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Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component.Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication.

Communication in science. --- Technical writing. --- Science --- Data mining. --- Information scientifique --- Rédaction scientifique et technique --- Exploration de données. --- Data processing. --- Exploration de données --- Rédaction scientifique et technique --- Exploration de données

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Managing and Mining Graph Data is a comprehensive survey book in graph data analytics. It contains extensive surveys on important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by leading researchers, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science. About the Editors: Charu C. Aggarwal obtained his B.Tech in Computer Science from IIT Kanpur in 1993 and Ph.D. from MIT in 1996. He has worked as a researcher at IBM since then, and has published over 130 papers in major data mining conferences and journals. He has applied for or been granted over 70 US and International patents, and has thrice been designated a Master Inventor at IBM. He has received an IBM Corporate award for his work on data stream analytics, and an IBM Outstanding Innovation Award for his work on privacy technology. He has served on the executive committees of most major data mining conferences. He has served as an associate editor of the IEEE TKDE, as an associate editor of the ACM SIGKDD Explorations, and as an action editor of the DMKD Journal. He is a fellow of the IEEE, and a life-member of the ACM. Haixun Wang is currently a researcher at Microsoft Research Asia. He received the B.S. and the M.S. degree, both in computer science, from Shanghai Jiao Tong University in 1994 and 1996. He received the Ph.D. degree in computer science from the University of California, Los Angeles in 2000. He subsequently worked as a researcher at IBM until 2009. His main research interest is database language and systems, data mining, and information retrieval. He has published more than 100 research papers in referred international journals and conference proceedings. He serves as an associate editor of the IEEE TKDE, and has served as a reviewer and program committee member of leading database conferences and journals.

Computer science --- Database management --- Data mining --- Information storage and retrieval systems --- Information systems. --- Computer graphics --- Informatique --- Bases de données --- Exploration de données (Informatique) --- Systèmes d'information --- Infographie --- Gestion --- Bases de données --- Exploration de données (Informatique) --- Systèmes d'information --- EPUB-LIV-FT LIVINFOR SPRINGER-B

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Das dem MIT angehörende Senseable City Lab unter Carlo Ratti ist eines der Forschungszentren, die sich mit den Strömen von Menschen und Waren, aber auch von Müll beschäftigen, die sich um den Globus bewegen. Erfahrungen mit infrastrukturellen Großprojekten legen nahe, dass immer komplexere und vor allem flexiblere Antworten auf Fragen des Transports oder der Entsorgung gesucht werden müssen. Der von Dietmar Offenhuber und Carlo Ratti herausgegebene Band zeigt, wie Big Data die Realität und damit die Beschäftigung mit der Stadt verändern. Er diskutiert die Auswirkungen von Echtzeitdaten auf Architektur und Stadtplanung anhand von Beispielen, die im Senseable City Lab erarbeitet wurden: Sie demonstrieren, wie das Lab digitale Daten als Material interpretiert, das für die Formulierung einer anderen urbanen Zukunft herangezogen werden kann. Nicht übersehen werden dabei die Schattenseiten der stadtbezogenen Datenerfassung und -steuerung.Die Autoren thematisieren Fragestellungen, mit welchen sich die planenden Disziplinen in der Stadt in Zukunft intensiv beschäftigen werden: Fragestellungen, die die bisherigen Aufgaben und das Selbstverständnis der beteiligten Professionen nicht nur radikal in Zweifel ziehen, sondern fundamental verändern werden. The MIT based SENSEable City Lab under Carlo Ratti is one of the research centers that deal with the flow of people and goods, but also of refuse that moves around the world. Experience with large-scale infrastructure projects suggest that more complex and above all flexible answers must be sought to questions of transportation or disposal. This edition, edited by Dietmar Offenhuber and Carlo Ratti, shows how Big Data change reality and, hence, the way we deal with the city. It discusses the impact of real-time data on architecture and urban planning, using examples developed in the SENSEable City Lab. They demonstrate how the Lab interprets digital data as material that can be used for the formulation of a different urban future. It also looks at the negative aspects of the city-related data acquisition and control. The authors address issues with which urban planning disciplines will work intensively in the future: questions that not only radically and critically review, but also change fundamentally, the existing tasks and how the professions view their own roles.

711.4 --- 71.037 --- 13 --- 004 --- 004.38 --- 07 --- Stedenbouw (theorie) --- 21ste eeuw (stedenbouw) --- Eenentwintigste eeuw (stedenbouw) --- Cultuurfilosofie --- Computertechnologie --- Computers --- ICT --- Internet --- Mediawezen --- Data mining. --- City planning --- Exploration de données (Informatique) --- Urbanisme --- Data processing. --- Informatique --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching

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This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs and constraints of the available options. Solutions presented in the book cover a range of parallelization platforms from FPGAs and GPUs to multi-core systems and commodity clusters, concurrent programming frameworks including CUDA, MPI, MapReduce and DryadLINQ, and learning settings (supervised, unsupervised, semi-supervised and online learning). Extensive coverage of parallelization of boosted trees, SVMs, spectral clustering, belief propagation and other popular learning algorithms and deep dives into several applications make the book equally useful for researchers, students and practitioners.

Machine learning --- Data mining --- Parallel algorithms --- Parallel programs (Computer programs) --- Apprentissage automatique --- Exploration de données (Informatique) --- Algorithmes parallèles --- Programmes parallèles (Logiciels) --- Exploration de données (Informatique) --- Algorithmes parallèles --- Programmes parallèles (Logiciels) --- Machine Learning --- Machine learning. --- Data mining. --- Parallel algorithms. --- Parallel computer programs --- Parallel processing (Electronic computers) --- Computer programs --- Algorithms --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Learning, Machine --- Artificial intelligence --- Machine theory

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In today’s world, numbers are in the ascendancy. Societies dominated by star ratings, scores, likes and lists are rapidly emerging, as data are collected on virtually every aspect of our lives. From annual university rankings, ratings agencies and fitness tracking technologies to our credit score and health status, everything and everybody is measured and evaluated.In this important new book, Steffen Mau offers a critical analysis of this increasingly pervasive phenomenon. While the original intention behind the drive to quantify may have been to build trust and transparency, Mau shows how metrics have in fact become a form of social conditioning. The ubiquitous language of ranking and scoring has changed profoundly our perception of value and status. What is more, through quantification, our capacity for competition and comparison has expanded significantly – we can now measure ourselves against others in practically every area. The rise of quantification has created and strengthened social hierarchies, transforming qualitative differences into quantitative inequalities that play a decisive role in shaping the life chances of individuals.This timely analysis of the pernicious impact of quantification will appeal to students and scholars across the social sciences, as well as anyone concerned by the cult of numbers and its impact on our lives and societies today.

Information technology --- Data mining. --- Big data. --- Social aspects. --- Data mining --- Big data --- Social aspects --- Data sets, Large --- Large data sets --- Data sets --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Technologie de l'information --- Exploration de données (Informatique) --- Données volumineuses --- Aspect social --- Sociology of culture --- Artificial intelligence. Robotics. Simulation. Graphics --- #SBIB:303H12 --- #SBIB:309H1720 --- Methoden en technieken: sociale wetenschappen --- Informatiekunde, informatie management --- Big Data. --- Data Mining. --- Datenanalyse. --- Ranking. --- Soziale Ungleichheit. --- Ungerechtigkeit. --- Information technology - Social aspects