Download or read book Operating Systems and Infrastructure in Data Science written by Josef Spillner and published by vdf Hochschulverlag AG. This book was released on 2023-09-22 with total page 172 pages. Available in PDF, EPUB and Kindle. Book excerpt: Programming, DataOps, Data Concepts, Applications, Workflows, Tools, Middleware, Collaborative Platforms, Cloud Facilities Modern data scientists work with a number of tools and operating system facilities in addition to online platforms. Mastering these in combination to manage their data and to deploy software, models and data as ready-to-use online services as well as to perform data science and analysis tasks is in the focus of Operating Systems and Infrastructure in Data Science. Readers will come to understand the fundamental concepts of operating systems and to explore plenty of tools in hands-on tasks and thus gradually develop the skills necessary to compose them for programming in the large, an essential capability in their later career. The book guides students through semester studies, acts as reference knowledge base and aids in acquiring the necessary knowledge, skills and competences especially in self-study settings. A unique feature of the book is the associated access to Edushell, a live environment to practice operating systems and infrastructure tasks.

Download or read book Urban Operating Systems written by Andres Luque-Ayala and published by MIT Press. This book was released on 2020-12-15 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: An exploration of the modest potentials and serious contradictions of reconfiguring urban life through computational operating systems. A new wave of enthusiasm for smart cities, urban data, and the Internet of Things has created the impression that computation can solve almost any urban problem. Subjecting this claim to critical scrutiny, in this book, Andrés Luque-Ayala and Simon Marvin examine the cultural, historical, and contemporary contexts in which urban computational logics have emerged. They consider the rationalities and techniques that constitute emerging computational forms of urbanization, including work on digital urbanism, smart cities, and, more recently, platform urbanism. They explore the modest potentials and serious contradictions of reconfiguring urban life, city services, and urban-networked infrastructure through computational operating systems.

Download or read book Data Science Quick Reference Manual Advanced Machine Learning and Deployment written by Mario A. B. Capurso and published by Mario Capurso. This book was released on 2023-09-08 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work follows the 2021 curriculum of the Association for Computing Machinery for specialists in Data Sciences, with the aim of producing a manual that collects notions in a simplified form, facilitating a personal training path starting from specialized skills in Computer Science or Mathematics or Statistics. It has a bibliography with links to quality material but freely usable for your own training and contextual practical exercises. Part in a series of texts, it first summarizes the standard CRISP DM working methodology used in this work and in Data Science projects. As this text uses Orange for the application aspects, it describes its installation and widgets. The data modeling phase is considered from the perspective of machine learning by summarizing machine learning types, model types, problem types, and algorithm types. Advanced aspects associated with modeling are described such as loss and optimization functions such as gradient descent, techniques to analyze model performance such as Bootstrapping and Cross Validation. Deployment scenarios and the most common platforms are analyzed, with application examples. Mechanisms are proposed to automate machine learning and to support the interpretability of models and results such as Partial Dependence Plot, Permuted Feature Importance and others. The exercises are described with Orange and Python using the Keras/Tensorflow library. The text is accompanied by supporting material and it is possible to download the examples and the test data.

Download or read book Creativity in Intelligent Technologies and Data Science written by Alla G. Kravets and published by Springer Nature. This book was released on 2019-08-29 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set constitutes the proceedings of the Third Conference on Creativity in Intellectual Technologies and Data Science, CIT&DS 2019, held in Volgograd, Russia, in September 2019. The 67 full papers, 1 short paper and 3 keynote papers presented were carefully reviewed and selected from 231 submissions. The papers are organized in topical sections in the two volumes. Part I: cyber-physical systems and Big Data-driven world. Part II: artificial intelligence and deep learning technologies for creative tasks; intelligent technologies in social engineering.

