Datafangst og dataanalyse

Get to grips with solving real-world NLP problems, such as dependency parsing, information extraction, topic modeling, and text data visualizationKey Features:Analyze varying complexities of text using popular Python packages such as NLTK, spaCy, sklearn, and gensimImplement common and not-so-common linguistic processing tasks using Python librariesOvercome the common challenges faced while implementing NLP pipelinesBook Description:Python is the most widely used language for natural language processing (NLP) thanks to its extensive tools and libraries for analyzing text and extracting computer-usable data. This book will take you through a range of techniques for text processing, from basics such as parsing the parts of speech to complex topics such as topic modeling, text classification, and visualization.Starting with an overview of NLP, the book presents recipes for dividing text into sentences, stemming and lemmatization, removing stopwords, and parts of speech tagging to help you to prepare your data. You'll then learn ways of extracting and representing grammatical information, such as dependency parsing and anaphora resolution, discover different ways of representing the semantics using bag-of-words, TF-IDF, word embeddings, and BERT, and develop skills for text classification using keywords, SVMs, LSTMs, and other techniques. As you advance, you'll also see how to extract information from text, implement unsupervised and supervised techniques for topic modeling, and perform topic modeling of short texts, such as tweets. Additionally, the book shows you how to develop chatbots using NLTK and Rasa and visualize text data.By the end of this NLP book, you'll have developed the skills to use a powerful set of tools for text processing.What You Will Learn:Become well-versed with basic and advanced NLP techniques in PythonRepresent grammatical information in text using spaCy, and semantic information using bag-of-words, TF-IDF, and word embeddingsPerform text classification using different methods, including SVMs and LSTMsExplore different techniques for topic modeling such as K-means, LDA, NMF, and BERTWork with visualization techniques such as NER and word clouds for different NLP toolsBuild a basic chatbot using NLTK and RasaExtract information from text using regular expression techniques and statistical and deep learning toolsWho this book is for:This book is for data scientists and professionals who want to learn how to work with text. Intermediate knowledge of Python will help you to make the most out of this book. If you are an NLP practitioner, this book will serve as a code reference when working on your projects.

Getting early indication of potential contractor performance risks and contract execution issues is critical for proactive acquisition management. When contractors are in danger of not meeting contractual performance goals, Department of the Air Force (DAF) acquisition management may not be fully aware of the shortfall until, for example, a schedule deadline is missed, government testing indicates poor system's technical performance, or costs exceed expectations. Concerns continue to be raised about cost and schedule growth in acquisition and experts postulate about a lack of knowledge about the status of acquisition programs. In this report, the authors focus on metrics to identify emerging execution problems earlier than traditional acquisition oversight systems to enable more-proactive risk and performance management. They summarize their findings, which include a taxonomy of contractor relative risks, leading indicators of performance, relevant data sources, risk measures and equations, and a prototype that implements some of these findings using real data sources. This research should be of interest to acquisition professionals and leadership who are searching for ways to improve acquisition performance through early identification of potential relative contractor risks and execution problems to inform active program management and mitigation of risks. The prototype should be of interest to acquisition officials (from program managers to milestone decision authorities) to help them access more data in an easy-to-understand way so they can focus their limited time on areas that require increased management attention. This approach should be useful during any phase of the acquisition process.

