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Data Driven
by Dj Patil Hilary MasonSucceeding with data isn’t just a matter of putting Hadoop in your machine room, or hiring some physicists with crazy math skills. It requires you to develop a data culture that involves people throughout the organization. In this O’Reilly report, DJ Patil and Hilary Mason outline the steps you need to take if your company is to be truly data-driven—including the questions you should ask and the methods you should adopt.You’ll not only learn examples of how Google, LinkedIn, and Facebook use their data, but also how Walmart, UPS, and other organizations took advantage of this resource long before the advent of Big Data. No matter how you approach it, building a data culture is the key to success in the 21st century.You’ll explore:Data scientist skills—and why every company needs a SpockHow the benefits of giving company-wide access to data outweigh the costsWhy data-driven organizations use the scientific method to explore and solve data problemsKey questions to help you develop a research-specific process for tackling important issuesWhat to consider when assembling your data teamDeveloping processes to keep your data team (and company) engagedChoosing technologies that are powerful, support teamwork, and easy to use and learn
Data Driven Approach Towards Disruptive Technologies: Proceedings of MIDAS 2020 (Studies in Autonomic, Data-driven and Industrial Computing)
by T. P. Singh Thinagaran Perumal Tanupriya Choudhury Ravi Tomar Hussain Falih MahdiThis book is a compilation of peer-reviewed papers presented at the International Conference on Machine Intelligence and Data Science Applications, organized by the School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India, during 4–5 September 2020. The book addresses the algorithmic aspect of machine intelligence which includes the framework and optimization of various states of algorithms. Variety of papers related to wide applications in various fields like data-driven industrial IoT, bioinformatics, network and security, autonomous computing and various other aligned areas. The book concludes with interdisciplinary applications like legal, health care, smart society, cyber-physical system and smart agriculture. All papers have been carefully reviewed. The book is of interest to computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.
Data Driven Approaches for Healthcare: Machine learning for Identifying High Utilizers (Chapman & Hall/CRC Big Data Series)
by Sanjay Ranka Chengliang Yang Chris Delcher Elizabeth ShenkmanHealth care utilization routinely generates vast amounts of data from sources ranging from electronic medical records, insurance claims, vital signs, and patient-reported outcomes. Predicting health outcomes using data modeling approaches is an emerging field that can reveal important insights into disproportionate spending patterns. This book presents data driven methods, especially machine learning, for understanding and approaching the high utilizers problem, using the example of a large public insurance program. It describes important goals for data driven approaches from different aspects of the high utilizer problem, and identifies challenges uniquely posed by this problem. Key Features: Introduces basic elements of health care data, especially for administrative claims data, including disease code, procedure codes, and drug codes Provides tailored supervised and unsupervised machine learning approaches for understanding and predicting the high utilizers Presents descriptive data driven methods for the high utilizer population Identifies a best-fitting linear and tree-based regression model to account for patients’ acute and chronic condition loads and demographic characteristics
Data Driven Approaches in Digital Education: 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, Tallinn, Estonia, September 12–15, 2017, Proceedings (Lecture Notes in Computer Science #10474)
by Katrien Verbert Hendrik Drachsler Élise Lavoué Mar Pérez-Sanagustín Julien BroisinThis book constitutes the proceedings of the 12th European Conference on Technology Enhanced Learning, EC-TEL 2017, held in Tallinn, Estonia, in September 2017. The 24 full papers, 23 short papers, 6 demo papers, and 22 poster papers presented in this volume were carefully reviewed and selected from 141 submissions. The theme for the 12th EC-TEL conference on Data Driven Approaches in Digital Education' aims to explore the multidisciplinary approaches that eectively illustrate how data-driven education combined with digital education systems can look like and what are the empirical evidences for the use of datadriven tools in educational practices.
