Dataanalyse: generelt

Text mining has emerged as one of the most important data processing activities over the last few decades. While it makes the life of millions of everyday users of digital plat- forms and applications much easier, it is a domain that also challenges researchers in numerous ways. The challenges are many fold - ranging from the volume of the data that needs to be processed, storage issues, language identi¿cation challenges and many more. The work in this thesis focuses on one particular aspect of text mining e.g. Key- word identi¿cation for a document. While this may seem to be quite a trivial activity for short passages, it is quite dif¿cult to successfully identify keywords for extremely long text documents. Doing so using an automated systems only adds to the challenge. Everyone in today's world understands the importance of data. In the context of business, data is used to analyze market trends or can be used to understand customer needs. It also helps to understand the user's perspectives and choices. There are var-ious ways that data plays a crucial role in our everyday lives. Most businesses would be bound to fail if they could not comprehend the data that was available. This data could vary from stock indices, to customer feedback, to worker sentiments and numerous other insights. Analyzing data also helps in advertisement noti¿cations or to suggest a piece of relevant information to the user. It also helps to understand the likes and dislikes of a user. It can make for a world with a better user experience in terms of an individuals needs, e.g., if a user is more interested in cricket, we can provide targeted insight to the user about cricket. A customized user experience for a user is more at- tractive than a bland user experience which is homogeneous for everyone. Everyone's needs are different from others as everyone has different perspectives and opinions. We offered examples of keyword extraction, the challenges involved and the major issues faced by designers of keyword extraction algorithms. Finally about some common application areas where keyword extraction is being used in real life scenarios. Attracting users and providing them with better services through relevant data also helps the system to understand the users' needs. A user consciously or unknowingly provides his information for use in business or expresses his views on various platforms. If a user expresses some political opinions, it helps us to tailor his experience better the next time he uses the system.

The general format of this book is I'll start with each concept, explaining it in a bunch of sections and graphical examples. I will introduce you to some of the notations and fancy terminologies that data scientists like to use so you can talk the same language, but the concepts themselves are generally pretty simple. After that, I'll throw you into some actual Python code that actually works that we can run and mess around with, and that will show you how to actually apply these ideas to actual data.

When you enter the world of time series analysis, you step into a labyrinth of numerical patterns, where each turn you take unveils another layer of complexity. Here, simple mathematical or statistical models struggle to keep pace.Reality is riddled with complex patterns in time series data, which, like cryptic pieces of a jigsaw puzzle, hold the key to unraveling insightful predictions. These complex patterns include non-linearity, non-stationarity, long memory or dependence, asymmetry, and stochasticity.But what creates these intricate patterns? Raghurami Reddy Etukuru, Ph.D., a distinguished and adaptable specialist in data science and artificial intelligence, delves into that question in this groundbreaking book, explaining that the factors are numerous and multifaceted, each adding their own measure of challenge. He doesn't just discuss problems but also addresses the forecasting of time series amidst intricate patterns.Take a deep dive deep into the world of numbers and patterns, so you can unravel complexities and leverage the power of artificial intelligence to enhance predictive capabilities. More than just a theoretical guide, this book is a practical companion in the often-turbulent journey of understanding and predicting complex time series data.

This report explores how machine learning can be leveraged to enable military decisionmaking at the operational level of competition and conflict as a collaboration between machine learning tools and human analysts.

Crime research has grown substantially over the past decade, with a rise in evidence-informed approaches to criminal justice, statistics-driven decision-making and predictive analytics. The fuel that has driven this growth is data - and one of its most pressing challenges - is the lack of research on the use and interpretation of data sources. This accessible, engaging book closes that gap for researchers, practitioners and students. International researchers and crime analysts discuss the strengths, perils and opportunities of the data sources and tools now available and their best use in informing sound public policy and criminal justice practice.

We live in a rapidly changing world, called VUCA, with its unique challenges and opportunities for growth and innovation. To be able to adjust changes and navigate complexities, leaders need to be prepared for the crises and opportunities that await them. That is why this book provides current information on the new leadership skills and the ways for enhancing VUCA readiness of educational leaders. This book is thought to be a comprehensive guide to help today¿s educational leaders thrive in VUCA by addressing the topics such as managing change and complexity, authentic leadership, agility, resilience and digital transformation. Thus, the book addresses all practitioners, leaders and teachers who need a practical guide on how to expand their vision, enhance their skills to lead in constantly changing educational environments.