Bachelorarbeit aus dem Jahr 2019 im Fachbereich BWL - Sonstiges, Hochschule RheinMain, Sprache: Deutsch, Abstract: In erster Linie soll diese Arbeit ein grundsätzliches Verständnis der Funktionsweise von Blockchain und anderen Distributed Ledger Technologien verschaffen. Nebstdem wird herausgearbeitet, bei welchen Geschäftsprozessen die Implementierung der Blockchain sinnvoll ist. Da die Blockchain-Technologie noch ein sehr junges Thema darstellt, steht bisher wenig wissenschaftliche Literatur zur Verfügung. Daher besteht der größte Teil meiner Quellen aus Onlineliteratur. Nichtsdestotrotz wurden aber auch physische Literatur wie z.B. Bücher, Magazine und Vorträge als Quellen in Gebrauch genommen.Die Blockchain ist in aller Munde. Kaum ein IT-Thema geht momentan so stark durch die Presse wie sie. Es wurden bereits weltweit 620 Millionen US-Dollar in diese potenzielle "disruptive Innovation" investiert. Seit einigen Jahren bereits kommen aus der Blockchain-Szene die verschiedensten Vorschläge, was denn mithilfe einer Blockchain alles abgebildet werden soll.Von dem komplexen Ablauf von Prozessen zwischen verschiedenen Unternehmen bis hin zur singulären Beobachtung einer Person und ihren privaten als auch geschäftlichen Vorgängen ist alles möglich. Durch die Implementierung der Blockchain-Technologie sollen Transaktionen in unterschiedlichsten Branchen einfach, genau und sicher gehalten werden.Des Weiteren ist zu beachten, dass sie erstmalige Anwendung der Blockchain eng mit den Krisen des Jahren 2008 verknüpft ist. Nach der Krise in dem Finanz- und Versicherungssektor und dem Platzen der Immobilienblase in den Vereinigten Staaten war es an der Zeit das gegenseitige Vertrauen bei Geschäftsprozessen wiederherzustellen, und das sogar ohne Mittelsmann. Dafür aber mit mehr Kontrolle.Bis dato ist die Bürokratie noch auf der ganzen Welt stark von Papier geprägt. Digitalisierung ist auf dem besten Weg sich global mehr und mehr weiterzuentwickeln, jedoch ist der Großteil von Transaktionen in verschiedensten Branchen noch papierlastig. Insbesondere ist hier die Immobilienbranche genauer zu betrachten. Die Immobilienbranche gilt stets als träge, rückständig und intransparent. Ein großer Teil der Geschäftsprozesse in dieser Branche ist ineffizient und stark fehleranfällig, da die Dokumentation nach wie vor in Papierform erfolgt. Lange Abwicklungszeiten und hohe Kosten stellen Begleiterscheinungenjeder Immobilientransaktion dar.

From facial recognition-capable of checking us onto flights or identifying undocumented residents-to automated decision systems that inform everything from who gets loans to who receives bail, each of us moves through a world determined by data-empowered algorithms. But these technologies didn't just appear: they are part of a history that goes back centuries, from the birth of eugenics in Victorian Britain to the development of Google search.Expanding on the popular course they created at Columbia University, Chris Wiggins and Matthew Jones illuminate the ways in which data has long been used as a tool and a weapon in arguing for what is true, as well as a means of rearranging or defending power. By understanding the trajectory of data-where it has been and where it might yet go-Wiggins and Jones argue that we can understand how to bend it to ends that we collectively choose, with intentionality and purpose.

"Learn how to apply the principles of data science to improve business strategies. Chapters cover concepts such as A/B testing, supervised and unsupervised machine learning, web scraping, and more. Each concept is illustrated using real-world business applications, real-world data, and useful Python code examples"--