Data Driven Approaches on Medical Imaging
by Bin Zheng Stefan Andrei Md Kamruzzaman Sarker Kishor Datta GuptaThis book deals with the recent advancements in computer vision techniques such as active learning, few-shot learning, zero shot learning, explainable and interpretable ML, online learning, AutoML etc. and their applications in medical domain. Moreover, the key challenges which affect the design, development, and performance of medical imaging systems are addressed. In addition, the state-of-the-art medical imaging methodologies for efficient, interpretable, explainable, and practical implementation of computer imaging techniques are discussed. At present, there are no textbook resources that address the medical imaging technologies. There are ongoing and novel research outcomes which would be useful for the development of novel medical imaging technologies/processes/equipment which can improve the current state of the art.The book particularly focuses on the use of data driven new technologies on medical imaging vision such as Active learning, Online learning, few shot learning, AutoML, segmentation etc.
Data Driven Decision Making using Analytics (Computational Intelligence Techniques)
by Parul GandhiThis book aims to explain Data Analytics towards decision making in terms of models and algorithms, theoretical concepts, applications, experiments in relevant domains or focused on specific issues. It explores the concepts of database technology, machine learning, knowledge-based system, high performance computing, information retrieval, finding patterns hidden in large datasets and data visualization. Also, it presents various paradigms including pattern mining, clustering, classification, and data analysis. Overall aim is to provide technical solutions in the field of data analytics and data mining. Features: Covers descriptive statistics with respect to predictive analytics and business analytics. Discusses different data analytics platforms for real-time applications. Explain SMART business models. Includes algorithms in data sciences alongwith automated methods and models. Explores varied challenges encountered by researchers and businesses in the realm of real-time analytics. This book aims at researchers and graduate students in data analytics, data sciences, data mining, and signal processing.
Data Driven Decisions: Systems Engineering to Understand Corporate Value and Intangible Assets
by Joshua JahaniExpand your enterprise into new regions using systems engineering and data analysis In Data Driven Decisions: Systems Engineering to Understand Corporate Valuation and Intangible Assets, investment banker, systems engineer, and Cornell University lecturer Joshua Michael Jahani delivers an incisive and unique unveiling of how to use the tools of systems engineering to value your organization, its intangible assets, and how to gauge or prepare its readiness for an overseas or cross-border expansion. In the book, you’ll learn to implement a wide range of systems engineering tools, including context diagrams, decision matrices, Goal-Question-Metric analyses, and more. You’ll also discover the following: How to communicate corporate value measurements and their impact to owners, executives, and investors. Explorations of the relevant topics when considering an international expansion, including macroeconomics, joint ventures, market entry, corporate valuations, mergers and acquisitions, and company culture. A comprehensive framework and methodology for examining available global regions in your search for the perfect expansion target. A deep understanding of specific sectors in which intangible assets have a particular impact, including branded consumer products, ad-tech, and healthcare.A must-have resource for business owners, managers, executives, directors, and other corporate leaders, Data-Driven Decisions will also prove invaluable to consultants and other professionals who serve companies considering expansion or growth into new regions.
Data Driven Mathematical Modeling in Agriculture: Tools and Technologies (River Publishers Series in Mathematical, Statistical and Computational Modelling for Engineering)
by Sandip Roy Sabyasachi Pramanik Rajesh BoseThe research in this book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers' choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models are utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies.Technical topics discussed in the book include: Precision agriculture Machine learning Wireless sensor networks IoT Deep learning
Data Driven Smart Manufacturing Technologies and Applications (Springer Series in Advanced Manufacturing)
by Weidong Li Sheng Wang Yuchen LiangThis book reports innovative deep learning and big data analytics technologies for smart manufacturing applications. In this book, theoretical foundations, as well as the state-of-the-art and practical implementations for the relevant technologies, are covered. This book details the relevant applied research conducted by the authors in some important manufacturing applications, including intelligent prognosis on manufacturing processes, sustainable manufacturing and human-robot cooperation. Industrial case studies included in this book illustrate the design details of the algorithms and methodologies for the applications, in a bid to provide useful references to readers. Smart manufacturing aims to take advantage of advanced information and artificial intelligent technologies to enable flexibility in physical manufacturing processes to address increasingly dynamic markets. In recent years, the development of innovative deep learning and big data analytics algorithms is dramatic. Meanwhile, the algorithms and technologies have been widely applied to facilitate various manufacturing applications. It is essential to make a timely update on this subject considering its importance and rapid progress. This book offers a valuable resource for researchers in the smart manufacturing communities, as well as practicing engineers and decision makers in industry and all those interested in smart manufacturing and Industry 4.0.