Identify data quality issues, leverage real-world examples and templates to drive change, and unlock the benefits of improved data in processes and decision-makingKey Features:Get a practical explanation of data quality concepts and the imperative for change when data is poorGain insights into linking business objectives and data to drive the right data quality prioritiesExplore the data quality lifecycle and accelerate improvement with the help of real-world examplesPurchase of the print or Kindle book includes a free PDF eBookBook Description:Poor data quality can lead to increased costs, hinder revenue growth, compromise decision-making, and introduce risk into organizations. This leads to employees, customers, and suppliers finding every interaction with the organization frustrating.Practical Data Quality provides a comprehensive view of managing data quality within your organization, covering everything from business cases through to embedding improvements that you make to the organization permanently. Each chapter explains a key element of data quality management, from linking strategy and data together to profiling and designing business rules which reveal bad data. The book outlines a suite of tried-and-tested reports that highlight bad data and allow you to develop a plan to make corrections. Throughout the book, you'll work with real-world examples and utilize re-usable templates to accelerate your initiatives.By the end of this book, you'll have gained a clear understanding of every stage of a data quality initiative and be able to drive tangible results for your organization at pace.What You Will Learn:Explore data quality and see how it fits within a data management programmeDifferentiate your organization from its peers through data quality improvementCreate a business case and get support for your data quality initiativeFind out how business strategy can be linked to processes, analytics, and data to derive only the most important data quality rulesMonitor data through engaging, business-friendly data quality dashboardsIntegrate data quality into everyday business activities to help achieve goalsAvoid common mistakes when implementing data quality practicesWho this book is for:This book is for data analysts, data engineers, and chief data officers looking to understand data quality practices and their implementation in their organization. This book will also be helpful for business leaders who see data adversely affecting their success and data teams that want to optimize their data quality approach. No prior knowledge of data quality basics is required.

"This concise book for scientists and students interested in bioinformatics and data science covers all aspects of predictive modeling for biomarker discovery based on high-dimensional data, as well as modern data science methods for identification of parsimonious and robust multivariate biomarkers for medical diagnosis and personalized medicine"--

Prior to the events of February 2022, political interference was one of the most significant challenges in Russia-West relations. These proceedings reflect a series of discussions among U.S., Russian, and European Union nongovernmental experts who were convened in 2020-2021 to discuss mutual concerns regarding political interference and to find common ground on measures to address them. Even before February 2022, the European Union, the United States, and Russia had divergent interests, values, and worldviews, as well as significant mutual grievances. Despite these divergences and grievances, the assembled experts came to the view that all parties would have benefited from the establishment of mutually agreed-upon measures to mitigate the destabilizing impacts of political interference. In a text agreed on in January 2022, the expert group proposed the following measures: (1) increase transparency regarding interpretations of prohibited interference, (2) enhance dialogue on interference, (3) establish self-restraint commitments (regarding election-related infrastructure and hack-and-leak operations), (4) develop technical measures to demonstrate compliance with self-restraint commitments, (5) create guidelines to limit cross-border manipulation of social media, (6) relax restrictions on foreign broadcasters, and (7) formulate declarations of intent not to interfere.