La apasionante historia de Cambridge Analytica y el Big Data. ¿Está realmente a salvo nuestra democracia tras la victoria de Trump? La dictadura de los datos revela cómo han utilizado nuestros datos y nos advierte cómo podrían volver a hacerlo. Saben lo que compras. Brittany Kaiser, una novata asesora política especializada en Derechos Humanos y Relaciones Internacionales, creía que los datos recogidos y analizados por los smartphones y las redes sociales estaban en buenas manos hasta que conoció a Alexander Nix, el carismático líder de una nueva empresa de comunicación política llamada Cambridge Analytica. Lo que empezó siendo sólo un puesto de trabajo, pronto se convierte en una operación infame con el objetivo de ayudar a la elección de Trump o interferir en el referéndum que dio paso al Brexit. La dictadura de los datos es la dramática y perturbadora historia del ascenso y la caída de Cambridge Analytica, la empresa responsable de reunir, manipular y enriquecerse de nuestros datos. Kaiser, el testigo directo de este atropello, advierte que las repercusiones podrían seguir perjudicándonos en las elecciones de 2020 si no hacemos nada al respecto. Una poderosa y esclarecedora autobiografía sobre uno de los temas más relevantes de nuestra época. «¿Quiénes se convierten en informantes? Aquellos que son leales no solo a sus carreras, a su jefe, a su agencia o incluso al mismo presidente. Son aquellos que son leales por encima de todo a la Constitución [...] Son ese tipo de patriota. Esto último se aplica a mi amiga Brittany». ?Daniel Ellsberg, escritor, activista e informante que desveló los papeles del Pentágono. BRITTANY KAISER le apasionó la política desde muy joven. Estudió Derechos Humanos y Relaciones Internacionales en la Universidad de Edimburgo en Escocia, la Universidad de Londres y la Universidad de Middlesex en Gran Bretaña. Trabajó para Cambridge Analytica en Gran Bretaña y en los Estados Unidos durante tres años antes de denunciar a su antigua empresa y a la industria del Big Data. Es la protagonista del documental de Netflix El gran hackeo y fundadora de la organización de protección de datos Digital Asset Trade Association (DATA).

Explore IoT, data analytics, and machine learning to solve cyber-physical problems using the latest capabilities of managed services such as AWS IoT Greengrass and Amazon SageMakerKey Features:Accelerate your next edge-focused product development with the power of AWS IoT GreengrassDevelop proficiency in architecting resilient solutions for the edge with proven best practicesHarness the power of analytics and machine learning for solving cyber-physical problemsBook Description:The Internet of Things (IoT) has transformed how people think about and interact with the world. The ubiquitous deployment of sensors around us makes it possible to study the world at any level of accuracy and enable data-driven decision-making anywhere. Data analytics and machine learning (ML) powered by elastic cloud computing have accelerated our ability to understand and analyze the huge amount of data generated by IoT. Now, edge computing has brought information technologies closer to the data source to lower latency and reduce costs.This book will teach you how to combine the technologies of edge computing, data analytics, and ML to deliver next-generation cyber-physical outcomes. You'll begin by discovering how to create software applications that run on edge devices with AWS IoT Greengrass. As you advance, you'll learn how to process and stream IoT data from the edge to the cloud and use it to train ML models using Amazon SageMaker. The book also shows you how to train these models and run them at the edge for optimized performance, cost savings, and data compliance.By the end of this IoT book, you'll be able to scope your own IoT workloads, bring the power of ML to the edge, and operate those workloads in a production setting.What You Will Learn:Build an end-to-end IoT solution from the edge to the cloudDesign and deploy multi-faceted intelligent solutions on the edgeProcess data at the edge through analytics and MLPackage and optimize models for the edge using Amazon SageMakerImplement MLOps and DevOps for operating an edge-based solutionOnboard and manage fleets of edge devices at scaleReview edge-based workloads against industry best practicesWho this book is for:This book is for IoT architects and software engineers responsible for delivering analytical and machine learning-backed software solutions to the edge. AWS customers who want to learn and build IoT solutions will find this book useful. Intermediate-level experience with running Python software on Linux is required to make the most of this book.

Künstliche Intelligenz (KI) ist in der Wirtschaft in aller Munde. Aber nur die wenigsten Firmen wissen, wie sie KI für den Aufbau neuer Geschäftsfelder nutzen können. Genau darum geht es in diesem Buch. Die Autoren kommen aus der Praxis und beschreiben unter anderem KI-Projekte, die sie gemeinsam mit ihren Kunden umgesetzt haben. Profitieren Sie von diesem Know-how , um mit KI erfolgreich zu sein:- grundlegendes Verständnis für die Möglichkeiten von Künstlicher Intelligenz- Vorgehensmodell für das Entwickeln von KI-Anwendungen- Folgenabschätzung für Prozesse und Geschäftsmodelle in verschiedenen Branchen durch KI- Einsatzszenarien für Prozesse von Kommunikation bis Verwaltung- Unterstützung mobiler Prozesse mithilfe von KI Das Buch enthält konkrete, realisierbare Anwendungsfälle für das verarbeitende Gewerbe und für Dienstleister. Lassen Sie sich davon inspirieren, um eigene Lösungen zu entwickeln und zu vermarkten.

