eminent researchers in the field which deal with key issues of S&T, institutional, financial, sustainability, legal, IPR, data protocols, community norms and others, that need attention related to data management practices and protocols, coordinate area activities, and promote common practices and standards of the research community globally. In addition to the aspects touched above, the national / international perspectives of data and its various contours have also been portrayed through case studies in this volume.
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R 611.00
R 1,096.00
Data-Intensive Computing
The world is awash with digital data from social networks, blogs, business, science and engineering. Data-intensive computing facilitates understanding of complex problems that must process massive amounts of data. Through the development of new classes of software, algorithms and hardware, data-intensive applications can provide timely and meaningful analytical results in response to exponentially growing data complexity and associated analysis requirements. This emerging area brings many challenges that are different from traditional high-performance computing. This reference for computing professionals and researchers describes the dimensions of the field, the key challenges, the state of the art and the characteristics of likely approaches that future data-intensive problems will require. Chapters cover general principles and methods for designing such systems and for managing and analyzing the big data sets of today that live in the cloud and describe example applications in bioinformatics and cybersecurity that illustrate these principles in practice.
R 4,061.17
Dynamic Network Representation Based on Latent Factorization of Tensors
A dynamic network is frequently encountered in various real industrial applications, such as the Internet of Things. It is composed of numerous nodes and large-scale dynamic real-time interactions among them, where each node indicates a specified entity, each directed link indicates a real-time interaction, and the strength of an interaction can be quantified as the weight of a link. As the involved nodes increase drastically, it becomes impossible to observe their full interactions at each time slot, making a resultant dynamic network High Dimensional and Incomplete (HDI). An HDI dynamic network with directed and weighted links, despite its HDI nature, contains rich knowledge regarding involved nodes’ various behavior patterns. Therefore, it is essential to study how to build efficient and effective representation learning models for acquiring useful knowledge.In this book, we first model a dynamic network into an HDI tensor and present the basic latent factorization of tensors (LFT) model. Then, we propose four representative LFT-based network representation methods. The first method integrates the short-time bias, long-time bias and preprocessing bias to precisely represent the volatility of network data. The second method utilizes a proportion-al-integral-derivative controller to construct an adjusted instance error to achieve a higher convergence rate. The third method considers the non-negativity of fluctuating network data by constraining latent features to be non-negative and incorporating the extended linear bias. The fourth method adopts an alternating direction method of multipliers framework to build a learning model for implementing representation to dynamic networks with high preciseness and efficiency.
R 2,241.80
Knowledge Discovery and Data Mining
Knowledge Discovery and Data mining (KDD) is dedicated to exploring meaningful information from a large volume of data. ""Knowledge Discovery and Data Mining: Challenges and Realities"" is the most comprehensive reference publication for researchers and real-world data mining practitioners to advance knowledge discovery from low-quality data. This premier reference source presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying, low-quality data. International experts in the field of data mining have contributed all-inclusive chapters focusing on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing.
R 6,404.96
Web and Big Data. APWeb-WAIM 2022 International Workshops
This book constitutes revised selected papers from the workshops of the 6th Asia-Pacific Web and Web-Age Information Management International Joint Conference on Web and Big Data, APWeb-WAIM 2022: The Fifth International Workshop on Knowledge Graph Management and Applications, KGMA 2022, The Fourth International Workshop on Semi-structured Big Data Management and Applications, SemiBDMA 2022, and The Third International Workshop on Deep Learning in Large-scale Unstructured Data Analytics, DeepLUDA 2022, held in Nanjing, China, in August 2022. The 23 papers were thoroughly reviewed and selected from the 39 submissions and present recent research on the theory, design, and implementation of data management systems.
R 3,318.68
Big Data Revolution
Exploit the power and potential of Big Data to revolutionize business outcomes Big Data Revolution is a guide to improving performance, making better decisions, and transforming business through the effective use of Big Data. In this collaborative work by an IBM Vice President of Big Data Products and an Oxford Research Fellow, this book presents inside stories that demonstrate the power and potential of Big Data within the business realm. Readers are guided through tried-and-true methodologies for getting more out of data, and using it to the utmost advantage. This book describes the major trends emerging in the field, the pitfalls and triumphs being experienced, and the many considerations surrounding Big Data, all while guiding readers toward better decision making from the perspective of a data scientist. Companies are generating data faster than ever before, and managing that data has become a major challenge. With the right strategy, Big Data can be a powerful tool for creating effective business solutions – but deep understanding is key when applying it to individual business needs. Big Data Revolution provides the insight executives need to incorporate Big Data into a better business strategy, improving outcomes with innovation and efficient use of technology. Examine the major emerging patterns in Big DataConsider the debate surrounding the ethical use of dataRecognize patterns and improve personal and organizational performanceMake more informed decisions with quantifiable results In an information society, it is becoming increasingly important to make sense of data in an economically viable way. It can drive new revenue streams and give companies a competitive advantage, providing a way forward for businesses navigating an increasingly complex marketplace. Big Data Revolution provides expert insight on the tool that can revolutionize industries.
R 456.00
Structural Health Monitoring Based on Data Science Techniques
The modern structural health monitoring (SHM) paradigm of transforming in situ, real-time data acquisition into actionable decisions regarding structural performance, health state, maintenance, or life cycle assessment has been accelerated by the rapid growth of “big data” availability and advanced data science. Such data availability coupled with a wide variety of machine learning and data analytics techniques have led to rapid advancement of how SHM is executed, enabling increased transformation from research to practice. This book intends to present a representative collection of such data science advancements used for SHM applications, providing an important contribution for civil engineers, researchers, and practitioners around the world.
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R 486.00
Repairing and Querying Databases under Aggregate Constraints
Research has deeply investigated several issues related to the use of integrity constraints on relational databases. In particular, a great deal of attention has been devoted to the problem of extracting "reliable" information from databases containing pieces of information inconsistent with regard to some integrity constraints. In this manuscript, the problem of extracting consistent information from relational databases violating integrity constraints on numerical data is addressed. Aggregate constraints defined as linear inequalities on aggregate-sum queries on input data are considered. The notion of repair as consistent set of updates at attribute-value level is exploited, and the characterization of several data-complexity issues related to repairing data and computing consistent query answers is provided. Moreover, a method for computing “reasonable” repairs of inconsistent numerical databases isintroduced, for a restricted but expressive class of aggregate constraints. An extension of this method for dealing with the data repairing problem in the presence of weak aggregate constraints which are expected to be satisfied, but not required to, is presented. Furthermore, a technique for computing consistent answers of aggregate queries in the presence of a wide form of aggregate constraints is provided. Finally, extensions of the framework as well as several open problems are discussed.
R 1,099.51
Database Principles and Technologies – Based on Huawei GaussDB
This open access book contains eight chapters that deal with database technologies, including the development history of database, database fundamentals, introduction to SQL syntax, classification of SQL syntax, database security fundamentals, database development environment, database design fundamentals, and the application of Huawei’s cloud database product GaussDB database.This book can be used as a textbook for database courses in colleges and universities, and is also suitable as a reference book for the HCIA-GaussDB V1.5 certification examination. The Huawei GaussDB (for MySQL) used in the book is a Huawei cloud-based high-performance, highly applicable relational database that fully supports the syntax and functionality of the open source database MySQL. All the experiments in this book can be run on this database platform.As the world’s leading provider of ICT (information and communication technology) infrastructure and smart terminals, Huawei’s products range from digital data communication, cyber security, wireless technology, data storage, cloud computing, and smart computing to artificial intelligence.
R 2,198.00
