This book provides an account of the use of computational tactical metrics in improving sports analysis, in particular the use of Global Positioning System (GPS) data in soccer. As well as offering a practical perspective on collective behavioural analysis, it introduces the computational metrics available in the literature that allow readers to identify collective behaviour and patterns of play in team sports. These metrics only require the bio-dimensional geo-referencing information from GPS or video-tracking systems to provide qualitative and quantitative information about the tactical behaviour of players and the inter-relationships between teammates and their opponents. Exercises, experimental cases and algorithms enable readers to fully comprehend how to compute these metrics, as well as introducing them to the ultimate performance analysis tool, which is the basis to run them on. The script to compute the metrics is presented in Python. The book is a valuable resource for professional analysts as well students and researchers in the field of sports analysis wanting to optimise the use of GPS trackers in soccer.

This text introduces and provides instruction on the design and analysis of experiments for a broad audience. Formed by decades of teaching, consulting, and industrial experience in the Design of Experiments field, this new edition contains updated examples, exercises, and situations covering the science and engineering practice. This text minimizes the amount of mathematical detail, while still doing full justice to the mathematical rigor of the presentation and the precision of statements, making the text accessible for those who have little experience with design of experiments and who need some practical advice on using such designs to solve day-to-day problems. Additionally, an intuitive understanding of the principles is always emphasized, with helpful hints throughout.

Alpha-Versionen sind Lehrbucher, Gesetze, Hochglanzprospekte, Aktienneuemissionsanzeigen, Regierungserklarungen. Dahinter ist das Reale. Hinter den Lehrbuchern die vorlesende Forscherpersonlichkeit, hinter dem Prospekt der Rat des erfahrenen Fachverkaufers. Alpha-Versionen meiden Urteile, Meinungen und Leidenschaftlichkeit. Dieses Buch ist kompromisslos beta. Hier werden die schnellen Veranderungen der Informationsgesellschaft mit dem einhergehenden taglichen Wahnsinn aus moglichen und unmoglichen Perspektiven aufs Korn genommen - und wo es nicht anders geht, wird das zu arg Provozierende in Schwarzhumorsatire geniessbar gemacht ("Nicht nur zur Neujahrszeit" oder "Das Ende der DGeneration").Das Buch enthalt die bisherigen Texte der "Kult"-Kolumne Beta-inside (Informatik-Spektrum) des "Wild Duck" Autors, erganzt um Satiren, die eher "das Schonste" am Buche sind. Die Neuauflage wurde um ein Nachwort des Autors erweitert

This book constitutes revised selected papers from the 4th ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2016, held in Riva del Garda, Italy, in September 2016. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book and handle topics such as time series forecasting, the detection of faults, cyber security, smart grid and smart cities, technology integration, demand response and many others.

This book constitutes the thoroughly refereed post-workshop proceedings of the 6th International Workshop on Big Data Benchmarking, WBDB 2015, held in Toronto, ON, Canada, in June 2015 and the 7th International Workshop, WBDB 2015, held in New Delhi, India, in December 2015. The 8 full papers presented in this book were carefully reviewed and selected from 22 submissions. They deal with recent trends in big data and HPC convergence, new proposals for big data benchmarking, as well as tooling and performance results.