Download or read book Frontiers in Massive Data Analysis written by National Research Council and published by National Academies Press. This book was released on 2013-09-03 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining of massive data sets is transforming the way we think about crisis response, marketing, entertainment, cybersecurity and national intelligence. Collections of documents, images, videos, and networks are being thought of not merely as bit strings to be stored, indexed, and retrieved, but as potential sources of discovery and knowledge, requiring sophisticated analysis techniques that go far beyond classical indexing and keyword counting, aiming to find relational and semantic interpretations of the phenomena underlying the data. Frontiers in Massive Data Analysis examines the frontier of analyzing massive amounts of data, whether in a static database or streaming through a system. Data at that scale-terabytes and petabytes-is increasingly common in science (e.g., particle physics, remote sensing, genomics), Internet commerce, business analytics, national security, communications, and elsewhere. The tools that work to infer knowledge from data at smaller scales do not necessarily work, or work well, at such massive scale. New tools, skills, and approaches are necessary, and this report identifies many of them, plus promising research directions to explore. Frontiers in Massive Data Analysis discusses pitfalls in trying to infer knowledge from massive data, and it characterizes seven major classes of computation that are common in the analysis of massive data. Overall, this report illustrates the cross-disciplinary knowledge-from computer science, statistics, machine learning, and application disciplines-that must be brought to bear to make useful inferences from massive data.

Download or read book Big Data Analytics in Earth Atmospheric and Ocean Sciences written by Thomas Huang and published by John Wiley & Sons. This book was released on 2022-10-14 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applying tools for data analysis to the rapidly increasing volume of data about the Earth An ever-increasing volume of Earth data is being gathered. These data are “big” not only in size but also in their complexity, different formats, and varied scientific disciplines. As such, big data are disrupting traditional research. New methods and platforms, such as the cloud, are tackling these new challenges. Big Data Analytics in Earth, Atmospheric, and Ocean Sciences explores new tools for the analysis and display of the rapidly increasing volume of data about the Earth. Volume highlights include: An introduction to the breadth of big earth data analytics Architectures developed to support big earth data analytics Different analysis and statistical methods for big earth data Current applications of analytics to Earth science data Challenges to fully implementing big data analytics The American Geophysical Union promotes discovery in Earth and space science for the benefit of humanity. Its publications disseminate scientific knowledge and provide resources for researchers, students, and professionals. Find out more in this Q&A with the editors.

Download or read book Secure Data Science written by Bhavani Thuraisingham and published by CRC Press. This book was released on 2022-04-27 with total page 457 pages. Available in PDF, EPUB and Kindle. Book excerpt: Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science. After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media. This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.

Download or read book Strategies in Biomedical Data Science written by Jay A. Etchings and published by John Wiley & Sons. This book was released on 2017-01-03 with total page 466 pages. Available in PDF, EPUB and Kindle. Book excerpt: An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.

Download or read book Machine Learning and Knowledge Discovery in Databases Applied Data Science Track written by Yuxiao Dong and published by Springer Nature. This book was released on 2021-02-24 with total page 612 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 5-volume proceedings, LNAI 12457 until 12461 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2020, which was held during September 14-18, 2020. The conference was planned to take place in Ghent, Belgium, but had to change to an online format due to the COVID-19 pandemic. The 232 full papers and 10 demo papers presented in this volume were carefully reviewed and selected for inclusion in the proceedings. The volumes are organized in topical sections as follows: Part I: Pattern Mining; clustering; privacy and fairness; (social) network analysis and computational social science; dimensionality reduction and autoencoders; domain adaptation; sketching, sampling, and binary projections; graphical models and causality; (spatio-) temporal data and recurrent neural networks; collaborative filtering and matrix completion. Part II: deep learning optimization and theory; active learning; adversarial learning; federated learning; Kernel methods and online learning; partial label learning; reinforcement learning; transfer and multi-task learning; Bayesian optimization and few-shot learning. Part III: Combinatorial optimization; large-scale optimization and differential privacy; boosting and ensemble methods; Bayesian methods; architecture of neural networks; graph neural networks; Gaussian processes; computer vision and image processing; natural language processing; bioinformatics. Part IV: applied data science: recommendation; applied data science: anomaly detection; applied data science: Web mining; applied data science: transportation; applied data science: activity recognition; applied data science: hardware and manufacturing; applied data science: spatiotemporal data. Part V: applied data science: social good; applied data science: healthcare; applied data science: e-commerce and finance; applied data science: computational social science; applied data science: sports; demo track.