Make any team or business data driven with this practical guide to overcoming common challenges and creating a data culture. Businesses are increasingly focusing on their data and analytics strategy, but a data-driven culture grounded in evidence-based decision making can be difficult to achieve. Be Data Driven outlines a step-by-step roadmap to building a data-driven organization or team, beginning with deciding on outcomes and a strategy before moving onto investing in technology and upskilling where necessary. This practical guide explains what it means to be a data-driven organization and explores which technologies are advancing data and analytics. Crucially, it also examines the most common challenges to becoming data driven, from a foundational skills gap to issues with leadership and strategy and the impact of organizational culture. With case studies of businesses who have successfully used data, Be Data Driven shows managers, leaders and data professionals how to address hurdles, encourage a data culture and become truly data driven.

Make informed decisions using the numbers around you to work smarter and live better.How often have you heard it said, or even said yourself, 'I'm not a numbers person?' Well, Dr Selena Fisk believes we no longer have a choice. Data iseverywhere. Smart watches track our steps and heart rate, social media platforms recommend people we might know and products we might like, and map applications suggest when we should leave home depending on the traffic.When you get off the phone to a customer service representative, you are asked to take a survey. Why? Because the data from the survey drives business decisions. Numbers are all around us and can help us make better decisions.The good news is that anyone can become a numbers person. I'm Not a Numbers Person shows you how to collect data in your working life, how to interpret it, present it visually and understand the story that it tells. These stories will be powerful for decision-making and for driving growth and productivity in your organisation.Whether you're a solopreneur, a small business owner, an emerging leader, or in an executive leadership role, this book is a must-have guide to understanding data and making better decisions in the 21st century.

Neural Networks is an integral part in machine learning and a known tool for controlling nonlinear processes. The area is under rapid development and provides a tool for modelling and controlling of advanced processes. This book provides a comprehensive overview for modelling, simulation, measurement and control strategies for reactive distillations using neural networks.

The New York Times best-selling Freakonomics was a worldwide sensation, selling over four million copies in thirty-five languages and changing the way we look at the world. Now, Steven D. Levitt and Stephen J. Dubner return with SuperFreakonomics, and fans and newcomers alike will find that the freakquel is even bolder, funnier, and more surprising than the first. Four years in the making, SuperFreakonomics asks not only the tough questions, but the unexpected ones: What's more dangerous, driving drunk or walking drunk? Why is chemotherapy prescribed so often if it's so ineffective? Can a sex change boost your salary? SuperFreakonomics challenges the way we think all over again, exploring the hidden side of everything with such questions as: How is a street prostitute like a department-store Santa? Why are doctors so bad at washing their hands? How much good do car seats do? What's the best way to catch a terrorist? Did TV cause a rise in crime? What do hurricanes, heart attacks, and highway deaths have in common? Are people hard-wired for altruism or selfishness? Can eating kangaroo save the planet? Which adds more value: a pimp or a Realtor? Levitt and Dubner mix smart thinking and great storytelling like no one else, whether investigating a solution to global warming or explaining why the price of oral sex has fallen so drastically. By examining how people respond to incentives, they show the world for what it really is ? good, bad, ugly, and, in the final analysis, super freaky. Freakonomics has been imitated many times over ? but only now, with SuperFreakonomics, has it met its match.