Data Driven Strategies: Theory and Applications
by Ricardo A. Ramirez-Mendoza Ruben Morales-Menendez Wang JianhongA key challenge in science and engineering is to provide a quantitative description of the systems under investigation, leveraging the noisy data collected. Such a description may be a complete mathematical model or a mechanism to return controllers corresponding to new, unseen inputs. Recent advances in the theories are described in detail, along with their applications in engineering. The book aims to develop model-free system analysis and control strategies, i.e., data-driven control from theoretical analysis and engineering applications based only on measured data. The study aims to develop system identification, and combination in advanced control theory, i.e., data-driven control strategy as system and controller are generated from measured data directly. The book reviews the development of system identification and its combination in advanced control theory, i.e., data-driven control strategy, as they all depend on measured data. Firstly, data-driven identification is developed for the closed-loop, nonlinear system and model validation, i.e., obtaining model descriptions from measured data. Secondly, the data-driven idea is combined with some control strategies to be considered data-driven control strategies, such as data-driven model predictive control, data-driven iterative tuning control, and data-driven subspace predictive control. Thirdly data-driven identification and data-driven control strategies are applied to interested engineering. In this context, the book provides algorithms to perform state estimation of dynamical systems from noisy data and some convex optimization algorithms through identification and control problems.
Data Driven: An Introduction to Management Consulting in the 21st Century (Management for Professionals)
by Jeremy David CuruksuThis book is a “scientific” introduction to management consulting that covers elementary and more advanced concepts, such as strategy and client-relationship. It discusses the emerging role of information technologies in consulting activities and introduces the essential tools in data science, assuming no technical background. Drawing on extensive literature reviews with more than 200 peer reviewed articles, reports, books and surveys referenced, this book has at least four objectives: to be scientific, modern, complete and concise. An interactive version of some sections (industry snapshots, method toolbox) is freely accessible at econsultingdata.com.
Data Driven: Truckers, Technology, and the New Workplace Surveillance
by Karen LevyA behind-the-scenes look at how digital surveillance is affecting the trucking way of lifeLong-haul truckers are the backbone of the American economy, transporting goods under grueling conditions and immense economic pressure. Truckers have long valued the day-to-day independence of their work, sharing a strong occupational identity rooted in a tradition of autonomy. Yet these workers increasingly find themselves under many watchful eyes. Data Driven examines how digital surveillance is upending life and work on the open road, and raises crucial questions about the role of data collection in broader systems of social control.Karen Levy takes readers inside a world few ever see, painting a bracing portrait of one of the last great American frontiers. Federal regulations now require truckers to buy and install digital monitors that capture data about their locations and behaviors. Intended to address the pervasive problem of trucker fatigue by regulating the number of hours driven each day, these devices support additional surveillance by trucking firms and other companies. Traveling from industry trade shows to law offices and truck-stop bars, Levy reveals how these invasive technologies are reconfiguring industry relationships and providing new tools for managerial and legal control—and how truckers are challenging and resisting them.Data Driven contributes to an emerging conversation about how technology affects our work, institutions, and personal lives, and helps to guide our thinking about how to protect public interests and safeguard human dignity in the digital age.
Data Economy in the Digital Age (Data-Intensive Research)
by Samiksha Shukla Kapil Tiwari Shahid Bashir Kritica BishtThe book is a comprehensive guide that explores the concept of data economy and its implications in today's world. The book discusses the principles and components of the ecosystem, the challenges and opportunities presented by data monetization, and the potential risks related to data privacy. Real-life examples and case studies are included to understand the concepts better. The book is suitable for individuals in data science, economics, business, and technology and for students, academics, and policymakers. It is an excellent read for anyone interested in the data economy.