Discover relevant questions-and detailed answers-to help you prepare for job interviews and break into the field of analytics. This book contains more than 200 questions based on consultations with hiring managers and technical professionals already working in analytics. Interview Questions in Business Analytics: How to Ace Interviews and Get the Job You Want fills a gap in information on business analytics for job seekers. Bhasker Gupta, the founder and editor of Analytics India Magazine, has come up with more than 200 questions job applicants are likely to face in an interview. Covering data preparation, statistics, analytics implementation, as well as other crucial topics favored by interviewers, this book: Provides 200+ interview questions often asked by recruiters and hiring managers in global corporations Offers short and to-the-point answers to the depth required, while looking at the problem from all angles Provides a full range of interview questions for jobs ranging from junior analytics to senior data scientists and managers Offers analytics professionals a quick reference on topics in analytics Using a question-and-answer format from start to finish, Interview Questions in Business Analytics: How to Ace Interviews and Get the Job You Want will help you grasp concepts sooner and with deep clarity. The book therefore also serves as a primer on analytics and covers issues relating to business implementation. You will learn about not just the how and what of analytics, but also the why and when. This book will thus ensure that you are well prepared for interviews-putting your dream job well within reach. Business analytics is currently one of the hottest and trendiest areas for technical professionals. With the rise of the profession, there is significant job growth. Even so, it's not easy to get a job in the field, because you need knowledge of subjects such as statistics, databases, and IT services. Candidates must also possess keen business acumen. What's more, employers cast a cold critical eye on all applicants, making the task of getting a job even more difficult. What You'll Learn The 200 questions in this book cover such topics as: * The different types of data used in analytics * How analytics are put to use in different industries * The process of hypothesis testing * Predictive vs. descriptive analytics * Correlation, regression, segmentation and advanced statistics * Predictive modeling Who This Book Is For Those aspiring to jobs in business analytics, including recent graduates and technical professionals looking for a new or better job. Job interviewers will also find the book helpful in preparing interview questions.

One-stop solution for NLP practitioners, ML developers, and data scientists to build effective NLP systems that can perform real-world complicated tasks Key Features Apply deep learning algorithms and techniques such as BiLSTMS, CRFs, BPE and more using TensorFlow 2 Explore applications like text generation, summarization, weakly supervised labelling and more Read cutting edge material with seminal papers provided in the GitHub repository with full working code Book DescriptionRecently, there have been tremendous advances in NLP, and we are now moving from research labs into practical applications. This book comes with a perfect blend of both the theoretical and practical aspects of trending and complex NLP techniques. The book is focused on innovative applications in the field of NLP, language generation, and dialogue systems. It helps you apply the concepts of pre-processing text using techniques such as tokenization, parts of speech tagging, and lemmatization using popular libraries such as Stanford NLP and SpaCy. You will build Named Entity Recognition (NER) from scratch using Conditional Random Fields and Viterbi Decoding on top of RNNs. The book covers key emerging areas such as generating text for use in sentence completion and text summarization, bridging images and text by generating captions for images, and managing dialogue aspects of chatbots. You will learn how to apply transfer learning and fine-tuning using TensorFlow 2. Further, it covers practical techniques that can simplify the labelling of textual data. The book also has a working code that is adaptable to your use cases for each tech piece. By the end of the book, you will have an advanced knowledge of the tools, techniques and deep learning architecture used to solve complex NLP problems. What you will learn Grasp important pre-steps in building NLP applications like POS tagging Use transfer and weakly supervised learning using libraries like Snorkel Do sentiment analysis using BERT Apply encoder-decoder NN architectures and beam search for summarizing texts Use Transformer models with attention to bring images and text together Build apps that generate captions and answer questions about images using custom Transformers Use advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest DeepNLP models Who this book is forThis is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. The readers who can benefit the most from this book include intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques and professionals who already use TensorFlow/Python for purposes such as data science, ML, research, analysis, etc.

This book constitutes revised selected papers from the third ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2015, held in Porto, Portugal, in September 2015. The 10 papers presented in this volume were carefully reviewed and selected for inclusion in this book.

With this practical book, you will learn proven methods for anonymizing health data to help your organization share meaningful datasets, without exposing patient identity. Leading experts Khaled El Emam and Luk Arbuckle walk you through a risk-based methodology, using case studies from their efforts to de-identify hundreds of datasets.