Find all the information, exercises, and tools to ace the Splunk Enterprise Certified Admin exam in one place Key Features:Explore various administration topics including installation, configuration, and user managementGain a deep understanding of data inputs, parsing, and field extractionExcel in the Splunk Enterprise Admin exam with the help of self-assessment questions and mock examsPurchase of the print or Kindle book includes a free PDF eBookBook Description:The IT sector's appetite for Splunk and skilled Splunk developers continues to surge, offering more opportunities for developers with each passing decade. If you want to enhance your career as a Splunk Enterprise administrator, then Splunk 9.x Enterprise Certified Admin Guide will not only aid you in excelling on your exam but also pave the way for a successful career.You'll begin with an overview of Splunk Enterprise, including installation, license management, user management, and forwarder management. Additionally, you'll delve into indexes management, including the creation and management of indexes used to store data in Splunk. You'll also uncover config files, which are used to configure various settings and components in Splunk.As you advance, you'll explore data administration, including data inputs, which are used to collect data from various sources, such as log files, network protocols (TCP/UDP), APIs, and agentless inputs (HEC).You'll also discover search-time and index-time field extraction, used to create reports and visualizations, and help make the data in Splunk more searchable and accessible. The self-assessment questions and answers at the end of each chapter will help you gauge your understanding.By the end of this book, you'll be well versed in all the topics required to pass the Splunk Enterprise Admin exam and use Splunk features effectively.What You Will Learn:Explore Splunk Enterprise 9.x features and usageInstall, configure, and manage licenses and users for SplunkCreate and manage indexes for data storageExplore Splunk configuration files, their precedence, and troubleshootingManage forwarders and source data into Splunk from various resourcesParse and transform data to make it easy to useExtract fields from data at search and index time for data analysisEngage with mock exam questions to simulate the Splunk admin examWho this book is for:This book is for data professionals looking to gain certified Splunk administrator credentials. It will also help data analysts, Splunk users, IT experts, security analysts, and system administrators seeking to explore the Splunk admin realm, understand its functionalities, and become proficient in effectively administering Splunk Enterprise. This guide serves as both a valuable resource for learning and a practical manual for administering Splunk Enterprise, encompassing features beyond the scope of certification preparation.

"This book explores the work of activists in the Americas who are documenting feminicide, arguing that feminist activists at the margins have much to teach mainstream data scientists about data ethics: how to work with data ethically amidst extreme and durable structural inequalities"--

The authors of this report describe and analyze online information and potential misinformation about the security clearance process. Reviewing the content on internet forums reveals potential misperceptions about the process.

Learn the key skills and capabilities that empower Citizens of Data Science to not only survive but thrive in an AI-dominated world.Purchase of the print or Kindle book includes a free PDF eBookKey Features:Prepare for a future dominated by AI and big dataEnhance your AI and data literacy with real-world examplesLearn how to leverage AI and data to address current and future challengesBook Description:AI is undoubtedly a game-changing tool with immense potential to improve human life.This book aims to empower you as a Citizen of Data Science, covering the privacy, ethics, and theoretical concepts you'll need to exploit to thrive amid the current and future developments in the AI landscape.We'll explore AI's inner workings, user intent, and the critical role of the AI utility function while also briefly touching on statistics and prediction to build decision models that leverage AI and data for highly informed, more accurate, and less risky decisions.Additionally, we'll discuss how organizations of all sizes can leverage AI and data to engineer or create value. We'll establish why the economies of learning are more powerful than the economies of scale in a digital-centric world. Ethics and personal/organizational empowerment in the context of AI will also be addressed.Lastly, we'll delve into ChatGPT and the role of Large Language Models (LLMs), preparing you for the growing importance of Generative AI. By the end of the book, you'll have a deeper understanding of AI and the knowledge of how best to leverage it and thrive alongside it.What You Will Learn:Get to know the fundamentals of data literacy, privacy, and analyticsFind out what makes AI tick and the role of the AI utility functionMake informed decisions using prominent decision-making frameworksUnderstand relevant statistics and probability conceptsCreate new sources of value by leveraging and applying AI and dataApply ethical parameters to AI development with real-world examplesFind out how to get the most out of ChatGPT and its peersWho this book is for:This book is for anyone looking to navigate an AI-driven future and make the most of the emerging technologies that have begun changing every industry without exception. Whether you're looking to expand your career prospects, find opportunities in your industry, future-proof your organization, or simply need guidance to keep up with the rapid progress, this book will help you gain a better understanding of AI and data for years to come.

The purpose of training and education in the United States Department of the Air Force (DAF) is to develop and sustain mission-critical knowledge, skills, and abilities (KSAs) among airmen, guardians, and civilians. The DAF must deliver effective training and education to fully use its human capital, provide warfighting assets to combatant commanders, and maintain asymmetric advantage over competitors. Yet training and education is costly. A recent budget request included more than $2 billion for training and education, and recent guidance has highlighted that the U.S. Air Force must transform all facets of training and education to field a highly capable force in an affordable manner. This report focuses on computational cognitive models, a class of training technologies with transformative potential. Computational cognitive models emulate psychological processes like knowledge acquisition and retention. These models have been used to develop empirically grounded training curricula and deliver personalized training in diverse domains. The primary benefits of using these models to deliver personalized training are enhanced learning gains and reduced training time. This report explores the feasibility of applying computational cognitive models to the acquisition and sustainment of mission-critical KSAs, with emphasis on second-language learning. The authors affirm that cognitive models can be integrated with training curricula in a variety of ways, and each of these potential courses of action (COAs) presents different levels of benefits along with different technical and logistical challenges.

