Practical Applications Of Data Processing Algorithms And Modeling

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Practical Applications of Data Processing, Algorithms, and Modeling

Author : Whig, Pawan
Publisher : IGI Global
ISBN 13 :
Total Pages : 334 pages
Book Rating : 4.3/5 (693 download)

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Book Synopsis Practical Applications of Data Processing, Algorithms, and Modeling by : Whig, Pawan

Download or read book Practical Applications of Data Processing, Algorithms, and Modeling written by Whig, Pawan and published by IGI Global. This book was released on 2024-04-29 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: In today's data-driven era, the persistent gap between theoretical understanding and practical implementation in data science poses a formidable challenge. As we navigate through the complexities of harnessing data, deciphering algorithms, and unleashing the potential of modeling techniques, the need for a comprehensive guide becomes increasingly evident. This is the landscape explored in Practical Applications of Data Processing, Algorithms, and Modeling. This book is a solution to the pervasive problem faced by aspiring data scientists, seasoned professionals, and anyone fascinated by the power of data-driven insights. From the web of algorithms to the strategic role of modeling in decision-making, this book is an effective resource in a landscape where data, without proper guidance, risks becoming an untapped resource. The objective of Practical Applications of Data Processing, Algorithms, and Modeling is to address the pressing issue at the heart of data science – the divide between theory and practice. This book seeks to examine the complexities of data processing techniques, algorithms, and modeling methodologies, offering a practical understanding of these concepts. By focusing on real-world applications, the book provides readers with the tools and knowledge needed to bridge the gap effectively, allowing them to apply these techniques across diverse industries and domains. In the face of constant technological advancements, the book highlights the latest trends and innovative approaches, fostering a deeper comprehension of how these technologies can be leveraged to solve complex problems. As a practical guide, it empowers readers with hands-on examples, case studies, and problem-solving scenarios, aiming to instill confidence in navigating data challenges and making informed decisions using data-driven insights.

Applications of Big Data Analytics

Author : Mohammed M. Alani
Publisher : Springer
ISBN 13 : 3319764721
Total Pages : 214 pages
Book Rating : 4.3/5 (197 download)

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Book Synopsis Applications of Big Data Analytics by : Mohammed M. Alani

Download or read book Applications of Big Data Analytics written by Mohammed M. Alani and published by Springer. This book was released on 2018-07-23 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: This timely text/reference reviews the state of the art of big data analytics, with a particular focus on practical applications. An authoritative selection of leading international researchers present detailed analyses of existing trends for storing and analyzing big data, together with valuable insights into the challenges inherent in current approaches and systems. This is further supported by real-world examples drawn from a broad range of application areas, including healthcare, education, and disaster management. The text also covers, typically from an application-oriented perspective, advances in data science in such areas as big data collection, searching, analysis, and knowledge discovery. Topics and features: Discusses a model for data traffic aggregation in 5G cellular networks, and a novel scheme for resource allocation in 5G networks with network slicing Explores methods that use big data in the assessment of flood risks, and apply neural networks techniques to monitor the safety of nuclear power plants Describes a system which leverages big data analytics and the Internet of Things in the application of drones to aid victims in disaster scenarios Proposes a novel deep learning-based health data analytics application for sleep apnea detection, and a novel pathway for diagnostic models of headache disorders Reviews techniques for educational data mining and learning analytics, and introduces a scalable MapReduce graph partitioning approach for high degree vertices Presents a multivariate and dynamic data representation model for the visualization of healthcare data, and big data analytics methods for software reliability assessment This practically-focused volume is an invaluable resource for all researchers, academics, data scientists and business professionals involved in the planning, designing, and implementation of big data analytics projects. Dr. Mohammed M. Alani is an Associate Professor in Computer Engineering and currently is the Provost at Al Khawarizmi International College, Abu Dhabi, UAE. Dr. Hissam Tawfik is a Professor of Computer Science in the School of Computing, Creative Technologies & Engineering at Leeds Beckett University, UK. Dr. Mohammed Saeed is a Professor in Computing and currently is the Vice President for Academic Affairs and Research at the University of Modern Sciences, Dubai, UAE. Dr. Obinna Anya is a Research Staff Member at IBM Research – Almaden, San Jose, CA, USA.