In diesem Buch werden alle Teilgebiete der KI kompakt, leicht verständlich und anwendungsbezogen vorgestellt. Der Autor kennt das Gebiet nicht nur bestens aus Forschung und praktischer Anwendung, sondern engagiert sich auch erfolgreich in der Lehre. Die Themen reichen von der klassischen Logik über das Schließen mit Unsicherheit und maschinelles Lernen bis hin zu Anwendungen wie Diagnosesysteme, lernfähige Roboter oder Kreativität in der KI.Sie profitieren von dem umfassenden Einblick in dieses faszinierende Teilgebiet der Informatik, wobei, abgesehen von grundlegenden Programmierkenntnissen sowie etwas Mathematik, alle Voraussetzungen für ein gutes Verständnis bereitgestellt werden. Sie gewinnen vertiefte Kenntnisse, z. B. hinsichtlich der wichtigsten Verfahren zur Repräsentation und Verarbeitung von Wissen und in dem immer wichtiger werdenden Gebiet des maschinellen Lernens. Vor allem der Anwendungsbezug steht im Fokus der Darstellung. Viele Übungsaufgaben mit Lösungen sowie eine strukturierte Liste mit Verweisen auf Literatur und Ressourcen im Web ermöglichen ein effektives und kurzweiliges Selbststudium.

In this groundbreaking work, Seth Stephens-Davidowitz, a Harvard-trained economist, former Google data scientist, and New York Times writer, argues that much of what we thought about people has been dead wrong. The reason? People lie, to friends, lovers, doctors, surveys?and themselves. However, we no longer need to rely on what people tell us. New data from the internet?the traces of information that billions of people leave on Google, social media, dating, and even pornography sites?finally reveals the truth.Everybody Lies combines the informed analysis of Nate Silver's The Signal and the Noise, the storytelling of Malcolm Gladwell's Outliers, and the wit and fun of Stephen Dubner and Steven Levitt's Freakonomics in a book that will change the way you view the world. There is almost no limit to what can be learned about human nature from Big Data?provided, that is, you ask the right questions.

The volume, complexity, and irregularity of computational data in modern algorithms and simulations necessitates an unorthodox approach to computing. Understanding the facets and possibilities of soft computing algorithms is necessary for the accurate and timely processing of complex data. Research Advances in the Integration of Big Data and Smart Computing builds on the available literature in the realm of Big Data while providing further research opportunities in this dynamic field. This publication provides the resources necessary for technology developers, scientists, and policymakers to adopt and implement new paradigms in computational methods across the globe. The chapters in this publication advance the body of knowledge on soft computing techniques through topics such as transmission control protocol for mobile ad hoc networks, feature extraction, comparative analysis of filtering techniques, big data in economic policy, and advanced dimensionality reduction methods.

This book constitutes the thoroughly refereed post-conference proceedings of the 18th International Workshop on Fast Software Encryption, held in Lyngby, Denmark, in February 2011. The 22 revised full papers presented together with 1 invited lecture were carefully reviewed and selected from 106 initial submissions. The papers are organized in topical sections on differential cryptanalysis, hash functions, security and models, stream ciphers, block ciphers and modes, as well as linear and differential cryptanalysis.

Exploratory data analysis (EDA) is about detecting and describing patterns, trends, and relations in data, motivated by certain purposes of investigation. As something relevant is detected in data, new questions arise, causing specific parts to be viewed in more detail. So EDA has a significant appeal: it involves hypothesis generation rather than mere hypothesis testing.The authors describe in detail and systemize approaches, techniques, and methods for exploring spatial and temporal data in particular. They start by developing a general view of data structures and characteristics and then build on top of this a general task typology, distinguishing between elementary and synoptic tasks. This typology is then applied to the description of existing approaches and technologies, resulting not just in recommendations for choosing methods but in a set of generic procedures for data exploration. Professionals practicing analysis will profit from tested solutions - illustrated in many examples - for reuse in the catalogue of techniques presented. Students and researchers will appreciate the detailed description and classification of exploration techniques, which are not limited to spatial data only. In addition, the general principles and approaches described will be useful for designers of new methods for EDA.