Data Science And Algorithms In Systems
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Data Science And Algorithms In Systems
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Author : Radek Silhavy
language : en
Publisher: Springer Nature
Release Date : 2023-01-03
Data Science And Algorithms In Systems written by Radek Silhavy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-03 with Technology & Engineering categories.
This book offers real-world data science and algorithm design topics linked to systems and software engineering. Furthermore, articles describing unique techniques in data science, algorithm design, and systems and software engineering are featured. This book is the second part of the refereed proceedings of the 6th Computational Methods in Systems and Software 2022 (CoMeSySo 2022). The CoMeSySo 2022 conference, which is being hosted online, is breaking down barriers. CoMeSySo 2022 aims to provide a worldwide venue for debate of the most recent high-quality research findings.
Big Data Analytics Systems Algorithms Applications
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Author : C.S.R. Prabhu
language : en
Publisher: Springer Nature
Release Date : 2019-10-14
Big Data Analytics Systems Algorithms Applications written by C.S.R. Prabhu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-14 with Computers categories.
This book provides a comprehensive survey of techniques, technologies and applications of Big Data and its analysis. The Big Data phenomenon is increasingly impacting all sectors of business and industry, producing an emerging new information ecosystem. On the applications front, the book offers detailed descriptions of various application areas for Big Data Analytics in the important domains of Social Semantic Web Mining, Banking and Financial Services, Capital Markets, Insurance, Advertisement, Recommendation Systems, Bio-Informatics, the IoT and Fog Computing, before delving into issues of security and privacy. With regard to machine learning techniques, the book presents all the standard algorithms for learning – including supervised, semi-supervised and unsupervised techniques such as clustering and reinforcement learning techniques to perform collective Deep Learning. Multi-layered and nonlinear learning for Big Data are also covered. In turn, the book highlights real-life case studies on successful implementations of Big Data Analytics at large IT companies such as Google, Facebook, LinkedIn and Microsoft. Multi-sectorial case studies on domain-based companies such as Deutsche Bank, the power provider Opower, Delta Airlines and a Chinese City Transportation application represent a valuable addition. Given its comprehensive coverage of Big Data Analytics, the book offers a unique resource for undergraduate and graduate students, researchers, educators and IT professionals alike.
Introduction To Data Systems
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Author : Thomas Bressoud
language : en
Publisher: Springer Nature
Release Date : 2020-12-04
Introduction To Data Systems written by Thomas Bressoud and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-04 with Computers categories.
Encompassing a broad range of forms and sources of data, this textbook introduces data systems through a progressive presentation. Introduction to Data Systems covers data acquisition starting with local files, then progresses to data acquired from relational databases, from REST APIs and through web scraping. It teaches data forms/formats from tidy data to relationally defined sets of tables to hierarchical structure like XML and JSON using data models to convey the structure, operations, and constraints of each data form. The starting point of the book is a foundation in Python programming found in introductory computer science classes or short courses on the language, and so does not require prerequisites of data structures, algorithms, or other courses. This makes the material accessible to students early in their educational career and equips them with understanding and skills that can be applied in computer science, data science/data analytics, and information technology programs as well as for internships and research experiences. This book is accessible to a wide variety of students. By drawing together content normally spread across upper level computer science courses, it offers a single source providing the essentials for data science practitioners. In our increasingly data-centric world, students from all domains will benefit from the “data-aptitude” built by the material in this book.
Algorithms For Data Science
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Author : Brian Steele
language : en
Publisher: Springer
Release Date : 2016-12-25
Algorithms For Data Science written by Brian Steele and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-25 with Computers categories.
This textbook on practical data analytics unites fundamental principles, algorithms, and data. Algorithms are the keystone of data analytics and the focal point of this textbook. Clear and intuitive explanations of the mathematical and statistical foundations make the algorithms transparent. But practical data analytics requires more than just the foundations. Problems and data are enormously variable and only the most elementary of algorithms can be used without modification. Programming fluency and experience with real and challenging data is indispensable and so the reader is immersed in Python and R and real data analysis. By the end of the book, the reader will have gained the ability to adapt algorithms to new problems and carry out innovative analyses. This book has three parts:(a) Data Reduction: Begins with the concepts of data reduction, data maps, and information extraction. The second chapter introduces associative statistics, the mathematical foundation of scalable algorithms and distributed computing. Practical aspects of distributed computing is the subject of the Hadoop and MapReduce chapter.(b) Extracting Information from Data: Linear regression and data visualization are the principal topics of Part II. The authors dedicate a chapter to the critical domain of Healthcare Analytics for an extended example of practical data analytics. The algorithms and analytics will be of much interest to practitioners interested in utilizing the large and unwieldly data sets of the Centers for Disease Control and Prevention's Behavioral Risk Factor Surveillance System.(c) Predictive Analytics Two foundational and widely used algorithms, k-nearest neighbors and naive Bayes, are developed in detail. A chapter is dedicated to forecasting. The last chapter focuses on streaming data and uses publicly accessible data streams originating from the Twitter API and the NASDAQ stock market in the tutorials. This book is intended for a one- or two-semester course in data analytics for upper-division undergraduate and graduate students in mathematics, statistics, and computer science. The prerequisites are kept low, and students with one or two courses in probability or statistics, an exposure to vectors and matrices, and a programming course will have no difficulty. The core material of every chapter is accessible to all with these prerequisites. The chapters often expand at the close with innovations of interest to practitioners of data science. Each chapter includes exercises of varying levels of difficulty. The text is eminently suitable for self-study and an exceptional resource for practitioners.