Radar Data Processing With Applications

Author : He You
Publisher : John Wiley & Sons
ISBN 13 : 1118956893
Total Pages : 560 pages
Book Rating : 4.1/5 (189 download)

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Book Synopsis Radar Data Processing With Applications by : He You

Download or read book Radar Data Processing With Applications written by He You and published by John Wiley & Sons. This book was released on 2016-08-01 with total page 560 pages. Available in PDF, EPUB and Kindle. Book excerpt: A systematic introduction to the theory, development and latest research results of radar data processing technology • Presents both classical theory and development methods of radar data processing • Provides state-of-the-art research results, including data processing for modern style radars, and tracking performance evaluation theory • Includes coverage of performance evaluation, registration algorithm for Radar network, data processing of passive radar, pulse Doppler radar, and phased array radar • Has applications for those engaged in information engineering, radar engineering, electronic countermeasures, infrared techniques, sonar techniques, and military command

Big Data

Author : Kuan-Ching Li
Publisher : Chapman and Hall/CRC
ISBN 13 : 9781482240559
Total Pages : 0 pages
Book Rating : 4.2/5 (45 download)

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Book Synopsis Big Data by : Kuan-Ching Li

Download or read book Big Data written by Kuan-Ching Li and published by Chapman and Hall/CRC. This book was released on 2015-02-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: As today’s organizations are capturing exponentially larger amounts of data than ever, now is the time for organizations to rethink how they digest that data. Through advanced algorithms and analytics techniques, organizations can harness this data, discover hidden patterns, and use the newly acquired knowledge to achieve competitive advantages. Presenting the contributions of leading experts in their respective fields, Big Data: Algorithms, Analytics, and Applications bridges the gap between the vastness of Big Data and the appropriate computational methods for scientific and social discovery. It covers fundamental issues about Big Data, including efficient algorithmic methods to process data, better analytical strategies to digest data, and representative applications in diverse fields, such as medicine, science, and engineering. The book is organized into five main sections: Big Data Management—considers the research issues related to the management of Big Data, including indexing and scalability aspects Big Data Processing—addresses the problem of processing Big Data across a wide range of resource-intensive computational settings Big Data Stream Techniques and Algorithms—explores research issues regarding the management and mining of Big Data in streaming environments Big Data Privacy—focuses on models, techniques, and algorithms for preserving Big Data privacy Big Data Applications—illustrates practical applications of Big Data across several domains, including finance, multimedia tools, biometrics, and satellite Big Data processing Overall, the book reports on state-of-the-art studies and achievements in algorithms, analytics, and applications of Big Data. It provides readers with the basis for further efforts in this challenging scientific field that will play a leading role in next-generation database, data warehousing, data mining, and cloud computing research. It also explores related applications in diverse sectors, covering technologies for media/data communication, elastic media/data storage, cross-network media/data fusion, and SaaS.

Unveiling Machine Learning: Theory, Algorithms and Practical Applications

Author : Dr.Padmaja Pulicherla
Publisher : SK Research Group of Companies
ISBN 13 : 8119980727
Total Pages : 221 pages
Book Rating : 4.1/5 (199 download)

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Book Synopsis Unveiling Machine Learning: Theory, Algorithms and Practical Applications by : Dr.Padmaja Pulicherla

