Data science and big data analytics: discovering, analyzing, visualizing and presenting data

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science teamDeploy a structured lifecycle approach to data analytics problemsApply appropriate analytic techniques an

Information Storage and Management: Storing, Managing, and Protecting Digital Information in Classic, Virtualized, and Cloud Environments

The new edition of a bestseller, now revised and update throughout! This new edition of the unparalleled bestseller serves as a full training course all in one and as the world's largest data storage company, EMC is the ideal author for such a critical resource. They cover the components of a storage system and the different storage system models while also offering essential new material that explores the advances in existing technologies and the emergence of the "Cloud" as well as updates and vital information on new technologies. Features a separate section on emerging area of cloud comput

Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data

Data Science and Big Data Analytics is about harnessing the power of data for new insights. The book covers the breadth of activities and methods and tools that Data Scientists use. The content focuses on concepts, principles and practical applications that are applicable to any industry and technology environment, and the learning is supported and explained with examples that you can replicate using open-source software. This book will help you: Become a contributor on a data science team Deploy a structured lifecycle approach to data analytics problems Apply appropriate analytic techniques and tools to analyzing big data Learn how to tell a compelling story with data to drive business action Prepare for EMC Proven Professional Data Science Certification Corresponding data sets are available at www.wiley.com/go/9781118876138. Get started discovering, analyzing, visualizing, and presenting data in a meaningful way today

On big data-guided upstream business research and its knowledge management

© 2018 The emerging Big Data integration imposes diverse challenges, compromising the sustainable business research practice. Heterogeneity, multi-dimensionality, velocity, and massive volumes that challenge Big Data paradigm may preclude the effective data and system integration processes. Business alignments get affected within and across joint ventures as enterprises attempt to adapt to changes in industrial environments rapidly. In the context of the Oil and Gas industry, we design integrated artefacts for a resilient multidimensional warehouse repository. With access to several decades of resource data in upstream companies, we incorporate knowledge-based data models with spatial-temporal dimensions in data schemas to minimize ambiguity in warehouse repository implementation. The design considerations ensure uniqueness and monotonic properties of dimensions, maintaining the connectivity between artefacts and achieving the business alignments. The multidimensional attributes envisage Big Data analysts a scope of business research with valuable new knowledge for decision support systems and adding further business values in geographic scales