Data is constantly increasing and data analysts are in higher demand than ever. This book is an essential guide to the role of data analyst. Aspiring data analysts will discover what data analysts do all day, what skills they will need for the role, and what regulations they will be required to adhere to. Practising data analysts can explore useful data analysis tools, methods and techniques, brush up on best practices and look at how they can advance their career.

Anwendbarkeit des Mediendienstestaatsvertrages oder handelt es sich um Rund funk mit der Folge der Anwendung der Rundfunkgesetzes der Lander? Der zweite Abschnitt behandelt den "Rechtsverkehr im Internet'. Zunachst wird in Kapitel 3 der "Vertragsschluss im Internet" nach deutschem Recht erfasst. In Kapitel 15 ("Electronic Commerce im Internet") und 16 ("Rechtsfragen des In ternet-Vertriebs von Versicherungsdienstleistungen" werden die europaischen Re gelungen - insbesondere aus der Sicht des Verbraucherschutzes - hierzu bereits an tizipiert. Ferner gilt es zu berucksichtigen, dass der Geschaftsverkehr uber das In ternet eine zusatzliche Flankierung durch die Moglichkeit der Abwicklung von "Zahlungsverkehr im Internet' erhalt. Die zahlreichen rechtlichen Probleme, die mit der Verwendung von Cybermoney etc. auftauchen, werden in Kapitel 4 aufge griffen. Das Kapitel 5 behandelt sodann mit dem Thema, Rechtssicherheit im digitalen Rechtsverkehr'' einen zentralen Gesichtspunkt des Rechtsverkehrs. Dabei wird neben dem deutschen Signaturgesetz samt Signaturverordnung auch die eu ropaische Rechtsentwicklung berucksichtigt. Der dritte Abschnitt umfasst "die Rechtsstellung der Beteiligten." Zentral hier fiir ist die Frage nach der Verantwortlichkeit die sowohl den Diensteanbieter Kapi tel 6) als auch den Netzbelreiber (Kapitel 7) betrifft. Die strafrechtliche Perspek tive wird gesondert in Kapitel 18 aufgenommen. Eine in der Praxis immer haufi ger auftretende Frage gilt der Einordnung der "Vertragsgestaltung zwischen den Beteiligten" woruber Kapitel 8 Auskunft gibt."

Konzentrationstendenzen, Globalisierung und gut informierte Kunden sind Belege f r den harten Wettbewerb, in dem sich Handelsunternehmen befinden. Um in diesem Wettbewerb zu bestehen, ben tigen H ndler flexibel an die jeweilige Unternehmensstruktur anpassbare Informations- und Kommunikationssysteme, die die operativen Abl ufe, Beschaffung, Lagerung und Distribution und die betriebswirtschaftlich-administrativen Aufgaben der Buchhaltung, Kostenrechnung und Personalwirtschaft unterst tzen und aussagekr ftige Auswertungssysteme umfassen. Dar ber hinaus sind Informations- und Planungssysteme zur Unterst tzung von Marketing und Management heute kritischer Erfolgsfaktor. Das Buch stellt die Architektur von Handelsinformationssystemen am Beispiel des SAP Retail-Systems dar. Es zeigt auf, wie modernes Handelsmanagement durch Einsatz integrierter Standardsoftware realisiert werden kann.

Dieses Lehrbuch bietet eine umfassende EinfA1/4hrung in Grundlagen und Methoden der Computerlinguistik und stellt die wichtigsten Anwendungsgebiete in der Sprachtechnologie vor. Es richtet sich gleichermaAen an Studierende der Computerlinguistik und verwandter FAcher mit Bezug zur Verarbeitung natA1/4rlicher Sprache wie an Entwickler sprachverarbeitender Systeme.

FA1/4r die dritte Auflage wurden sAmtliche Kapitel A1/4berarbeitet und aktualisiert sowie zum Teil zu eigenstAndigen, neuen Kapiteln zusammengefA1/4hrt. Insbesondere trAgt die dritte Auflage der rasanten Entwicklung in der Computerlinguistik und Sprachtechnologie durch eine stArkere Fokussierung auf statistische Grundlagen und Methoden Rechnung.