Download or read book Unveiling Machine Learning: Theory, Algorithms and Practical Applications written by Dr.Padmaja Pulicherla and published by SK Research Group of Companies. This book was released on 2024-05-02 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dr.Padmaja Pulicherla, Professor, Department of Computer Science and Engineering, Hyderabad Institute of Technology and Management, Affiliated to JNTU, Hyderabad, Telangana, India. Dr.Kasarla Satish Reddy, Professor, Department of Electronics and Communication Engineering, Hyderabad Institute of Technology and Management, Affiliated to JNTU, Hyderabad, Telangana, India. D.Satyanarayana, Assistant Professor, Department of Computer Science and Engineering(DS), Santhiram Engineering College(Autonomous), Nandyal, Andhra Pradesh, India. Dr.R.Sudheer Babu, Associate Professor, Department of Electronics and Communication Engineering, G.Pulla Reddy Engineering College (Autonomous), Kurnool, Andhra Pradesh, India. Dr.Ravi Babu Devareddi, Assistant Professor, Department of Computer Science and Engineering, SRKR Engineering College, Bhimavaram, Andhra Pradesh, India.

Knowledge Graphs and Big Data Processing

Author : Valentina Janev
Publisher : Springer Nature
ISBN 13 : 3030531996
Total Pages : 212 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Knowledge Graphs and Big Data Processing by : Valentina Janev

Download or read book Knowledge Graphs and Big Data Processing written by Valentina Janev and published by Springer Nature. This book was released on 2020-07-15 with total page 212 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book is part of the LAMBDA Project (Learning, Applying, Multiplying Big Data Analytics), funded by the European Union, GA No. 809965. Data Analytics involves applying algorithmic processes to derive insights. Nowadays it is used in many industries to allow organizations and companies to make better decisions as well as to verify or disprove existing theories or models. The term data analytics is often used interchangeably with intelligence, statistics, reasoning, data mining, knowledge discovery, and others. The goal of this book is to introduce some of the definitions, methods, tools, frameworks, and solutions for big data processing, starting from the process of information extraction and knowledge representation, via knowledge processing and analytics to visualization, sense-making, and practical applications. Each chapter in this book addresses some pertinent aspect of the data processing chain, with a specific focus on understanding Enterprise Knowledge Graphs, Semantic Big Data Architectures, and Smart Data Analytics solutions. This book is addressed to graduate students from technical disciplines, to professional audiences following continuous education short courses, and to researchers from diverse areas following self-study courses. Basic skills in computer science, mathematics, and statistics are required.

Risk Modeling

Author : Terisa Roberts
Publisher : John Wiley & Sons
ISBN 13 : 111982494X
Total Pages : 214 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Risk Modeling by : Terisa Roberts

Download or read book Risk Modeling written by Terisa Roberts and published by John Wiley & Sons. This book was released on 2022-09-20 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: A wide-ranging overview of the use of machine learning and AI techniques in financial risk management, including practical advice for implementation Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning introduces readers to the use of innovative AI technologies for forecasting and evaluating financial risks. Providing up-to-date coverage of the practical application of current modelling techniques in risk management, this real-world guide also explores new opportunities and challenges associated with implementing machine learning and artificial intelligence (AI) into the risk management process. Authors Terisa Roberts and Stephen Tonna provide readers with a clear understanding about the strengths and weaknesses of machine learning and AI while explaining how they can be applied to both everyday risk management problems and to evaluate the financial impact of extreme events such as global pandemics and changes in climate. Throughout the text, the authors clarify misconceptions about the use of machine learning and AI techniques using clear explanations while offering step-by-step advice for implementing the technologies into an organization's risk management model governance framework. This authoritative volume: Highlights the use of machine learning and AI in identifying procedures for avoiding or minimizing financial risk Discusses practical tools for assessing bias and interpretability of resultant models developed with machine learning algorithms and techniques Covers the basic principles and nuances of feature engineering and common machine learning algorithms Illustrates how risk modeling is incorporating machine learning and AI techniques to rapidly consume complex data and address current gaps in the end-to-end modelling lifecycle Explains how proprietary software and open-source languages can be combined to deliver the best of both worlds: for risk models and risk practitioners Risk Modeling: Practical Applications of Artificial Intelligence, Machine Learning, and Deep Learning is an invaluable guide for CEOs, CROs, CFOs, risk managers, business managers, and other professionals working in risk management.

