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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 scientist's 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...

Unlock the power of responsible AI governance with this essential guide for leaders and practitioners. Gain the knowledge and tools to effectively govern and oversee the use of artificial intelligence in your organization, ensuring compliance with emerging global regulations. Become equipped with the necessary knowledge to establish a solid foundation for managing your AI investments and apply a technology that will revolutionize how you serve your markets, manage your internal business processes, and embed your products’ capabilities. Depending on your organization's locations and ...

Manage and optimize metadata using Artificial Intelligence (AI) and Machine Learning (ML) through this comprehensive guide on the intricate and pivotal world of data cataloging. The book demystifies the concepts of data cataloging, highlighting its critical role in ensuring that data within organizations is accurate, accessible, and actionable. Jeff meticulously lays out strategies and insights on creating a robust data catalog that manages metadata and uses AI and ML to enhance its usability and reliability. In an era dominated by data-driven decisions, understanding and implementing ...

Getting the most from your data requires winning the Enrichment game. Learn the game rules and fit the “pieces,” processes, and people together to achieve a high score in data quality and data useability. First, demonstrate the value of the data our organizations gather, protect, and, in some cases, share. Second, enrich the available data by providing more context on the meaning of the data. Chief data officers, data strategists, data architects, data managers, data scientists, business analysts, and data governance and business intelligence professionals all play the game, and ...

What's the Return on Investment (ROI) on data management? Sound like an impossible question to answer? Not if you read this book and learn the value-added approach to managing enterprise resources and assets. This book defines the five interrelated best practices that comprise data management, and shows you how by example to successfully communicate data management ROI to senior management. The 17 cases we share will help you to identify opportunities to introduce data management into the strategic conversations that occur in the C-suite. You will gain a new perspective regarding the ...

The data lakehouse is the next generation of the data warehouse and data lake, designed to meet today’s complex and ever-changing modern information systems. This book shows you how to construct your data lakehouse as the foundation for your artificial intelligence (AI), machine learning (ML), and data mesh initiatives. Know the pitfalls and techniques for maximizing business value of your data lakehouse. In addition, be able to explain the core characteristics and critical success factors of a data lakehouse. By reviewing entry errors, key incompatibility, and ensuring good documentation...

Overcome the challenges, appreciate the varieties, and apply the process of data integration. Learn all about data integration and become a data integration hero instead of following the masses and running in the opposite direction at the mere mention of the word “integration”. Understand why organizations avoid data integration and often wind up with spider web environments containing siloed applications instead of an enterprise database which excites analysts and data scientists. Distinguish the different types of integration: database, attribute, key, index, encoding, measurement, ...

Let the sun shine through! The cloud in data warehousing skies is finally clearing as Dr. Barry Devlin builds the architectural and systems foundations for data lakehouse, data fabric, and data mesh, as well as the base cloud data warehouse. The past five years has seen an explosion of innovation and new technical forms as cloud data warehousing has gone mainstream. But confusion has grown too. After all, the business needs are largely unchanged. So, why are there so many options and approaches? How do they differ? Which one may be the best choice? And why? In this first volume of a two-...

Integrating Hadoop 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. Integrating Hadoop covers the ...

Read true crime stories of people abusing and misusing data. Just because there’s data doesn’t necessarily mean it’s the right data or people are using it properly. And as the saying goes, those who do not learn history are doomed to repeat it. Documenting and understanding these stories can prevent them from happening again. Through a career in data, Merrill sees data problems (data crimes) caused by people treating data improperly. Recognizing data crimes is the first step in getting a resolution. This book details incidents of data crimes and their impact. Although not the person ...