To date, statistics has tended to be neatly divided into two theoretical approaches or frameworks: frequentist (or classical) and Bayesian. Scientists typically choose the statistical framework to analyse their data depending on the nature and complexity of the problem, and based on their personal views and prior training on probability and uncertainty. Although textbooks and courses should reflect and anticipate this dual reality, they rarely do so. This accessible textbook explains, discusses, and applies both the frequentist and Bayesian theoretical frameworks to fit the different types of statistical models that allow an analysis of the types of data most commonly gathered by life scientists. It presents the material in an informal, approachable, and progressive manner suitable for readers with only a basic knowledge of calculus and statistics. Statistical Modeling with R is aimed at senior undergraduate and graduate students, professional researchers, and practitioners throughout the life sciences, seeking to strengthen their understanding of quantitative methods and to apply them successfully to real world scenarios, whether in the fields of ecology, evolution, environmental studies, or computational biology.

Das Buch thematisiert den deutschen Markt f r TV-Kabelnetze in seiner Entwicklung vom Monopol zum Wettbewerb. Schwerpunkt der Betrachtung bilden die auf diesem Markt handelnden Akteure mit ihren unterschiedlichen Interessen und Strategien. So wird die Bedeutung des ehemaligen Staatsmonopolisten "Deutsche Telekom" f r die Entwicklung dieses Marktes ebenso herausgestellt und kritisch analysiert, wie die der deutschen und internationalen Kabelnetzbetreiber. Zentrale Themen des Buches sind: Bedeutung von Wettbewerb und Deregulierung f r den deutschen TV-Kabelmarkt, Wettbewerbssituation und Potenziale privater Kabelnetzbetreiber. Diese Aspekte sind eingebettet in die Darstellung und Analyse der ordnungspolitischen Rahmenbedingungen des TV-Kabelmarktes sowie der hieraus resultierenden, innovativen Wettbewerbsbedingungen. Das Buch bietet einen im deutschsprachigen Raum einmaligen Einblick.

Level up with Tableau to build eye-catching, easy-to-interpret data visualizations. In this follow-up guide to Practical Tableau, author Ryan Sleeper takes you through a collection of unique tips and tutorials for using this popular software. Beginning to advanced Tableau users will learn how to go beyond Show Me to make better charts and learn dozens of tricks to improve both the author and user experience. Featuring many approaches he developed himself, Ryan shows you how to create charts that empower Tableau users to explore, understand, and derive value from their data. He also shares many of his favorite tricks that enabled him to become a Tableau Zen Master, Tableau Public Visualization of the Year author, and Tableau Global Iron Viz Champion. Learn what's new in Tableau since Practical Tableau was released Examine unique new charts-timelines, custom gauges, and leapfrog charts-plus innovations to traditional charts such as highlight tables, scatter plots, and maps Get tips that can help make a Tableau developer's life easier Understand what developers can do to make users' lives easier

This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns. After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly. The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.

Data Mining: Concepts and Techniques, Fourth Edition introduces concepts, principles, and methods for mining patterns, knowledge, and models from various kinds of data for diverse applications. Specifically, it delves into the processes for uncovering patterns and knowledge from massive collections of data, known as knowledge discovery from data, or KDD. It focuses on the feasibility, usefulness, effectiveness, and scalability of data mining techniques for large data sets. After an introduction to the concept of data mining, the authors explain the methods for preprocessing, characterizing, and warehousing data. They then partition the data mining methods into several major tasks, introducing concepts and methods for mining frequent patterns, associations, and correlations for large data sets; data classificcation and model construction; cluster analysis; and outlier detection. Concepts and methods for deep learning are systematically introduced as one chapter. Finally, the book covers the trends, applications, and research frontiers in data mining.

Probability, Statistics, and Random Signals offers a comprehensive treatment of probability, giving equal treatment to discrete and continuous probability. The topic of statistics is presented as the application of probability to data analysis, not as a cookbook of statistical recipes. This student-friendly text features accessible descriptions and highly engaging exercises on topics like gambling, the birthday paradox, and financial decision-making.

