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Karau Holden: High Performance Spark

High Performance Spark

Best Practices for Scaling and Optimizing Apache Spark

Sustainable Supply Chain Management: Computational Approaches and Modelling provides a comprehensive understanding of how computational approaches can be leveraged to address the challenges associated with sustainable supply chain management. It offers insights into emerging and advanced modelling techniques to optimize supply chain operations while considering environmental, social, and economic factors. This book will be a valuable resource for researchers, practitioners, and policymakers interested in understanding and implementing sustainable supply chain management practices through computational approaches and modelling. This book helps researchers understand the opportunities, challenges, and solutions by developing innovative computational tools and techniques to integrate sustainability considerations into supply chain decision-making processes.

Computational Approaches and Modelling

Sustainable Supply Chain Management

Ashgate Series in Nineteenth-Century Transatlantic Studies

Data Driven Applications for Industry 4.0 and Beyond

This is the first work to explore the theory, history and processes of physical representations of data

Materializing Digital Information

Making Data

This book illustrates statistics with the R programming language, an emerging tool in data science. Filled with techniques for data science and analytics programming, the book is written for students and professionals. It is focused on learning outcomes and problem solving.

This book illustrates statistics with the R programming language, an emerging tool in data science. Filled with techniques for data science and analytics programming, the book is written for students and professionals. It is focused on learning outcomes and problem solving.

Demystifying Probability and Statistics for Data Scientists with R

Data Governance: How to Design, Deploy, and Sustain an Effective Data Governance Program, Third Edition, is for any student, executive, manager, or data professional who needs to understand and implement a data governance program, to ensure consistent, accurate and reliable data across their organization. The problem of managing data continues to grow with issues surrounding the cost of storage, exponential growth, as well as administrative, management, and security concerns - the solution to being able to scale all of these issues up is data governance which provides better services to users and saves money. This book offers an overview of why data governance is needed, how to design, initiate, and execute a program and how to keep the program sustainable. John Ladley guides readers though the detailed process of developing a Data Governance program, including an introduction to the concepts and principles of Data Governance, Data literacy concepts, business case, strategy, systems architecture and design, the importance of team engagement, implementation, and change management. The Third Edition includes all-new coverage embracing new data capabilities such as ethics and AI integration, as well as key regulatory changes. Data governance is now inevitable, and no longer an option. John Ladley examines the impact of society on data and the relevance for organizations to simply "do better." The Third Edition updates coverage on data governance tools to achieve delivery of necessary capabilities. This valuable resource provides foundational understanding to advanced students in Data Management and related courses, as well as comprehensive guidance for beginning professionals, managers or analysts looking to improve their processes. With the provided framework and case studies, all students and professionals in the data governance field will gain key insights into launching successful and money-saving data governance programs.

How to Design, Deploy, and Sustain an Effective Data Governance Program

Data Governance

Microsoft Power BI For Dummies

Data-driven decision-making is a fundamental component of business success. Use this textbook to learn the core knowledge and techniques for analyzing business data with Python programming.

Business Analytics with Python assumes no prior knowledge or experience in computer science, presenting the technical aspects of the subject in an accessible, introductory way for students on business courses. It features chapters on linear regression, neural networks and cluster analysis, with a running case study that enables students to apply their knowledge. Students will also benefit from real-life examples to show how business analysis has been used for such tasks as customer churn prediction, credit card fraud detection and sales forecasting. This book presents a holistic approach to business analytics: in addition to Python, it covers mathematical and statistical concepts, essential machine learning methods and their applications. Business Analytics with Python comes complete with practical exercises and activities, learning objectives and chapter summaries as well as self-test quizzes. It is supported by online resources that include lecturer PowerPoint slides, study guides, sample code and datasets and interactive worksheets. This textbook is ideal for students taking upper level undergraduate and postgraduate modules on analytics as part of their business, management or finance degrees.

Essential Skills for Business Students

Business Analytics with Python

Bloomsbury Studies in Digital Cultures

Personalisation in Digital Culture, Healthcare and Data Science

People Like You

Data-Driven Insights and Analytics for Measurable Sustainable Development Goals discusses the growing imperative to understand, measure, and guide actions using data-driven insights. The SDGs encompass a broad spectrum of global challenges, from eradicating poverty and hunger to preserving the environment and fostering peace. To address these issues, one should be able to measure and analyze progress. This book bridges the gap between qualitative and quantitative assessments, recognizing that goals are not solely about numbers but also encompass complex social, environmental, and economic dynamics. By merging data science with qualitative analysis, readers can explore how SDGs intersect and influence each other. The book provides readers with an understanding of how to effectively leverage data science models and algorithms using descriptive analytics, allowing us to assess the current state of SDG performance and offering valuable insights into where we stand on these critical goals. Prescriptive analytics guides actions by offering actionable recommendations, while predictive analytics anticipates future trends and challenges, helping us navigate our path toward the SDGs effectively.

Data-Driven Insights and Analytics for Measurable Sustainable Development Goals

Increase efficiency, improve profitability and reduce spend and become more confident handling and analyzing customer data for further insight.

Strategies for Accuracy and Efficiency

Optimizing Sales and Marketing Data

Intelligent Data-Centric Systems

Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT) is a reference for IT engineers and decision-makers who may engage in IoT platform pilot projects. The resources covered in this book help establish plans for sustainable operations and management and assist with the long-term procurement of relevant IoT technologies. The aim of the book is to be exhaustive and holistic by pointing out numerous issues and related solution options that guide with daily challenges when deploying and running IoT platforms. The book is divided into three parts where each part includes relevant theoretical chapters and applied case studies. Part One focuses on architectural and federation options for the design and implementation of IoT platforms that foster strategic collaboration opportunities. Part Two addresses vertical security challenges across IoT platform layers. Finally, Part Three shows how IoT is driving the digital transformation wheel through existing and forthcoming case studies.

Accelerating Digital Transformation with the Cloud and the Internet of Things (IoT)

Data Driven Applications for Emerging Technologies

High Performance Spark

Weitere Titel zum Thema