Creative research methods for data generation have expanded over recent decades and researchers are eager to take a creative approach to data analysis. It is challenging to bring creativity into data analysis while retaining a systematic, rigorous, and ethical approach. Written by experts in the field, this handbook addresses these challenges. The chapters adapt analytical techniques in creative ways for novice and expert researchers. Existing and novel methods from analysis of quantitative data to embodied, performative, visual, written, arts-based, and collaborative analysis are featured with case examples that are transferable across disciplines. This collection offers a definitive practical guide to creative data analysis.

Medical Imaging Informatics is an edited book that discusses how medical images can be processed using machine learning techniques and big data analysis methods. These tools help physicians to gain a full overview of a patient's data, which in turn assists with diagnosis, prognosis or intervention.

Everything you need to know to apply data contracts and build a truly data-driven organization that harnesses quality data to deliver tangible business valuePurchase of the print or Kindle book includes a free PDF eBook.Key Features:Understand data contracts and their power to resolving the problems in contemporary data platformsLearn how to design and implement a cutting-edge data platform powered by data contractsAccess practical guidance from the pioneer of data contracts to get expert insights on effective utilizationBook Description:Despite the passage of time and the evolution of technology and architecture, the challenges we face in building data platforms persist. Our data often remains unreliable, lacks trust, and fails to deliver the promised value.With Driving Data Quality with Data Contracts, you'll discover the potential of data contracts to transform how you build your data platforms, finally overcoming these enduring problems. You'll learn how establishing contracts as the interface allows you to explicitly assign responsibility and accountability of the data to those who know it best-the data generators-and give them the autonomy to generate and manage data as required. The book will show you how data contracts ensure that consumers get quality data with clearly defined expectations, enabling them to build on that data with confidence to deliver valuable analytics, performant ML models, and trusted data-driven products.By the end of this book, you'll have gained a comprehensive understanding of how data contracts can revolutionize your organization's data culture and provide a competitive advantage by unlocking the real value within your data.What You Will Learn:Gain insights into the intricacies and shortcomings of today's data architecturesUnderstand exactly how data contracts can solve prevalent data challengesDrive a fundamental transformation of your data culture by implementing data contractsDiscover what goes into a data contract and why it's importantDesign a modern data architecture that leverages the power of data contractsExplore sample implementations to get practical knowledge of using data contractsEmbrace best practices for the successful deployment of data contractsWho this book is for:If you're a data engineer, data leader, architect, or practitioner thinking about your data architecture and looking to design one that enables your organization to get the most value from your data, this book is for you. Additionally, staff engineers, product managers, and software engineering leaders and executives will also find valuable insights.