In the age of big data, being able to make sense of data is an important key to success. Interactive Visual Data Analysis advocates the synthesis of visualization, interaction, and automatic computation to facilitate insight generation and knowledge crystallization from large and complex data. The book provides a systematic and comprehensive overview of visual, interactive, and analytical methods. It introduces criteria for designing interactive visual data analysis solutions, discusses factors influencing the design, and examines the involved processes. The reader is made familiar with the basics of visual encoding and gets to know numerous visualization techniques for multivariate data, temporal data, geo-spatial data, and graph data. A dedicated chapter introduces general concepts for interacting with visualizations and illustrates how modern interaction technology can facilitate the visual data analysis in many ways. Addressing today's large and complex data, the book covers relevant automatic analytical computations to support the visual data analysis. The book also sheds light on advanced concepts for visualization in multi-display environments, user guidance during the data analysis, and progressive visual data analysis. The authors present a top-down perspective on interactive visual data analysis with a focus on concise and clean terminology. Many real-world examples and rich illustrations make the book accessible to a broad interdisciplinary audience from students, to experts in the field, to practitioners in data-intensive application domains. Features: Dedicated to the synthesis of visual, interactive, and analysis methods Systematic top-down view on visualization, interaction, and automatic analysis Broad coverage of fundamental and advanced visualization techniques Comprehensive chapter on interacting with visual representations Extensive integration of automatic computational methods Accessible portrayal of cutting-edge visual analytics technology Foreword by Jack van Wijk For more information, you can also visit the author website, where the book's figures are made available under the CC BY Open Access license.

Publisher's Note: Products purchased from Third Party sellers are not guaranteed by the publisher for quality, authenticity, or access to any online entitlements included with the product. Create and manage high-quality, highly-interactive dashboards and reports using Microsoft Power BI This hands-on guide shows, step by step, how to use the powerful features of Microsoft Power BI to gain meaningful business insights. Written by an expert in the field, the book teaches you how to build accurate data models and design, create, and manage visually-rich and robust data analyses and dashboards. The book includes details on R, Python, and Microsoft's proprietary analytics language DAX. Data Analysis with Microsoft Power BI begins by clearly explaining the dashboard interaction and analysis techniques utilized by business users and proceeds to detail the skills needed to author visualizations from pre-existing data models. From there, you will learn the more advanced skills required to create custom data models from transactional, line-of-business data. Publishing to the Power BI Service (PowerBI.com) and Power BI Report Server are fully covered. *Contains practical exercises based on real-life business scenarios*Access online updates for key new features during the life of this edition*Written by a recognized BI expert and bestselling author

This is a book about how ecologists can integrate remote sensing and GIS in their research. It will allow readers to get started with the application of remote sensing and to understand its potential and limitations. Using practical examples, the book covers all necessary steps from planning field campaigns to deriving ecologically relevant information through remote sensing and modelling of species distributions. An Introduction to Spatial Data Analysis introduces spatial data handling using the open source software Quantum GIS (QGIS). In addition, readers will be guided through their first steps in the R programming language. The authors explain the fundamentals of spatial data handling and analysis, empowering the reader to turn data acquired in the field into actual spatial data. Readers will learn to process and analyse spatial data of different types and interpret the data and results. After finishing this book, readers will be able to address questions such as "What is the distance to the border of the protected area?", "Which points are located close to a road?", "Which fraction of land cover types exist in my study area?" using different software and techniques. This book is for novice spatial data users and does not assume any prior knowledge of spatial data itself or practical experience working with such data sets. Readers will likely include student and professional ecologists, geographers and any environmental scientists or practitioners who need to collect, visualize and analyse spatial data. The software used is the widely applied open source scientific programs QGIS and R. All scripts and data sets used in the book will be provided online at book.ecosens.org. This book covers specific methods including: what to consider before collecting in situ data how to work with spatial data collected in situ the difference between raster and vector data how to acquire further vector and raster data how to create relevant environmental information how to combine and analyse in situ and remote sensing data how to create useful maps for field work and presentations how to use QGIS and R for spatial analysis how to develop analysis scripts

Today, interpreting data is a critical decision-making factor for businesses and organizations. If your job requires you to manage and analyze all kinds of data, turn to "Head First Data Analysis", where you'll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others. Whether you're a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in "Head First Data Analysis" is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool. You'll learn how to: determine which data sources to use for collecting information; assess data quality and distinguish signal from noise; build basic data models to illuminate patterns, and assimilate new information into the models; cope with ambiguous information; design experiments to test hypotheses and draw conclusions; use segmentation to organize your data within discrete market groups; visualize data distributions to reveal new relationships and persuade others; predict the future with sampling and probability models; clean your data to make it useful; and, communicate the results of your analysis to your audience. Using the latest research in cognitive science and learning theory to craft a multi-sensory learning experience, "Head First Data Analysis" uses a visually rich format designed for the way your brain works, not a text-heavy approach that puts you to sleep.

Die Medienmarkte konvergieren. Digitalisierung und technische Innovationen fuhren zu wachsenden Verzahnungen und Kompatibilitaten der traditionellen Medien- und Kommunikationsplattformen. Musik-, Film- oder TV-Inhalte konnen uber Internet oder mobile Telekommunikation verbreitet werden und sind als digitale Datensatze schnell verfugbar. Triple Play" und Interaktionsangebote liefern Massen- und Individualkommunikation aus einer Hand.

Mit dem Zusammenwachsen der Markte gewinnt die Gesamtheit der medienrechtlichen Rahmenbedingungen fur die Branchenbeteiligten zunehmend an Bedeutung.

Das Buch vermittelt einen strukturierten Uberblick uber das Medienrecht, die Rechtsbeziehungen der Beteiligten und die Entwicklung der Markte. Neben den rechtsspezifischen Aspekten der Konvergenz werden u.a. Fragen der Vertragsgestaltung und der Abgrenzung von Lizenzrechten thematisiert."

A practical guide to data-intensive humanities research using the Python programming language The use of quantitative methods in the humanities and related social sciences has increased considerably in recent years, allowing researchers to discover patterns in a vast range of source materials. Despite this growth, there are few resources addressed to students and scholars who wish to take advantage of these powerful tools. Humanities Data Analysis offers the first intermediate-level guide to quantitative data analysis for humanities students and scholars using the Python programming language. This practical textbook, which assumes a basic knowledge of Python, teaches readers the necessary skills for conducting humanities research in the rapidly developing digital environment. The book begins with an overview of the place of data science in the humanities, and proceeds to cover data carpentry: the essential techniques for gathering, cleaning, representing, and transforming textual and tabular data. Then, drawing from real-world, publicly available data sets that cover a variety of scholarly domains, the book delves into detailed case studies. Focusing on textual data analysis, the authors explore such diverse topics as network analysis, genre theory, onomastics, literacy, author attribution, mapping, stylometry, topic modeling, and time series analysis. Exercises and resources for further reading are provided at the end of each chapter. An ideal resource for humanities students and scholars aiming to take their Python skills to the next level, Humanities Data Analysis illustrates the benefits that quantitative methods can bring to complex research questions. Appropriate for advanced undergraduates, graduate students, and scholars with a basic knowledge of Python Applicable to many humanities disciplines, including history, literature, and sociology Offers real-world case studies using publicly available data sets Provides exercises at the end of each chapter for students to test acquired skills Emphasizes visual storytelling via data visualizations