2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems

Author : Chuanchao Huang
Publisher : Springer Nature
ISBN 13 : 9811617260
Total Pages : 1669 pages
Book Rating : 4.8/5 (116 download)

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Book Synopsis 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems by : Chuanchao Huang

Download or read book 2020 International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems written by Chuanchao Huang and published by Springer Nature. This book was released on 2021-06-01 with total page 1669 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers cutting-edge and advanced research on data processing techniques and applications for cyber-physical systems, gathering the proceedings of the International Conference on Data Processing Techniques and Applications for Cyber-Physical Systems (DPTA 2020), held in Laibin City, Guangxi Province, China, on December 11–12, 2020. It examines a wide range of topics, including distributed processing for sensor data in CPS networks; approximate reasoning and pattern recognition for CPS networks; data platforms for efficient integration with CPS networks; machine learning algorithms for CPS networks; and data security and privacy in CPS networks. Outlining promising future research directions, the book offers a valuable resource for students, researchers, and professionals alike, while also providing a useful reference guide for newcomers to the field.

Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion

Author : Christian Servin
Publisher : Springer
ISBN 13 : 3319126288
Total Pages : 112 pages
Book Rating : 4.3/5 (191 download)

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Book Synopsis Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion by : Christian Servin

Download or read book Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion written by Christian Servin and published by Springer. This book was released on 2014-11-20 with total page 112 pages. Available in PDF, EPUB and Kindle. Book excerpt: On various examples ranging from geosciences to environmental sciences, this book explains how to generate an adequate description of uncertainty, how to justify semiheuristic algorithms for processing uncertainty, and how to make these algorithms more computationally efficient. It explains in what sense the existing approach to uncertainty as a combination of random and systematic components is only an approximation, presents a more adequate three-component model with an additional periodic error component, and explains how uncertainty propagation techniques can be extended to this model. The book provides a justification for a practically efficient heuristic technique (based on fuzzy decision-making). It explains how the computational complexity of uncertainty processing can be reduced. The book also shows how to take into account that in real life, the information about uncertainty is often only partially known, and, on several practical examples, explains how to extract the missing information about uncertainty from the available data.

Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis

Author : Sujit Rokka Chhetri
Publisher : Springer Nature
ISBN 13 : 3030379620
Total Pages : 240 pages
Book Rating : 4.0/5 (33 download)

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Book Synopsis Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis by : Sujit Rokka Chhetri

Download or read book Data-Driven Modeling of Cyber-Physical Systems using Side-Channel Analysis written by Sujit Rokka Chhetri and published by Springer Nature. This book was released on 2020-02-08 with total page 240 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS.

Practical Applications of Sparse Modeling

Author : Irina Rish
Publisher : MIT Press
ISBN 13 : 0262027720
Total Pages : 265 pages
Book Rating : 4.2/5 (62 download)

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Book Synopsis Practical Applications of Sparse Modeling by : Irina Rish

Download or read book Practical Applications of Sparse Modeling written by Irina Rish and published by MIT Press. This book was released on 2014-09-12 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: Key approaches in the rapidly developing area of sparse modeling, focusing on its application in fields including neuroscience, computational biology, and computer vision. Sparse modeling is a rapidly developing area at the intersection of statistical learning and signal processing, motivated by the age-old statistical problem of selecting a small number of predictive variables in high-dimensional datasets. This collection describes key approaches in sparse modeling, focusing on its applications in fields including neuroscience, computational biology, and computer vision. Sparse modeling methods can improve the interpretability of predictive models and aid efficient recovery of high-dimensional unobserved signals from a limited number of measurements. Yet despite significant advances in the field, a number of open issues remain when sparse modeling meets real-life applications. The book discusses a range of practical applications and state-of-the-art approaches for tackling the challenges presented by these applications. Topics considered include the choice of method in genomics applications; analysis of protein mass-spectrometry data; the stability of sparse models in brain imaging applications; sequential testing approaches; algorithmic aspects of sparse recovery; and learning sparse latent models. Contributors A. Vania Apkarian, Marwan Baliki, Melissa K. Carroll, Guillermo A. Cecchi, Volkan Cevher, Xi Chen, Nathan W. Churchill, Rémi Emonet, Rahul Garg, Zoubin Ghahramani, Lars Kai Hansen, Matthias Hein, Katherine Heller, Sina Jafarpour, Seyoung Kim, Mladen Kolar, Anastasios Kyrillidis, Seunghak Lee, Aurelie Lozano, Matthew L. Malloy, Pablo Meyer, Shakir Mohamed, Alexandru Niculescu-Mizil, Robert D. Nowak, Jean-Marc Odobez, Peter M. Rasmussen, Irina Rish, Saharon Rosset, Martin Slawski, Stephen C. Strother, Jagannadan Varadarajan, Eric P. Xing