Data Engineering Design Patterns: Recipes for Solving the Most Common Data Engineering Problems
by Bartosz KoniecznyData projects are an intrinsic part of an organization’s technical ecosystem, but data engineers in many companies continue to work on problems that others have already solved. This hands-on guide shows you how to provide valuable data by focusing on various aspects of data engineering, including data ingestion, data quality, idempotency, and more.Author Bartosz Konieczny guides you through the process of building reliable end-to-end data engineering projects, from data ingestion to data observability, focusing on data engineering design patterns that solve common business problems in a secure and storage-optimized manner. Each pattern includes a user-facing description of the problem, solutions, and consequences that place the pattern into the context of real-life scenarios.Throughout this journey, you’ll use open source data tools and public cloud services to apply each pattern. You'll learn:Challenges data engineers face and their impact on data systemsHow these challenges relate to data system componentsUseful applications of data engineering patternsHow to identify and fix issues with your current data componentsTTechnology-agnostic solutions to new and existing data projects, with open source implementation examplesBartosz Konieczny is a freelance data engineer who's been coding since 2010. He's held various senior hands-on positions that allowed him to work on many data engineering problems in batch and stream processing.
Data Engineering and Applications: Proceedings of the International Conference, IDEA 2K22, Volume 1 (Lecture Notes in Electrical Engineering #1146)
by Jitendra Agrawal Sanjeev Sharma Rajesh K. Shukla Chin-Shiuh ShiehThis book comprises select proceedings from the 4th International Conference on Data, Engineering, and Applications (IDEA 2022). The contents discuss novel contributions and latest developments in the domains of data structures and data management algorithms, information retrieval and information integration, social data analytics, IoT and data intelligence, Industry 4.0 and digital manufacturing, data fusion, natural language processing, geolocation handling, image, video and signal processing, ICT applications and e-governance, among others. This book is of interest to researchers in academia and industry working in big data, data mining, machine learning, data science, and their associated learning systems and applications.
Data Engineering and Applications: Proceedings of the International Conference, IDEA 2K22, Volume 2 (Lecture Notes in Electrical Engineering #1189)
by Jitendra Agrawal Sanjeev Sharma Rajesh K. Shukla Chin-Shiuh ShiehThis book comprises select proceedings from the 4th International Conference on Data, Engineering, and Applications (IDEA 2022). The contents discuss novel contributions and latest developments in the domains of data structures and data management algorithms, information retrieval and information integration, social data analytics, IoT and data intelligence, Industry 4.0 and digital manufacturing, data fusion, natural language processing, geolocation handling, image, video and signal processing, ICT applications and e-governance, among others. This book is of interest to researchers in academia and industry working in big data, data mining, machine learning, data science, and their associated learning systems and applications.
Data Engineering and Communication Technology: Proceedings of ICDECT 2020 (Lecture Notes on Data Engineering and Communications Technologies #63)
by K. Srujan Raju Boby George K. Ashoka Reddy B. Rama DeviThis book includes selected papers presented at the 4th International Conference on Data Engineering and Communication Technology (ICDECT 2020), held at Kakatiya Institute of Technology & Science, Warangal, India, during 25–26 September 2020. It features advanced, multidisciplinary research towards the design of smart computing, information systems and electronic systems. It also focuses on various innovation paradigms in system knowledge, intelligence and sustainability which can be applied to provide viable solutions to diverse problems related to society, the environment and industry.
Data Engineering and Intelligent Computing: Proceedings of 5th ICICC 2021, Volume 1 (Lecture Notes in Networks and Systems #446)
by Suresh Chandra Satapathy Vikrant Bhateja Jerry Chun-Wei Lin Lai Khin Wee T. M. RajeshThis book features a collection of high-quality, peer-reviewed papers presented at the Fifth International Conference on Intelligent Computing and Communication (ICICC 2021) organized by the Department of Computer Science and Engineering and the Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India, on 26–27 November 2021. The book is organized in two volumes and discusses advanced and multi-disciplinary research regarding the design of smart computing and informatics. It focuses on innovation paradigms in system knowledge, intelligence and sustainability that can be applied to provide practical solutions to a number of problems in society, the environment and industry. Further, the book also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology and health care.