Clinical data is valuable for research and other types of analytics, but making it anonymous without compromising data quality is tricky. This book demonstrates techniques for handling different data types, based on the authors' experiences with a maternal-child registry, inpatient discharge abstracts, health insurance claims, electronic medical record databases, and the World Trade Center disaster registry, among others.Understand different methods for working with cross-sectional and longitudinal datasetsAssess the risk of adversaries who attempt to re-identify patients in anonymized datasetsReduce the size and complexity of massive datasets without losing key information or jeopardizing privacyUse methods to anonymize unstructured free-form text dataMinimize the risks inherent in geospatial data, without omitting critical location-based health informationLook at ways to anonymize coding information in health dataLearn the challenge of anonymously linking related datasets

Der Mangel an qualifizierten Softwareentwicklern im deutschsprachigen Raum verscharft sich. Die effektive Zusammenarbeit in weltweit verteilten Teams ist daher ein entscheidender Wettbewerbsfaktor und Offshoring wird immer relevanter. Der Autor moechte das Thema auch kleinen und mittleren Unternehmen naher bringen und die Eintrittsbarrieren fur kostengunstige Offshore-Softwareentwicklungen reduzieren. Er zeigt, wie Unternehmen erfolgreich Offshore-Projekte umsetzen koennen: praxisnah, mit konkreten Fallstudien und Hinweisen zur Projektabwicklung. Dem Leser werden Werkzeuge vermittelt, mit denen er die Risiken in der Abwicklung von Offshore-Projekten reduzieren kann, ohne dass Kostenvorteile verloren gehen.

Use Microsoft SQL Server 2019 to implement, administer, and secure a robust database solution that is disaster-proof and highly available Key Features Explore new features of SQL Server 2019 to set up, administer, and maintain your database solution successfully Develop a dynamic SQL Server environment and streamline big data pipelines Discover best practices for fixing performance issues, database access management, replication, and security Book DescriptionSQL Server is one of the most popular relational database management systems developed by Microsoft. This second edition of the SQL Server Administrator's Guide will not only teach you how to administer an enterprise database, but also help you become proficient at managing and keeping the database available, secure, and stable. You'll start by learning how to set up your SQL Server and configure new and existing environments for optimal use. The book then takes you through designing aspects and delves into performance tuning by showing you how to use indexes effectively. You'll understand certain choices that need to be made about backups, implement security policy, and discover how to keep your environment healthy. Tools available for monitoring and managing a SQL Server database, including automating health reviews, performance checks, and much more, will also be discussed in detail. As you advance, the book covers essential topics such as migration, upgrading, and consolidation, along with the techniques that will help you when things go wrong. Once you've got to grips with integration with Azure and streamlining big data pipelines, you'll learn best practices from industry experts for maintaining a highly reliable database solution. Whether you are an administrator or are looking to get started with database administration, this SQL Server book will help you develop the skills you need to successfully create, design, and deploy database solutions. What you will learn Discover SQL Server 2019's new features and how to implement them Fix performance issues by optimizing queries and making use of indexes Design and use an optimal database management strategy Combine SQL Server 2019 with Azure and manage your solution using various automation techniques Implement efficient backup and recovery techniques in line with security policies Get to grips with migrating, upgrading, and consolidating with SQL Server Set up an AlwaysOn-enabled stable and fast SQL Server 2019 environment Understand how to work with Big Data on SQL Server environments Who this book is forThis book is for database administrators, database developers, and anyone who wants to administer large and multiple databases single-handedly using Microsoft's SQL Server 2019. Basic awareness of database concepts and experience with previous SQL Server versions is required.