Introduction To Data Science And Machine Learning
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Author : Keshav Sud
language : en
Publisher: BoD – Books on Demand
Release Date : 2020-03-25
Introduction To Data Science And Machine Learning written by Keshav Sud and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-03-25 with Computers categories.
Introduction to Data Science and Machine Learning has been created with the goal to provide beginners seeking to learn about data science, data enthusiasts, and experienced data professionals with a deep understanding of data science application development using open-source programming from start to finish. This book is divided into four sections: the first section contains an introduction to the book, the second covers the field of data science, software development, and open-source based embedded hardware; the third section covers algorithms that are the decision engines for data science applications; and the final section brings together the concepts shared in the first three sections and provides several examples of data science applications.
Data Engineering And Data Science
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Author : Kukatlapalli Pradeep Kumar
language : en
Publisher: John Wiley & Sons
Release Date : 2023-08-29
Data Engineering And Data Science written by Kukatlapalli Pradeep Kumar and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-08-29 with Mathematics categories.
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.
Intelligent System Algorithms And Applications In Science And Technology
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Author : Sunil Pathak
language : en
Publisher: CRC Press
Release Date : 2022-02-03
Intelligent System Algorithms And Applications In Science And Technology written by Sunil Pathak and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-02-03 with Computers categories.
The 21st century has witnessed massive changes around the world in intelligence systems in order to become smarter, energy efficient, reliable, and cheaper. This volume explores the application of intelligent techniques in various fields of engineering and technology. It addresses diverse topics in such areas as machine learning-based intelligent systems for healthcare, applications of artificial intelligence and the Internet of Things, intelligent data analytics techniques, intelligent network systems and applications, and inequalities and process control systems. The authors explore the full breadth of the field, which encompasses data analysis, image processing, speech processing and recognition, medical science and healthcare monitoring, smart irrigation systems, insurance and banking, robotics and process control, and more.
Data Science
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Author : Gyanendra K. Verma
language : en
Publisher: Springer Nature
Release Date : 2021-08-19
Data Science written by Gyanendra K. Verma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-19 with Computers categories.
This book targets an audience with a basic understanding of deep learning, its architectures, and its application in the multimedia domain. Background in machine learning is helpful in exploring various aspects of deep learning. Deep learning models have a major impact on multimedia research and raised the performance bar substantially in many of the standard evaluations. Moreover, new multi-modal challenges are tackled, which older systems would not have been able to handle. However, it is very difficult to comprehend, let alone guide, the process of learning in deep neural networks, there is an air of uncertainty about exactly what and how these networks learn. By the end of the book, the readers will have an understanding of different deep learning approaches, models, pre-trained models, and familiarity with the implementation of various deep learning algorithms using various frameworks and libraries.
Integrating Deep Learning Algorithms To Overcome Challenges In Big Data Analytics
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Author : R. Sujatha
language : en
Publisher: CRC Press
Release Date : 2021-09-22
Integrating Deep Learning Algorithms To Overcome Challenges In Big Data Analytics written by R. Sujatha and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-22 with Technology & Engineering categories.
Data science revolves around two giants: Big Data analytics and Deep Learning. It is becoming challenging to handle and retrieve useful information due to how fast data is expanding. This book presents the technologies and tools to simplify and streamline the formation of Big Data as well as Deep Learning systems. This book discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and decision-making. It also covers numerous applications in healthcare, education, communication, media, and entertainment. Integrating Deep Learning Algorithms to Overcome Challenges in Big Data Analytics offers innovative platforms for integrating Big Data and Deep Learning and presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval. FEATURES Provides insight into the skill set that leverages one’s strength to act as a good data analyst Discusses how Big Data and Deep Learning hold the potential to significantly increase data understanding and help in decision-making Covers numerous potential applications in healthcare, education, communication, media, and entertainment Offers innovative platforms for integrating Big Data and Deep Learning Presents issues related to adequate data storage, semantic indexing, data tagging, and fast information retrieval from Big Data This book is aimed at industry professionals, academics, research scholars, system modelers, and simulation experts.
Data Science And Intelligent Systems
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Author : Radek Silhavy
language : en
Publisher: Springer Nature
Release Date : 2021-11-16
Data Science And Intelligent Systems written by Radek Silhavy and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-16 with Technology & Engineering categories.
This book constitutes the second part of refereed proceedings of the 5th Computational Methods in Systems and Software 2021 (CoMeSySo 2021) proceedings. The real-world problems related to data science and algorithm design related to systems and software engineering are presented in this papers. Furthermore, the basic research’ papers that describe novel approaches in the data science, algorithm design and in systems and software engineering are included. The CoMeSySo 2021 conference is breaking the barriers, being held online. CoMeSySo 2021 intends to provide an international forum for the discussion of the latest high-quality research results