Download or read book Big Data Analytics for Sensor Network Collected Intelligence written by Hui-Huang Hsu and published by Morgan Kaufmann. This book was released on 2017-02-02 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Analytics for Sensor-Network Collected Intelligence explores state-of-the-art methods for using advanced ICT technologies to perform intelligent analysis on sensor collected data. The book shows how to develop systems that automatically detect natural and human-made events, how to examine people's behaviors, and how to unobtrusively provide better services. It begins by exploring big data architecture and platforms, covering the cloud computing infrastructure and how data is stored and visualized. The book then explores how big data is processed and managed, the key security and privacy issues involved, and the approaches used to ensure data quality. In addition, readers will find a thorough examination of big data analytics, analyzing statistical methods for data analytics and data mining, along with a detailed look at big data intelligence, ubiquitous and mobile computing, and designing intelligence system based on context and situation. Indexing: The books of this series are submitted to EI-Compendex and SCOPUS - Contains contributions from noted scholars in computer science and electrical engineering from around the globe - Provides a broad overview of recent developments in sensor collected intelligence - Edited by a team comprised of leading thinkers in big data analytics

Download or read book Data Science for Business Professionals written by Probyto Data Science and Consulting Pvt. Ltd. and published by BPB Publications. This book was released on 2020-05-06 with total page 376 pages. Available in PDF, EPUB and Kindle. Book excerpt: Primer into the multidisciplinary world of Data Science KEY FEATURESÊÊ - Explore and use the key concepts of Statistics required to solve data science problems - Use Docker, Jenkins, and Git for Continuous Development and Continuous Integration of your web app - Learn how to build Data Science solutions with GCP and AWS DESCRIPTIONÊ The book will initially explain the What-Why of Data Science and the process of solving a Data Science problem. The fundamental concepts of Data Science, such as Statistics, Machine Learning, Business Intelligence, Data pipeline, and Cloud Computing, will also be discussed. All the topics will be explained with an example problem and will show how the industry approaches to solve such a problem. The book will pose questions to the learners to solve the problems and build the problem-solving aptitude and effectively learn. The book uses Mathematics wherever necessary and will show you how it is implemented using Python with the help of an example dataset.Ê WHAT WILL YOU LEARNÊÊ - Understand the multi-disciplinary nature of Data Science - Get familiar with the key concepts in Mathematics and Statistics - Explore a few key ML algorithms and their use cases - Learn how to implement the basics of Data Pipelines - Get an overview of Cloud Computing & DevOps - Learn how to create visualizations using Tableau WHO THIS BOOK IS FORÊ This book is ideal for Data Science enthusiasts who want to explore various aspects of Data Science. Useful for Academicians, Business owners, and Researchers for a quick reference on industrial practices in Data Science.Ê TABLE OF CONTENTS 1. Data Science in Practice 2. Mathematics Essentials 3. Statistics Essentials 4. Exploratory Data Analysis 5. Data preprocessing 6. Feature Engineering 7. Machine learning algorithms 8. Productionizing ML models 9. Data Flows in Enterprises 10. Introduction to Databases 11. Introduction to Big Data 12. DevOps for Data Science 13. Introduction to Cloud Computing 14. Deploy Model to Cloud 15. Introduction to Business IntelligenceÊ 16. Data Visualization Tools 17. Industry Use Case 1 Ð FormAssist 18. Industry Use Case 2 Ð PeopleReporter 19. Data Science Learning Resources 20. Do It Your Self Challenges 21. MCQs for Assessments