Practical Machine Learning with Python

Author : Dipanjan Sarkar
Publisher : Apress
ISBN 13 : 1484232070
Total Pages : 545 pages
Book Rating : 4.4/5 (842 download)

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Book Synopsis Practical Machine Learning with Python by : Dipanjan Sarkar

Download or read book Practical Machine Learning with Python written by Dipanjan Sarkar and published by Apress. This book was released on 2017-12-20 with total page 545 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries and frameworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students

Practical Data Science with Hadoop and Spark

Author : Ofer Mendelevitch
Publisher : Addison-Wesley Professional
ISBN 13 : 0134029720
Total Pages : 463 pages
Book Rating : 4.1/5 (34 download)

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Book Synopsis Practical Data Science with Hadoop and Spark by : Ofer Mendelevitch

Download or read book Practical Data Science with Hadoop and Spark written by Ofer Mendelevitch and published by Addison-Wesley Professional. This book was released on 2016-12-08 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Complete Guide to Data Science with Hadoop—For Technical Professionals, Businesspeople, and Students Demand is soaring for professionals who can solve real data science problems with Hadoop and Spark. Practical Data Science with Hadoop® and Spark is your complete guide to doing just that. Drawing on immense experience with Hadoop and big data, three leading experts bring together everything you need: high-level concepts, deep-dive techniques, real-world use cases, practical applications, and hands-on tutorials. The authors introduce the essentials of data science and the modern Hadoop ecosystem, explaining how Hadoop and Spark have evolved into an effective platform for solving data science problems at scale. In addition to comprehensive application coverage, the authors also provide useful guidance on the important steps of data ingestion, data munging, and visualization. Once the groundwork is in place, the authors focus on specific applications, including machine learning, predictive modeling for sentiment analysis, clustering for document analysis, anomaly detection, and natural language processing (NLP). This guide provides a strong technical foundation for those who want to do practical data science, and also presents business-driven guidance on how to apply Hadoop and Spark to optimize ROI of data science initiatives. Learn What data science is, how it has evolved, and how to plan a data science career How data volume, variety, and velocity shape data science use cases Hadoop and its ecosystem, including HDFS, MapReduce, YARN, and Spark Data importation with Hive and Spark Data quality, preprocessing, preparation, and modeling Visualization: surfacing insights from huge data sets Machine learning: classification, regression, clustering, and anomaly detection Algorithms and Hadoop tools for predictive modeling Cluster analysis and similarity functions Large-scale anomaly detection NLP: applying data science to human language

An Introductory Guide to EC Competition Law and Practice

Author : Valentine Korah
Publisher :
ISBN 13 :
Total Pages : 2354 pages
Book Rating : 4.4/5 (11 download)

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Book Synopsis An Introductory Guide to EC Competition Law and Practice by : Valentine Korah

Download or read book An Introductory Guide to EC Competition Law and Practice written by Valentine Korah and published by . This book was released on 1994 with total page 2354 pages. Available in PDF, EPUB and Kindle. Book excerpt:

