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No one knows colleges better than The Princeton Review! Inside The Complete Book of Colleges, 2020 Edition, students will find meticulously researched information that will help them narrow their college search.
This is the Digital Practitioner Foundation Study Guide for the DPBoK Part 1 Examination. It gives an overview of every learning objective included in the Digital Practitioner Foundation syllabus, and provides in-depth coverage on preparing and taking the DPBoK Part 1 Examination. It is specifically designed to help individuals prepare for certification. This Study Guide is excellent material for: • Senior digital business professionals who need an increased awareness of digital practices • Mid-career IT professionals who need to stay relevant and validate their digital Subject Matter Expert (SME) status in specific domain areas • Entry-level computing and digital business professionals • College-level students and computing and digital business majors It covers the following topics: • An introduction to DPBoK Foundation certification, including the DPBoK Part 1 Examination • Key terminology, key concepts, and the structure of the Body of Knowledge • Basic concepts employed by the Digital Practitioner • The capabilities of digital infrastructure and initial concerns for its effective, efficient, and secure operation • The objectives and activities of application development • Why product management is formalized as a company or team grows, and the differences between product and project management • The key concerns and practices of work management as a team increases in size • The basic concepts and practices of operations management in a digital/IT context • How to coordinate as the organization grows into multiple teams and multiple products • IT investment and portfolio management • Organizational structure, human resources, and cultural factors • Governance, risk, security, and compliance • Information and data management on a large scale • Practices and methods for managing complexity using Enterprise Architecture
Imagine what you could do if scalability wasn't a problem. With this hands-on guide, you’ll learn how the Cassandra database management system handles hundreds of terabytes of data while remaining highly available across multiple data centers. This third edition—updated for Cassandra 4.0—provides the technical details and practical examples you need to put this database to work in a production environment. Authors Jeff Carpenter and Eben Hewitt demonstrate the advantages of Cassandra’s nonrelational design, with special attention to data modeling. If you’re a developer, DBA, or application architect looking to solve a database scaling issue or future-proof your application, this guide helps you harness Cassandra’s speed and flexibility. Understand Cassandra’s distributed and decentralized structure Use the Cassandra Query Language (CQL) and cqlsh—the CQL shell Create a working data model and compare it with an equivalent relational model Develop sample applications using client drivers for languages including Java, Python, and Node.js Explore cluster topology and learn how nodes exchange data
This volume will assist readers in fitting big data analysis into their service-based organizations. Volume I of this two-volume series focuses on the role of big data in service delivery systems. It discusses the definition and orientation to big data, applications of it in service delivery systems, how to obtain results that can affect/enhance service delivery, and how to build an effective big data organization. This volume will assist readers in fitting big data analysis into their service-based organizations. It will also help readers understand how to improve the use of big data to enhance their service-oriented organizations.
The continued advancement of globalization, increases in internet connectivity, compatibility of international payment systems, and adaptability of logistics and shipping processes have combined to contribute to the rapid growth of the cross-border e-commerce market. Due to these advancements and the ubiquitous presence of smartphones, consumer use of cross-border e-commerce is increasingly simplified, and thus, sellers are hardly restricted to a specific country in terms of promoting, selling, and shipping goods worldwide. The burgeoning opportunities, habits, and trends of shopping on cross-border e-commerce platforms have expedited the prospect of becoming a presence in the global market. This is true for enterprises of all sizes, especially for small? and medium?sized enterprises (SMEs) that want to add their footprint in the international market for the first time. Like any other industry, cross-border e-commerce has its specific economics and driving forces, but has different scopes, challenges, and trends due to the geographic and cultural expanse of relevant environments. Cross-Border E-Commerce Marketing and Management was conceptualized by identifying the scope of new complementary information with a comprehensive understanding of the issues and potential of cross-border e-commerce businesses. The authors believe that this book will not only fill the void in the current research but will also provide far-sighted vision and strategies, as it covers big data, artificial intelligence, IoT, supply chain management, and more. This book provides the necessary knowledge to managers to compete with the competitive market structure and ultimately contribute to the sustainable economic growth of a country. It works as a guideline for existing cross-border e-commerce managers to formulate individual strategies that combine to optimize the industry while keeping the enterprise competitive. This book is useful in both developed and developing country contexts. This publication is an ideal resource for academicians, policy makers, stakeholders, and cross-border e-commerce managers, especially from SMEs.