Download or read book Effective Data Science Infrastructure written by Ville Tuulos and published by Simon and Schuster. This book was released on 2022-08-30 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Simplify data science infrastructure to give data scientists an efficient path from prototype to production. In Effective Data Science Infrastructure you will learn how to: Design data science infrastructure that boosts productivity Handle compute and orchestration in the cloud Deploy machine learning to production Monitor and manage performance and results Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, Conda, and Docker Architect complex applications for multiple teams and large datasets Customize and grow data science infrastructure Effective Data Science Infrastructure: How to make data scientists more productive is a hands-on guide to assembling infrastructure for data science and machine learning applications. It reveals the processes used at Netflix and other data-driven companies to manage their cutting edge data infrastructure. In it, you’ll master scalable techniques for data storage, computation, experiment tracking, and orchestration that are relevant to companies of all shapes and sizes. You’ll learn how you can make data scientists more productive with your existing cloud infrastructure, a stack of open source software, and idiomatic Python. The author is donating proceeds from this book to charities that support women and underrepresented groups in data science. About the technology Growing data science projects from prototype to production requires reliable infrastructure. Using the powerful new techniques and tooling in this book, you can stand up an infrastructure stack that will scale with any organization, from startups to the largest enterprises. About the book Effective Data Science Infrastructure teaches you to build data pipelines and project workflows that will supercharge data scientists and their projects. Based on state-of-the-art tools and concepts that power data operations of Netflix, this book introduces a customizable cloud-based approach to model development and MLOps that you can easily adapt to your company’s specific needs. As you roll out these practical processes, your teams will produce better and faster results when applying data science and machine learning to a wide array of business problems. What's inside Handle compute and orchestration in the cloud Combine cloud-based tools into a cohesive data science environment Develop reproducible data science projects using Metaflow, AWS, and the Python data ecosystem Architect complex applications that require large datasets and models, and a team of data scientists About the reader For infrastructure engineers and engineering-minded data scientists who are familiar with Python. About the author At Netflix, Ville Tuulos designed and built Metaflow, a full-stack framework for data science. Currently, he is the CEO of a startup focusing on data science infrastructure. Table of Contents 1 Introducing data science infrastructure 2 The toolchain of data science 3 Introducing Metaflow 4 Scaling with the compute layer 5 Practicing scalability and performance 6 Going to production 7 Processing data 8 Using and operating models 9 Machine learning with the full stack

Download or read book Machine Intelligence and Data Science Applications written by Vaclav Skala and published by Springer Nature. This book was released on 2022-08-01 with total page 909 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a compilation of peer reviewed papers presented at International Conference on Machine Intelligence and Data Science Applications (MIDAS 2021), held in Comilla University, Cumilla, Bangladesh during 26 – 27 December 2021. The book covers applications in various fields like image processing, natural language processing, computer vision, sentiment analysis, speech and gesture analysis, etc. It also includes interdisciplinary applications like legal, healthcare, smart society, cyber physical system and smart agriculture, etc. The book is a good reference for computer science engineers, lecturers/researchers in machine intelligence discipline and engineering graduates.

Download or read book The Dictionary of Artificial Intelligence written by Utku Taşova and published by Entropol. This book was released on 2023-11-03 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unveiling the Future: Your Portal to Artificial Intelligence Proficiency In the epoch of digital metamorphosis, Artificial Intelligence (AI) stands as the vanguard of a new dawn, a nexus where human ingenuity intertwines with machine precision. As we delve deeper into this uncharted realm, the boundary between the conceivable and the fantastical continually blurs, heralding a new era of endless possibilities. The Dictionary of Artificial Intelligence, embracing a compendium of 3,300 meticulously curated titles, endeavors to be the torchbearer in this journey of discovery, offering a wellspring of knowledge to both the uninitiated and the adept. Embarking on the pages of this dictionary is akin to embarking on a voyage through the vast and often turbulent seas of AI. Each entry serves as a beacon, illuminating complex terminologies, core principles, and the avant-garde advancements that characterize this dynamic domain. The dictionary is more than a mere compilation of terms; it's a labyrinth of understanding waiting to be traversed. The Dictionary of Artificial Intelligence is an endeavor to demystify the arcane, to foster a shared lexicon that enhances collaboration, innovation, and comprehension across the AI community. It's a mission to bridge the chasm between ignorance and insight, to unravel the intricacies of AI that often seem enigmatic to the outsiders. This profound reference material transcends being a passive repository of terms; it’s an engagement with the multifaceted domain of artificial intelligence. Each title encapsulated within these pages is a testament to the audacity of human curiosity and the unyielding quest for advancement that propels the AI domain forward. The Dictionary of Artificial Intelligence is an invitation to delve deeper, to grapple with the lexicon of a field that stands at the cusp of redefining the very fabric of society. It's a conduit through which the curious become enlightened, the proficient become masters, and the innovators find inspiration. As you traverse through the entries of The Dictionary of Artificial Intelligence, you are embarking on a journey of discovery. A journey that not only augments your understanding but also ignites the spark of curiosity and the drive for innovation that are quintessential in navigating the realms of AI. We beckon you to commence this educational expedition, to explore the breadth and depth of AI lexicon, and to emerge with a boundless understanding and an unyielding resolve to contribute to the ever-evolving narrative of artificial intelligence. Through The Dictionary of Artificial Intelligence, may your quest for knowledge be as boundless and exhilarating as the domain it explores.