Data Engineering and Intelligent Computing: Proceedings of IC3T 2016 (Advances in Intelligent Systems and Computing #542)
by Suresh Chandra Satapathy Vikrant Bhateja K. Srujan Raju B. JanakiramaiahThe book is a compilation of high-quality scientific papers presented at the 3rd International Conference on Computer & Communication Technologies (IC3T 2016). The individual papers address cutting-edge technologies and applications of soft computing, artificial intelligence and communication. In addition, a variety of further topics are discussed, which include data mining, machine intelligence, fuzzy computing, sensor networks, signal and image processing, human-computer interaction, web intelligence, etc. As such, it offers readers a valuable and unique resource.
Data Engineering and Intelligent Computing: Proceedings of ICICC 2020 (Advances in Intelligent Systems and Computing #1)
by Suresh Chandra Satapathy Vikrant Bhateja V. N. Manjunath Aradhya Carlos M. Travieso-GonzálezThis book features a collection of high-quality, peer-reviewed papers presented at the Fourth International Conference on Intelligent Computing and Communication (ICICC 2020) organized by the Department of Computer Science and Engineering and the Department of Computer Science and Technology, Dayananda Sagar University, Bengaluru, India, on 18–20 September 2020. The book is organized in two volumes and discusses advanced and multi-disciplinary research regarding the design of smart computing and informatics. It focuses on innovation paradigms in system knowledge, intelligence and sustainability that can be applied to provide practical solutions to a number of problems in society, the environment and industry. Further, the book also addresses the deployment of emerging computational and knowledge transfer approaches, optimizing solutions in various disciplines of science, technology and health care.
Data Engineering for Machine Learning Pipelines: From Python Libraries to ML Pipelines and Cloud Platforms
by Pavan Kumar NarayananThis book covers modern data engineering functions and important Python libraries, to help you develop state-of-the-art ML pipelines and integration code. The book begins by explaining data analytics and transformation, delving into the Pandas library, its capabilities, and nuances. It then explores emerging libraries such as Polars and CuDF, providing insights into GPU-based computing and cutting-edge data manipulation techniques. The text discusses the importance of data validation in engineering processes, introducing tools such as Great Expectations and Pandera to ensure data quality and reliability. The book delves into API design and development, with a specific focus on leveraging the power of FastAPI. It covers authentication, authorization, and real-world applications, enabling you to construct efficient and secure APIs using FastAPI. Also explored is concurrency in data engineering, examining Dask's capabilities from basic setup to crafting advanced machine learning pipelines. The book includes development and delivery of data engineering pipelines using leading cloud platforms such as AWS, Google Cloud, and Microsoft Azure. The concluding chapters concentrate on real-time and streaming data engineering pipelines, emphasizing Apache Kafka and workflow orchestration in data engineering. Workflow tools such as Airflow and Prefect are introduced to seamlessly manage and automate complex data workflows. What sets this book apart is its blend of theoretical knowledge and practical application, a structured path from basic to advanced concepts, and insights into using state-of-the-art tools. With this book, you gain access to cutting-edge techniques and insights that are reshaping the industry. This book is not just an educational tool. It is a career catalyst, and an investment in your future as a data engineering expert, poised to meet the challenges of today's data-driven world. What You Will Learn Elevate your data wrangling jobs by utilizing the power of both CPU and GPU computing, and learn to process data using Pandas 2.0, Polars, and CuDF at unprecedented speeds Design data validation pipelines, construct efficient data service APIs, develop real-time streaming pipelines and master the art of workflow orchestration to streamline your engineering projects Leverage concurrent programming to develop machine learning pipelines and get hands-on experience in development and deployment of machine learning pipelines across AWS, GCP, and Azure Who This Book Is For Data analysts, data engineers, data scientists, machine learning engineers, and MLOps specialists
Data Engineering for Smart Systems: Proceedings of SSIC 2021 (Lecture Notes in Networks and Systems #238)
by Sumit Srivastava Vivek Kumar Verma Priyadarsi Nanda Rohit Kumar Gupta Arka Prokash MazumdarThis book features original papers from the 3rd International Conference on Smart IoT Systems: Innovations and Computing (SSIC 2021), organized by Manipal University, Jaipur, India, during January 22–23, 2021. It discusses scientific works related to data engineering in the context of computational collective intelligence consisted of interaction between smart devices for smart environments and interactions. Thanks to the high-quality content and the broad range of topics covered, the book appeals to researchers pursuing advanced studies.