Build an end-to-end geospatial data lake in AWS using popular AWS services such as RDS, Redshift, DynamoDB, and Athena to manage geodata Purchase of the print or Kindle book includes a free PDF eBook.Key Features:Explore the architecture and different use cases to build and manage geospatial data lakes in AWSDiscover how to leverage AWS purpose-built databases to store and analyze geospatial dataLearn how to recognize which anti-patterns to avoid when managing geospatial data in the cloudBook Description:Managing geospatial data and building location-based applications in the cloud can be a daunting task. This comprehensive guide helps you overcome this challenge by presenting the concept of working with geospatial data in the cloud in an easy-to-understand way, along with teaching you how to design and build data lake architecture in AWS for geospatial data.You'll begin by exploring the use of AWS databases like Redshift and Aurora PostgreSQL for storing and analyzing geospatial data. Next, you'll leverage services such as DynamoDB and Athena, which offer powerful built-in geospatial functions for indexing and querying geospatial data. The book is filled with practical examples to illustrate the benefits of managing geospatial data in the cloud. As you advance, you'll discover how to analyze and visualize data using Python and R, and utilize QuickSight to share derived insights. The concluding chapters explore the integration of commonly used platforms like Open Data on AWS, OpenStreetMap, and ArcGIS with AWS to enable you to optimize efficiency and provide a supportive community for continuous learning.By the end of this book, you'll have the necessary tools and expertise to build and manage your own geospatial data lake on AWS, along with the knowledge needed to tackle geospatial data management challenges and make the most of AWS services.What You Will Learn:Discover how to optimize the cloud to store your geospatial dataExplore management strategies for your data repository using AWS Single Sign-On and IAMCreate effective SQL queries against your geospatial data using AthenaValidate postal addresses using Amazon Location servicesProcess structured and unstructured geospatial data efficiently using RUse Amazon SageMaker to enable machine learning features in your applicationExplore the free and subscription satellite imagery data available for use in your GISWho this book is for:If you understand the importance of accurate coordinates, but not necessarily the cloud, then this book is for you. This book is best suited for GIS developers, GIS analysts, data analysts, and data scientists looking to enhance their solutions with geospatial data for cloud-centric applications. A basic understanding of geographic concepts is suggested, but no experience with the cloud is necessary for understanding the concepts in this book.

Build advanced NLU systems by utilizing NLP libraries such as NLTK, SpaCy, BERT, and OpenAI; ML libraries like Keras, scikit-learn, pandas, TensorFlow, and NumPy, along with visualization libraries such as Matplotlib and Seaborn. ¿Purchase of the print Kindle book includes a free PDF eBookKey Features:Master NLU concepts from basic text processing to advanced deep learning techniquesExplore practical NLU applications like chatbots, sentiment analysis, and language translationGain a deeper understanding of large language models like ChatGPTBook Description:Natural language understanding (NLU) organizes and structures, language allowing computer systems to effectively process textual information for many different practical applications. Natural Language Understanding with Python will help you explore practical techniques that make use of NLU to build a wide variety of creative and useful applications.Complete with step-by-step explanations of essential concepts and practical examples, this book begins by teaching you about NLU and its applications. You'll then explore a wide range of current NLU techniques and their most appropriate use-case. In the process, you'll be introduced to the most useful Python NLU libraries. Not only will you learn the basics of NLU, you'll also be introduced to practical issues such as acquiring data, evaluating systems, and deploying NLU applications, along with their solutions. This book is a comprehensive guide that will help you explore the full spectrum of essential NLU techniques and resources.By the end of this book, you will be familiar with the foundational concepts of NLU, deep learning, and large language models (LLMs). You will be well on your way to having the skills to independently apply NLU technology in your own academic and practical applications.What You Will Learn:Explore the uses and applications of different NLP techniquesUnderstand practical data acquisition and system evaluation workflowsBuild cutting-edge and practical NLP applications to solve problemsMaster NLP development from selecting an application to deploymentOptimize NLP application maintenance after deploymentBuild a strong foundation in neural networks and deep learning for NLUWho this book is for:This book is for python developers, computational linguists, linguists, data scientists, NLP developers, conversational AI developers, and students looking to learn about natural language understanding (NLU) and applying natural language processing (NLP) technology to real problems. Anyone interested in addressing natural language problems will find this book useful. Working knowledge in Python is a must.

"This volume introduces researchers to the idea of no-boundary thinking (NBT) in biological and biomedical research. Written by a team of specialists, drawing on their own experience, it provides a guide to integrating and synthesizing data and knowledge from bioinformatics to define important problems and articulate impactful research questions"--

A growing concern exists that the expected annual growth in the number of eligible employees will be outpaced by economic growth predictions. While employee retention and employee voluntary turnover have received considerable scholarly attention, few research studieshave examined the phenomenon in a professional sales arena. No investigation to date has tracked employee voluntary turnover and retention over a 14-year longitudinal wave as was the focus of this study.With the supply of talented employees for the predicted available jobs around the world declining, employee retention and voluntary turnover have jumped to the forefront of HRD practitioners', as well as senior managers', strategic initiative