Download or read book Data Science written by Qinglei Zhou and published by Springer. This book was released on 2018-09-10 with total page 676 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two volume set (CCIS 901 and 902) constitutes the refereed proceedings of the 4th International Conference of Pioneering Computer Scientists, Engineers and Educators, ICPCSEE 2018 (originally ICYCSEE) held in Zhengzhou, China, in September 2018. The 125 revised full papers presented in these two volumes were carefully reviewed and selected from 1057 submissions. The papers cover a wide range of topics related to basic theory and techniques for data science including mathematical issues in data science, computational theory for data science, big data management and applications, data quality and data preparation, evaluation and measurement in data science, data visualization, big data mining and knowledge management, infrastructure for data science, machine learning for data science, data security and privacy, applications of data science, case study of data science, multimedia data management and analysis, data-driven scientific research, data-driven bioinformatics, data-driven healthcare, data-driven management, data-driven eGovernment, data-driven smart city/planet, data marketing and economics, social media and recommendation systems, data-driven security, data-driven business model innovation, social and/or organizational impacts of data science.

Download or read book Big Data Science and Analytics for Smart Sustainable Urbanism written by Simon Elias Bibri and published by Springer. This book was released on 2019-05-30 with total page 354 pages. Available in PDF, EPUB and Kindle. Book excerpt: We are living at the dawn of what has been termed ‘the fourth paradigm of science,’ a scientific revolution that is marked by both the emergence of big data science and analytics, and by the increasing adoption of the underlying technologies in scientific and scholarly research practices. Everything about science development or knowledge production is fundamentally changing thanks to the ever-increasing deluge of data. This is the primary fuel of the new age, which powerful computational processes or analytics algorithms are using to generate valuable knowledge for enhanced decision-making, and deep insights pertaining to a wide variety of practical uses and applications. This book addresses the complex interplay of the scientific, technological, and social dimensions of the city, and what it entails in terms of the systemic implications for smart sustainable urbanism. In concrete terms, it explores the interdisciplinary and transdisciplinary field of smart sustainable urbanism and the unprecedented paradigmatic shifts and practical advances it is undergoing in light of big data science and analytics. This new era of science and technology embodies an unprecedentedly transformative and constitutive power—manifested not only in the form of revolutionizing science and transforming knowledge, but also in advancing social practices, producing new discourses, catalyzing major shifts, and fostering societal transitions. Of particular relevance, it is instigating a massive change in the way both smart cities and sustainable cities are studied and understood, and in how they are planned, designed, operated, managed, and governed in the face of urbanization. This relates to what has been dubbed data-driven smart sustainable urbanism, an emerging approach based on a computational understanding of city systems and processes that reduces urban life to logical and algorithmic rules and procedures, while also harnessing urban big data to provide a more holistic and integrated view or synoptic intelligence of the city. This is increasingly being directed towards improving, advancing, and maintaining the contribution of both sustainable cities and smart cities to the goals of sustainable development. This timely and multifaceted book is aimed at a broad readership. As such, it will appeal to urban scientists, data scientists, urbanists, planners, engineers, designers, policymakers, philosophers of science, and futurists, as well as all readers interested in an overview of the pivotal role of big data science and analytics in advancing every academic discipline and social practice concerned with data–intensive science and its application, particularly in relation to sustainability.

Download or read book T Bytes Agile AI Operations written by IT-Shades and published by EGBG Services LLC. This book was released on 2019-12-04 with total page 125 pages. Available in PDF, EPUB and Kindle. Book excerpt: