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.

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.

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.

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.

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

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.

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You'll work with a case study throughout the book to help you learn the entire data analysis process--from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts. The new edition switches over to Pandas for data processing. It also includes new chapters on multiple regression, survival analysis, missing value imputation and resampling.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data

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

A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: - Illustrations of the use of R software to perform all the analyses in the book - A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis - New sections in many chapters introducing the Bayesian approach for the methods of that chapter - More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets - An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.

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.

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

This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory. Students will find the book both stimulating and accessible, while practitioners will be guided smoothly through the material required for a good grasp of the theory and its applications. The concepts are introduced gradually in accessible and self-contained stages, while the presentation is rigorous and thorough. Pointers to relevant literature and web sites containing software make it an ideal starting point for further study.

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."

The essential textbook on agent-based modeling-now fully updated and expanded Agent-Based and Individual-Based Modeling has become the standard textbook on the subject for classroom use and self-instruction. Drawing on the latest version of NetLogo and fully updated with new examples, exercises, and an enhanced text for easier comprehension, this is the essential resource for anyone seeking to understand how the dynamics of biological, social, and other complex systems arise from the characteristics of the agents that make up these systems. Steven Railsback and Volker Grimm lead students stepwise through the processes of designing, programming, documenting, and doing scientific research with agent-based models, focusing on the adaptive behaviors that make these models necessary. They cover the fundamentals of modeling and model analysis, introduce key modeling concepts, and demonstrate how to implement them using NetLogo. They also address pattern-oriented modeling, an invaluable strategy for modeling real-world problems and developing theory. This accessible and authoritative book focuses on modeling as a tool for understanding real complex systems. It explains how to pose a specific question, use observations from actual systems to design models, write and test software, and more. A hands-on introduction that guides students from conceptual design to computer implementation to analysis Filled with new examples and exercises and compatible with the latest version of NetLogo Ideal for students and researchers across the natural and social sciences Written by two leading practitioners Supported by extensive instructional materials at www.railsback-grimm-abm-book.com

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.

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.

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.

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

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."

Learn how to get better answers in map analysis when you use spatial measurements and statistics. Spatial measurements and statistics give you a powerful way to analyze geospatial data, but you don't need to understand complex mathematical theories to apply statistical tools and get meaningful results in your projects. The Esri Guide to GIS Analysis, Volume 2: Spatial Measurements and Statistics, second edition, builds on Volume 1 by taking you to the next step of GIS analysis. Learn to answer such questions as, how are features distributed? What is the pattern created by a set of features? Where can clusters be found? This book introduces readers to basic statistical concepts and some of the most common spatial statistics tasks: measuring distributions, identifying patterns and clusters, and analyzing relationships. Updated with the latest and most useful software tools and revised explanations, each chapter in The Esri Guide to GIS Analysis, Volume 2 is organized to answer basic questions about the topic. Explore how spatial statistical tools can be applied in a range of disciplines, from public health to habitat conservation. Learn how to quantify patterns beyond visualizing them in maps. Examine spatial clusters through an updated chapter on identifying clusters. Use The Esri Guide to GIS Analysis, Volume 2, second edition, to understand the statistical methods and tools that can move your work past mapping and visualization to more quantitative statistical assessment.

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

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.

Advanced Data Science and Analytics with Python enables data scientists to continue developing their skills and apply them in business as well as academic settings. The subjects discussed in this book are complementary and a follow-up to the topics discussed in Data Science and Analytics with Python. The aim is to cover important advanced areas in data science using tools developed in Python such as SciKit-learn, Pandas, Numpy, Beautiful Soup, NLTK, NetworkX and others. The model development is supported by the use of frameworks such as Keras, TensorFlow and Core ML, as well as Swift for the development of iOS and MacOS applications. Features: Targets readers with a background in programming, who are interested in the tools used in data analytics and data science Uses Python throughout Presents tools, alongside solved examples, with steps that the reader can easily reproduce and adapt to their needs Focuses on the practical use of the tools rather than on lengthy explanations Provides the reader with the opportunity to use the book whenever needed rather than following a sequential path The book can be read independently from the previous volume and each of the chapters in this volume is sufficiently independent from the others, providing flexibility for the reader. Each of the topics addressed in the book tackles the data science workflow from a practical perspective, concentrating on the process and results obtained. The implementation and deployment of trained models are central to the book. Time series analysis, natural language processing, topic modelling, social network analysis, neural networks and deep learning are comprehensively covered. The book discusses the need to develop data products and addresses the subject of bringing models to their intended audiences - in this case, literally to the users' fingertips in the form of an iPhone app. About the Author Dr. Jesus Rogel-Salazar is a lead data scientist in the field, working for companies such as Tympa Health Technologies, Barclays, AKQA, IBM Data Science Studio and Dow Jones. He is a visiting researcher at the Department of Physics at Imperial College London, UK and a member of the School of Physics, Astronomy and Mathematics at the University of Hertfordshire, UK.