Deploy your data ingestion pipeline, orchestrate, and monitor efficiently to prevent loss of data and qualityPurchase of the print or Kindle book includes a free PDF eBookKey Features:Harness best practices to create a Python and PySpark data ingestion pipelineSeamlessly automate and orchestrate your data pipelines using Apache AirflowBuild a monitoring framework by integrating the concept of data observability into your pipelinesBook Description:Data Ingestion with Python Cookbook offers a practical approach to designing and implementing data ingestion pipelines. It presents real-world examples with the most widely recognized open source tools on the market to answer commonly asked questions and overcome challenges.You'll be introduced to designing and working with or without data schemas, as well as creating monitored pipelines with Airflow and data observability principles, all while following industry best practices. The book also addresses challenges associated with reading different data sources and data formats. As you progress through the book, you'll gain a broader understanding of error logging best practices, troubleshooting techniques, data orchestration, monitoring, and storing logs for further consultation.By the end of the book, you'll have a fully automated set that enables you to start ingesting and monitoring your data pipeline effortlessly, facilitating seamless integration with subsequent stages of the ETL process.What You Will Learn:Implement data observability using monitoring toolsAutomate your data ingestion pipelineRead analytical and partitioned data, whether schema or non-schema basedDebug and prevent data loss through efficient data monitoring and loggingEstablish data access policies using a data governance frameworkConstruct a data orchestration framework to improve data qualityWho this book is for:This book is for data engineers and data enthusiasts seeking a comprehensive understanding of the data ingestion process using popular tools in the open source community. For more advanced learners, this book takes on the theoretical pillars of data governance while providing practical examples of real-world scenarios commonly encountered by data engineers.

Análisis de política pública, el libro más ampliamente citado sobre la materia, proporciona a los estudiantes una comprehensiva metodología para el análisis de política pública. Parte de la premisa de que el análisis de política pública es una disciplina científico-social aplicada diseñada para solucionar los problemas prácticos que confrontan las organizaciones públicas y las organizaciones sin ánimo de lucro. Esta sexta edición, enteramente revisada, contiene varias actualizaciones importantes:¿ Cada capítulo incluye un estudio de caso de "grandes ideas" completamente nuevo en análisis de política pública para estimular el interés del estudiante en problemas apropiados e importantes.¿ El capítulo dedicado a la política pública basada en la evidencia y al papel de los experimentos de campo ha sido reescrito y ampliado en su integridad.¿ Se han agregado nuevas secciones sobre desarrollos importantes en el campo, incluidos el uso de evidencia científica en la factura de la política pública, revisiones sistemáticas, metaanálisis y "big data".¿ Se incluyen conjuntos de datos en línea para aplicar técnicas analíticas como archivos IBM SPSS 23.0, que son convertibles a los programas estadísticos Excel, Stata y R con el fin de que sirvan a una variedad de necesidades de cursos y estilos de enseñanza.¿ Se incluyen filminas enteramente nuevas en PowerPoint para facilitar el trabajo del instructor mejor que antes.Diseñado para que estudiantes con distintos antecedentes académicos realicen análisis por cuenta propia, sin el requerimiento de conocimientos en microeconomía, Análisis de política pública, sexta edición, ayuda a los estudiantes a desarrollar las habilidades prácticas necesarias para comunicar hallazgos a través de memos, documentos de posición y otras formas de escritura analítica estructurada. El texto involucra a los estudiantes desafiándolos a analizar críticamente los argumentos de profesionales de la política pública así como los de politólogos, economistas y filósofos de la política.

This edited book reviews the intertwining disciplines of nature-inspired optimization algorithms and bio-inspired soft-computing for real world applications, with the interaction between metaheuristics with complex systems. The authors present methods and techniques in IoT, image processing, smart manufacturing and healthcare.

The identification and interpretation of adverse event root cause is a critical function supporting the development of appropriate corrective actions in high-risk industries. Aviation Human Factors (HF) professionals are interested in identifying events caused by human error in aircraft and engine assembly and maintenance to develop solutions to systemic issues. Current event classification methods are heavily dependent on manual review of report narratives, which presents an opportunity to explore automated techniques using data science (DS), rule-based classification, and machine learning (ML). In this study, automated classification models were developed, combined, and compared, using multiple event report fields as model inputs. Based on the determination that event narratives are the most valuable source of root cause information, natural language processing (NLP) and feature engineering methods were explored to identify patterns in human language associated with error causal factors.