Big Data Analytics with Spark is a step-by-step guide for learning Spark, which is an open-source fast and general-purpose cluster computing framework for large-scale data analysis. You will learn how to use Spark for different types of big data analytics projects, including batch, interactive, graph, and stream data analysis as well as machine learning. In addition, this book will help you become a much sought-after Spark expert. Spark is one of the hottest Big Data technologies. The amount of data generated today by devices, applications and users is exploding. Therefore, there is a critical need for tools that can analyze large-scale data and unlock value from it. Spark is a powerful technology that meets that need. You can, for example, use Spark to perform low latency computations through the use of efficient caching and iterative algorithms; leverage the features of its shell for easy and interactive Data analysis; employ its fast batch processing and low latency features to process your real time data streams and so on. As a result, adoption of Spark is rapidly growing and is replacing Hadoop MapReduce as the technology of choice for big data analytics. This book provides an introduction to Spark and related big-data technologies. It covers Spark core and its add-on libraries, including Spark SQL, Spark Streaming, GraphX, and MLlib. Big Data Analytics with Spark is therefore written for busy professionals who prefer learning a new technology from a consolidated source instead of spending countless hours on the Internet trying to pick bits and pieces from different sources. The book also provides a chapter on Scala, the hottest functional programming language, and the program that underlies Spark. You'll learn the basics of functional programming in Scala, so that you can write Spark applications in it. What's more, Big Data Analytics with Spark provides an introduction to other big data technologies that are commonly used along with Spark, like Hive, Avro, Kafka and so on. So the book is self-sufficient; all the technologies that you need to know to use Spark are covered. The only thing that you are expected to know is programming in any language. There is a critical shortage of people with big data expertise, so companies are willing to pay top dollar for people with skills in areas like Spark and Scala. So reading this book and absorbing its principles will provide a boost-possibly a big boost-to your career.

Build, monitor, and manage real-time data pipelines to create data engineering infrastructure efficiently using open-source Apache projects Key Features Become well-versed in data architectures, data preparation, and data optimization skills with the help of practical examples Design data models and learn how to extract, transform, and load (ETL) data using Python Schedule, automate, and monitor complex data pipelines in production Book DescriptionData engineering provides the foundation for data science and analytics, and forms an important part of all businesses. This book will help you to explore various tools and methods that are used for understanding the data engineering process using Python. The book will show you how to tackle challenges commonly faced in different aspects of data engineering. You'll start with an introduction to the basics of data engineering, along with the technologies and frameworks required to build data pipelines to work with large datasets. You'll learn how to transform and clean data and perform analytics to get the most out of your data. As you advance, you'll discover how to work with big data of varying complexity and production databases, and build data pipelines. Using real-world examples, you'll build architectures on which you'll learn how to deploy data pipelines. By the end of this Python book, you'll have gained a clear understanding of data modeling techniques, and will be able to confidently build data engineering pipelines for tracking data, running quality checks, and making necessary changes in production. What you will learn Understand how data engineering supports data science workflows Discover how to extract data from files and databases and then clean, transform, and enrich it Configure processors for handling different file formats as well as both relational and NoSQL databases Find out how to implement a data pipeline and dashboard to visualize results Use staging and validation to check data before landing in the warehouse Build real-time pipelines with staging areas that perform validation and handle failures Get to grips with deploying pipelines in the production environment Who this book is forThis book is for data analysts, ETL developers, and anyone looking to get started with or transition to the field of data engineering or refresh their knowledge of data engineering using Python. This book will also be useful for students planning to build a career in data engineering or IT professionals preparing for a transition. No previous knowledge of data engineering is required.