Smartphones, Tablets, Multimedia-Konsolen im Fahrzeug, Microsoft Windows 8, PixelSense, Surface - diese modernen Systeme haben alle eines gemeinsam: bedient werden sie mit Beruhrungen. Die aktuelle Generation bietet dabei durch gleichzeitige Interaktion mit mehreren Fingern oder sogar Personen viele neue Moeglichkeiten, aber auch neue Herausforderungen. Wie koennen existierende Anwendungen portiert werden? Welche Gesten sind intuitiv fur Benutzer? Welche Technologien stehen zur Verfugung und was sind deren Vor- und Nachteile? Dabei hat quasi uber Nacht ein neues Interaktionsparadigma in die Gesellschaft Einzug gehalten. Mit Multitouch-Geraten und Apps werden heute Milliarden Euro umgesetzt. Fur Praktiker ebenso wie fur Wissenschaftler bietet das Buch aktuelle und neue Einsichten in dieses wichtige Thema. Wissenschaftliche Erkenntnisse und praktische Handreichungen zum grossen Thema Multi-Touch-Interaktion leiten die Leser in der softwaretechnischen Nutzung und Interaktionsgestaltung an, bieten aber auch einen thematischen UEberblick fur Interessierte.

If you're like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions

Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(R) presents an applied and interactive approach to data mining. Featuring hands-on applications with JMP Pro(R), a statistical package from the SAS Institute, the book uses engaging, real-world examples to build a theoretical and practical understanding of key data mining methods, especially predictive models for classification and prediction. Topics include data visualization, dimension reduction techniques, clustering, linear and logistic regression, classification and regression trees, discriminant analysis, naive Bayes, neural networks, uplift modeling, ensemble models, and time series forecasting. Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(R) also includes: * Detailed summaries that supply an outline of key topics at the beginning of each chapter * End-of-chapter examples and exercises that allow readers to expand their comprehension of the presented material * Data-rich case studies to illustrate various applications of data mining techniques * A companion website with over two dozen data sets, exercises and case study solutions, and slides for instructors Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro(R) is an excellent textbook for advanced undergraduate and graduate-level courses on data mining, predictive analytics, and business analytics. The book is also a one-of-a-kind resource for data scientists, analysts, researchers, and practitioners working with analytics in the fields of management, finance, marketing, information technology, healthcare, education, and any other data-rich field. Galit Shmueli, PhD, is Distinguished Professor at National Tsing Hua University s Institute of Service Science. She has designed and instructed data mining courses since 2004 at University of Maryland, Statistics.com, Indian School of Business, and National Tsing Hua University, Taiwan. Professor Shmueli is known for her research and teaching in business analytics, with a focus on statistical and data mining methods in information systems and healthcare. She has authored over 70 journal articles, books, textbooks, and book chapters, including Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition, also published by Wiley. Peter C. Bruce is President and Founder of the Institute for Statistics Education at www.statistics.com He has written multiple journal articles and is the developer of Resampling Stats software. He is the author of Introductory Statistics and Analytics: A Resampling Perspective and co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner (R), Third Edition, both published by Wiley. Mia Stephens is Academic Ambassador at JMP(R), a division of SAS Institute. Prior to joining SAS, she was an adjunct professor of statistics at the University of New Hampshire and a founding member of the North Haven Group LLC, a statistical training and consulting company. She is the co-author of three other books, including Visual Six Sigma: Making Data Analysis Lean, Second Edition, also published by Wiley. Nitin R. Patel, PhD, is Chairman and cofounder of Cytel, Inc., based in Cambridge, Massachusetts. A Fellow of the American Statistical Association, Dr. Patel has also served as a Visiting Professor at the Massachusetts Institute of Technology and at Harvard University. He is a Fellow of the Computer Society of India and was a professor at the Indian Institute of Management, Ahmedabad, for 15 years. He is co-author of Data Mining for Business Analytics: Concepts, Techniques, and Applications in XLMiner(R), Third Edition, also published by Wiley.

Was ist Informationsdesign? Welche Designdisziplinen spielen dabei eine Rolle? Und wo liegen Schnittstellen zu anderen Disziplinen wie Usability-Engineering und Informationsarchitektur? Das Kompendium bietet eine umfassende Einfuhrung in theoretische und gestalterische Grundlagen, in Geschichte und Praxis des Informationsdesigns. Verstandlich und anschaulich beschreiben die Autoren Teildisziplinen und Aufgabenfelder des Informationsdesigns: von Interaktionsdesign, Ausstellungsdesign und Signaletik uber Corporate Design, Textdesign und Sounddesign bis hin zu Informationsdidaktik und Informationspsychologie. Begriffsdefinitionen, Tipps sowie Beispiele aus der Praxis machen das Kompendium Informationsdesign zu einem Handbuch fur Studierende, Dozenten und Praktiker.