Data Engineering in Medical Imaging: First MICCAI Workshop, DEMI 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, Proceedings (Lecture Notes in Computer Science #14314)
by Danail Stoyanov Sharib Ali Binod Bhattarai Anita Rau Anh Nguyen Ana Namburete Razvan CaramalauVolume LNCS 14414 constitutes the refereed proceedings of the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, which was held in Vancouver, Canada in October 2023.The DEMI 2023 proceedings contain 11 high-quality papers of 9 to 15 pages pre-selected through a rigorous peer review process (with an average of three reviews per paper). All submissions were peer-reviewed through a double-blind process by at least three members of the scientific review committee, comprising 16 experts in the field of medical imaging. The accepted manuscripts cover various medical image analysis methods and applications.
Data Engineering in Medical Imaging: Second MICCAI Workshop, DEMI 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 10, 2024, Proceedings (Lecture Notes in Computer Science #15265)
by Danail Stoyanov Sharib Ali Binod Bhattarai Anita Rau Anh Nguyen Ana Namburete Razvan Caramalau Prashnna GyawaliThis book constitutes the proceedings of the Second MICCAI Workshop on Data Engineering in Medical Imaging, DEMI 2024, held in conjunction with the 27th International conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2024, in Marrakesh, Morocco, on October 10, 2024. The 18 papers presented in this book were carefully reviewed and selected. These papers focus on the application of various Data engineering techniques in the field of Medical Imaging.
Data Engineering on Azure
by Vlad RiscutiaBuild a data platform to the industry-leading standards set by Microsoft&’s own infrastructure.Summary In Data Engineering on Azure you will learn how to: Pick the right Azure services for different data scenarios Manage data inventory Implement production quality data modeling, analytics, and machine learning workloads Handle data governance Using DevOps to increase reliability Ingesting, storing, and distributing data Apply best practices for compliance and access control Data Engineering on Azure reveals the data management patterns and techniques that support Microsoft&’s own massive data infrastructure. Author Vlad Riscutia, a data engineer at Microsoft, teaches you to bring an engineering rigor to your data platform and ensure that your data prototypes function just as well under the pressures of production. You'll implement common data modeling patterns, stand up cloud-native data platforms on Azure, and get to grips with DevOps for both analytics and machine learning. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Build secure, stable data platforms that can scale to loads of any size. When a project moves from the lab into production, you need confidence that it can stand up to real-world challenges. This book teaches you to design and implement cloud-based data infrastructure that you can easily monitor, scale, and modify. About the book In Data Engineering on Azure you&’ll learn the skills you need to build and maintain big data platforms in massive enterprises. This invaluable guide includes clear, practical guidance for setting up infrastructure, orchestration, workloads, and governance. As you go, you&’ll set up efficient machine learning pipelines, and then master time-saving automation and DevOps solutions. The Azure-based examples are easy to reproduce on other cloud platforms. What's inside Data inventory and data governance Assure data quality, compliance, and distribution Build automated pipelines to increase reliability Ingest, store, and distribute data Production-quality data modeling, analytics, and machine learning About the reader For data engineers familiar with cloud computing and DevOps. About the author Vlad Riscutia is a software architect at Microsoft. Table of Contents 1 Introduction PART 1 INFRASTRUCTURE 2 Storage 3 DevOps 4 Orchestration PART 2 WORKLOADS 5 Processing 6 Analytics 7 Machine learning PART 3 GOVERNANCE 8 Metadata 9 Data quality 10 Compliance 11 Distributing data