Master the skills necessary to hire and manage a team of highly skilled individuals to design, build, and implement applications and systems based on advanced analytics and AI Key Features Learn to create an operationally effective advanced analytics team in a corporate environment Select and undertake projects that have a high probability of success and deliver the improved top and bottom-line results Understand how to create relationships with executives, senior managers, peers, and subject matter experts that lead to team collaboration, increased funding, and long-term success for you and your team Book DescriptionIn Building Analytics Teams, John K. Thompson, with his 30+ years of experience and expertise, illustrates the fundamental concepts of building and managing a high-performance analytics team, including what to do, who to hire, projects to undertake, and what to avoid in the journey of building an analytically sound team. The core processes in creating an effective analytics team and the importance of the business decision-making life cycle are explored to help achieve initial and sustainable success. The book demonstrates the various traits of a successful and high-performing analytics team and then delineates the path to achieve this with insights on the mindset, advanced analytics models, and predictions based on data analytics. It also emphasizes the significance of the macro and micro processes required to evolve in response to rapidly changing business needs. The book dives into the methods and practices of managing, developing, and leading an analytics team. Once you've brought the team up to speed, the book explains how to govern executive expectations and select winning projects. By the end of this book, you will have acquired the knowledge to create an effective business analytics team and develop a production environment that delivers ongoing operational improvements for your organization. What you will learn Avoid organizational and technological pitfalls of moving from a defined project to a production environment Enable team members to focus on higher-value work and tasks Build Advanced Analytics and Artificial Intelligence (AA&AI) functions in an organization Outsource certain projects to competent and capable third parties Support the operational areas that intend to invest in business intelligence, descriptive statistics, and small-scale predictive analytics Analyze the operational area, the processes, the data, and the organizational resistance Who this book is forThis book is for senior executives, senior and junior managers, and those who are working as part of a team that is accountable for designing, building, delivering and ensuring business success through advanced analytics and artificial intelligence systems and applications. At least 5 to 10 years of experience in driving your organization to a higher level of efficiency will be helpful.

This book contains extended and revised versions of a set of selected papers from two events organized by the Euro Working Group on Decision Support Systems (EWG-DSS), which were held in Toulouse, France and Barcelona, Spain, in June and July 2014. Overall, 8 papers were accepted for publication in this edition after a rigorous review process through at least three internationally known experts from the EWG-DSS Program Committee and external invited reviewers. The selected papers focus on knowledge management and sharing, and on information models developed to support various decision processes.

This book constitutes the proceedings of the Workshops held at the International Conference on Social Informatics, SocInfo 2014, which took place in Barcelona, Spain, in November 2014. This year SocInfo 2014 included nine satellite workshops: the City Labs Workshop, the Workshop on Criminal Network Analysis and Mining, CRIMENET, the Workshop on Interaction and Exchange in Social Media, DYAD, the Workshop on Exploration of Games and Gamers, EGG, the Workshop on HistoInformatics, the Workshop on Socio-Economic Dynamics, Networks and Agent-based Models, SEDNAM, the Workshop on Social Influence, SI, the Workshop on Social Scientists Working with Start-Ups and the Workshop on Social Media in Crowdsourcing and Human Computation, SoHuman.

Construct and implement a data warehousing plan.
In their efforts to collect information that will give them an edge, many companies have amassed vast amounts of data. Often this data becomes unwieldy and difficult to translate into anything useful. Data warehousing, storing all of this data in a system that allows for rapid retrieval of customized information, is the solution. This book, written by a data warehousing authority, is a step-by-step guide to creating and managing a data warehouse from start to finish, reviewing marketing, technology, and design issues.

This book constitutes the thoroughly refereed post conference proceedings of the First and Second International Workshops on In Memory Data Management and Analysis held in Riva del Garda, Italy, August 2013 and Hangzhou, China, in September 2014. The 11 revised full papers were carefully reviewed and selected from 18 submissions and cover topics from main-memory graph analytics platforms to main-memory OLTP applications.

The two-volume set LNCS 9014 and LNCS 9015 constitutes the refereed proceedings of the 12th International Conference on Theory of Cryptography, TCC 2015, held in Warsaw, Poland in March 2015. The 52 revised full papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on foundations, symmetric key, multiparty computation, concurrent and resettable security, non-malleable codes and tampering, privacy amplification, encryption an key exchange, pseudorandom functions and applications, proofs and verifiable computation, differential privacy, functional encryption, obfuscation.