Das vorliegende Buch stellt den Controller 68332 aus der 68300-Familie des Herstellers Motorola vor. Mit seiner 32 bit Struktur, der umfangreichen Peripherie und einem Adressbereich von 16 MByte gehort er zur oberen Leistungsklasse. Rund 60 Programmbeispiele und 30 Ubungsaufgaben vertiefen den Stoff."

If you're a business team leader, CIO, business analyst, or developer interested in how Apache Hadoop and Apache HBase-related technologies can address problems involving large-scale data in cost-effective ways, this book is for you. Using real-world stories and situations, authors Ted Dunning and Ellen Friedman show Hadoop newcomers and seasoned users alike how NoSQL databases and Hadoop can solve a variety of business and research issues. You'll learn about early decisions and pre-planning that can make the process easier and more productive. If you're already using these technologies, you'll discover ways to gain the full range of benefits possible with Hadoop. While you don't need a deep technical background to get started, this book does provide expert guidance to help managers, architects, and practitioners succeed with their Hadoop projects.Examine a day in the life of big data: India's ambitious Aadhaar project; review tools in the Hadoop ecosystem such as Apache's Spark, Storm, and Drill to learn how they can help you; pick up a collection of technical and strategic tips that have helped others succeed with Hadoop; learn from several prototypical Hadoop use cases, based on how organizations have actually applied the technology. You can explore real-world stories that reveal how MapR customers combine use cases when putting Hadoop and NoSQL to work, including in production.

Since long before computers were even thought of, data has been collected and organized by diverse cultures across the world. Once access to the Internet became a reality for large swathes of the world's population, the amount of data generated each day became huge, and continues to grow exponentially. It includes all our uploaded documents, video, and photos, all our social media traffic, our online shopping, even the GPS data from our cars. 'Big Data' represents a qualitative change, not simply a quantitative one. The term refers both to the new technologies involved, and to the way it can be used by business and government. Dawn E. Holmes uses a variety of case studies to explain how data is stored, analysed, and exploited by a variety of bodies from big companies to organizations concerned with disease control. Big data is transforming the way businesses operate, and the way medical research can be carried out. At the same time, it raises important ethical issues; Holmes discusses cases such as the Snowden affair, data security, and domestic smart devices which can be hijacked by hackers. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.

Anomaly detection is the detective work of machine learning: finding the unusual, catching the fraud, discovering strange activity in large and complex datasets. But, unlike Sherlock Holmes, you may not know what the puzzle is, much less what "suspects" you're looking for. This O'Reilly report uses practical examples to explain how the underlying concepts of anomaly detection work. From banking security to natural sciences, medicine, and marketing, anomaly detection has many useful applications in this age of big data. And the search for anomalies will intensify once the Internet of Things spawns even more new types of data. The concepts described in this report will help you tackle anomaly detection in your own project. Use probabilistic models to predict what's normal and contrast that to what you observe Set an adaptive threshold to determine which data falls outside of the normal range, using the t-digest algorithm Establish normal fluctuations in complex systems and signals (such as an EKG) with a more adaptive probablistic model Use historical data to discover anomalies in sporadic event streams, such as web traffic Learn how to use deviations in expected behavior to trigger fraud alerts

In 2010, as focal point for information technology management across the government, OMB's Federal Chief Information Officer launched the Federal Data Center Consolidation Initiative to consolidate the growing number of centres. The objectives of this book are to evaluate the extent to which agencies have achieved cost savings to date and identified future savings through their consolidation efforts; identify agencies' notable consolidation successes and challenges in achieving cost savings; and evaluate the extent to which data center optimisation metrics have been established.