With new technologies, such as computer vision, internet of things, mobile computing, e-governance and e-commerce, and wide applications of social media, organizations generate a huge volume of data and at a much faster rate than several years ago. Big data in large-/small-scale systems, characterized by high volume, diversity, and velocity, increasingly drives decision making and is changing the landscape of business intelligence. From governments to private organizations, from communities to individuals, all areas are being affected by this shift. There is a high demand for big data analytics that offer insights for computing efficiency, knowledge discovery, problem solving, and event prediction. To handle this demand and this increase in big data, there needs to be research on innovative and optimized machine learning algorithms in both large- and small-scale systems. Applications of Big Data in Large- and Small-Scale Systems includes state-of-the-art research findings on the latest development, up-to-date issues, and challenges in the field of big data and presents the latest innovative and intelligent applications related to big data. This book encompasses big data in various multidisciplinary fields from the medical field to agriculture, business research, and smart cities. While highlighting topics including machine learning, cloud computing, data visualization, and more, this book is a valuable reference tool for computer scientists, data scientists and analysts, engineers, practitioners, stakeholders, researchers, academicians, and students interested in the versatile and innovative use of big data in both large-scale and small-scale systems.
Intelligent business analytics is an emerging technology that has become a mainstream market adopted broadly across industries, organizations, and geographic regions. Intelligent business analytics is a current focus for research and development across academia and industries and must be examined and considered thoroughly so businesses can apply the technology appropriately. The Handbook of Research on Foundations and Applications of Intelligent Business Analytics examines the technologies and applications of intelligent business analytics and discusses the foundations of intelligent analytics such as intelligent mining, intelligent statistical modeling, and machine learning. Covering topics such as augmented analytics and artificial intelligence systems, this major reference work is ideal for scholars, engineers, professors, practitioners, researchers, industry professionals, academicians, and students.
Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants.
Meeting the Challenges of Data Quality Management outlines the foundational concepts of data quality management and its challenges. The book enables data management professionals to help their organizations get more value from data by addressing the five challenges of data quality management: the meaning challenge (recognizing how data represents reality), the process/quality challenge (creating high-quality data by design), the people challenge (building data literacy), the technical challenge (enabling organizational data to be accessed and used, as well as protected), and the accountability challenge (ensuring organizational leadership treats data as an asset). Organizations that fail to meet these challenges get less value from their data than organizations that address them directly. The book describes core data quality management capabilities and introduces new and experienced DQ practitioners to practical techniques for getting value from activities such as data profiling, DQ monitoring and DQ reporting. It extends these ideas to the management of data quality within big data environments. This book will appeal to data quality and data management professionals, especially those involved with data governance, across a wide range of industries, as well as academic and government organizations. Readership extends to people higher up the organizational ladder (chief data officers, data strategists, analytics leaders) and in different parts of the organization (finance professionals, operations managers, IT leaders) who want to leverage their data and their organizational capabilities (people, processes, technology) to drive value and gain competitive advantage. This will be a key reference for graduate students in computer science programs which normally have a limited focus on the data itself and where data quality management is an often-overlooked aspect of data management courses. - Describes the importance of high-quality data to organizations wanting to leverage their data and, more generally, to people living in today's digitally interconnected world - Explores the five challenges in relation to organizational data, including "Big Data," and proposes approaches to meeting them - Clarifies how to apply the core capabilities required for an effective data quality management program (data standards definition, data quality assessment, monitoring and reporting, issue management, and improvement) as both stand-alone processes and as integral components of projects and operations - Provides Data Quality practitioners with ways to communicate consistently with stakeholders