The two-volume set LNCS 9014 and LNCS 9015 constitutes the refereed proceedings of the 12th International Conference on Theory of Cryptography, TCC 2015, held in Warsaw, Poland in March 2015. The 52 revised full papers presented were carefully reviewed and selected from 137 submissions. The papers are organized in topical sections on foundations, symmetric key, multiparty computation, concurrent and resettable security, non-malleable codes and tampering, privacy amplification, encryption an key exchange, pseudorandom functions and applications, proofs and verifiable computation, differential privacy, functional encryption, obfuscation.

In dem Buch werden Methoden vorgestellt, mit denen ubersehenes IT-Potenzial in Organisation genutzt werden kann. Dabei geht die Autorin davon aus, dass das Wissen bereits vorhanden ist und nur gehoben werden muss. Mit Checklisten und Tipps fur die Umsetzung."

Das kompakte Fachbuch gibt einen UEberblick uber die Moeglichkeiten von "Big Data" im Gesundheitswesen und beschreibt anhand von ausgewahlten Szenarien moegliche Einsatzgebiete. Die Autoren erlautern zentrale Systemkomponenten und IT-Standards und thematisieren anhand wichtiger Daten des Gesundheitswesens die Notwendigkeit der Strukturierung und Modellierung von Daten. Das Buch gibt Hinweise wie Geschaftsprozesse im Gesundheitswesen dokumentiert, analysiert und verbessert werden koennen. Anwendungsszenarien, wie die Datenanalysen fur Krankenhauser, Labore, Versicherungen und die Pharmaindustrie, zeigen die praktische Relevanz des Themas. Aber auch rechtliche und ethische Aspekte werden inhaltlich angeschnitten. Ein Buch fur Entscheider in der medizinischen Leitung und Verwaltung von Krankenhausern, Fachleute sowie niedergelassene AErzte und Apotheker, aber auch Personen in Ausbildung und Studium im Gesundheitswesen.

This book constitutes revised selected papers from the second ECML PKDD Workshop on Data Analytics for Renewable Energy Integration, DARE 2014, held in Nancy, France, in September 2014. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book.

Data Scientists at Work is a collection of interviews with sixteen of the world's most influential and innovative data scientists from across the spectrum of this hot new profession. "Data scientist is the sexiest job in the 21st century," according to the Harvard Business Review. By 2018, the United States will experience a shortage of 190,000 skilled data scientists, according to a McKinsey report. Through incisive in-depth interviews, this book mines the what, how, and why of the practice of data science from the stories, ideas, shop talk, and forecasts of its preeminent practitioners across diverse industries: social network (Yann LeCun, Facebook); professional network (Daniel Tunkelang, LinkedIn); venture capital (Roger Ehrenberg, IA Ventures); enterprise cloud computing and neuroscience (Eric Jonas, formerly Salesforce.com); newspaper and media (Chris Wiggins, The New York Times); streaming television (Caitlin Smallwood, Netflix); music forecast (Victor Hu, Next Big Sound); strategic intelligence (Amy Heineike, Quid); environmental big data (Andre Karpis ts enko, Planet OS); geospatial marketing intelligence (Jonathan Lenaghan, PlaceIQ); advertising (Claudia Perlich, Dstillery); fashion e-commerce (Anna Smith, Rent the Runway); specialty retail (Erin Shellman, Nordstrom); email marketing (John Foreman, MailChimp); predictive sales intelligence (Kira Radinsky, SalesPredict); and humanitarian nonprofit (Jake Porway, DataKind). The book features a stimulating foreword by Google's Director of Research, Peter Norvig. Each of these data scientists shares how he or she tailors the torrent-taming techniques of big data, data visualization, search, and statistics to specific jobs by dint of ingenuity, imagination, patience, and passion. Data Scientists at Work parts the curtain on the interviewees' earliest data projects, how they became data scientists, their discoveries and surprises in working with data, their thoughts on the past, present, and future of the profession, their experiences of team collaboration within their organizations, and the insights they have gained as they get their hands dirty refining mountains of raw data into objects of commercial, scientific, and educational value for their organizations and clients.