On the Move to Meaningful Internet Systems: OTM 2018 Workshops

Author : Christophe Debruyne
Publisher : Springer
ISBN 13 : 3030116832
Total Pages : 261 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis On the Move to Meaningful Internet Systems: OTM 2018 Workshops by : Christophe Debruyne

Download or read book On the Move to Meaningful Internet Systems: OTM 2018 Workshops written by Christophe Debruyne and published by Springer. This book was released on 2019-02-06 with total page 261 pages. Available in PDF, EPUB and Kindle. Book excerpt: This volume constitutes the refereed proceedings of the Confederated International International Workshop on Enterprise Integration, Interoperability and Networking (EI2N ), Fact Based Modeling ( FBM), Industry Case Studies Program ( ICSP ), and International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society (Meta4eS), held as part of OTM 2018 in October 2018 in Valletta, Malta. As the three main conferences and the associated workshops all share the distributed aspects of modern computing systems, they experience the application pull created by the Internet and by the so-called Semantic Web, in particular developments of Big Data, increased importance of security issues, and the globalization of mobile-based technologies.

Application of Big Data, Blockchain, and Internet of Things for Education Informatization

Author : Mian Ahmad Jan
Publisher : Springer Nature
ISBN 13 : 3030879003
Total Pages : 598 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Application of Big Data, Blockchain, and Internet of Things for Education Informatization by : Mian Ahmad Jan

Download or read book Application of Big Data, Blockchain, and Internet of Things for Education Informatization written by Mian Ahmad Jan and published by Springer Nature. This book was released on 2021-10-12 with total page 598 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes the refereed proceedings of the First International Conference International Conference on Application of Big Data, Blockchain, and Internet of Things for Education Informatization. The conference was held in August 2021 and due to COVID-19 pandemic virtually.The 99 revised full papers and 45 short papers have been selected from 503 submissions. The papers describe research fields such as “big data” and “information education”. The aim of the conference is to provide international cooperation and exchange platforms for big data and information education experts, scholars and enterprise developers to share research results, discuss existing problems and challenges, and explore cutting-edge science and technology.

Data Engineering and Data Science

Author : Kukatlapalli Pradeep Kumar
Publisher : John Wiley & Sons
ISBN 13 : 1119841976
Total Pages : 367 pages
Book Rating : 4.1/5 (198 download)

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Book Synopsis Data Engineering and Data Science by : Kukatlapalli Pradeep Kumar

Download or read book Data Engineering and Data Science written by Kukatlapalli Pradeep Kumar and published by John Wiley & Sons. This book was released on 2023-08-29 with total page 367 pages. Available in PDF, EPUB and Kindle. Book excerpt: DATA ENGINEERING and DATA SCIENCE Written and edited by one of the most prolific and well-known experts in the field and his team, this exciting new volume is the “one-stop shop” for the concepts and applications of data science and engineering for data scientists across many industries. The field of data science is incredibly broad, encompassing everything from cleaning data to deploying predictive models. However, it is rare for any single data scientist to be working across the spectrum day to day. Data scientists usually focus on a few areas and are complemented by a team of other scientists and analysts. Data engineering is also a broad field, but any individual data engineer doesn’t need to know the whole spectrum of skills. Data engineering is the aspect of data science that focuses on practical applications of data collection and analysis. For all the work that data scientists do to answer questions using large sets of information, there have to be mechanisms for collecting and validating that information. In this exciting new volume, the team of editors and contributors sketch the broad outlines of data engineering, then walk through more specific descriptions that illustrate specific data engineering roles. Data-driven discovery is revolutionizing the modeling, prediction, and control of complex systems. This book brings together machine learning, engineering mathematics, and mathematical physics to integrate modeling and control of dynamical systems with modern methods in data science. It highlights many of the recent advances in scientific computing that enable data-driven methods to be applied to a diverse range of complex systems, such as turbulence, the brain, climate, epidemiology, finance, robotics, and autonomy. Whether for the veteran engineer or scientist working in the field or laboratory, or the student or academic, this is a must-have for any library.