Dataanalyse: generelt

Be part of the future of Web3, decoding blockchain data to build trust in the next-generation internetKey Features:Build a deep understanding of the fundamentals of blockchain analyticsExtract actionable business insights by modeling blockchain dataShowcase your work and gain valuable experience to seize opportunities in the Web3 ecosystemPurchase of the print or Kindle book includes a free PDF eBookBook Description:Data is the new oil and Web3 is generating it at an unprecedented rate. Complete with practical examples, detailed explanations, and ideas for portfolio development, this comprehensive book serves as a step-by-step guide covering the industry best practices, tools, and resources needed to easily navigate the world of data in Web3.You'll begin by acquiring a solid understanding of key blockchain concepts and the fundamental data science tools essential for Web3 projects. The subsequent chapters will help you explore the main data sources that can help address industry challenges, decode smart contracts, and build DeFi- and NFT-specific datasets. You'll then tackle the complexities of feature engineering specific to blockchain data and familiarize yourself with diverse machine learning use cases that leverage Web3 data.The book includes interviews with industry leaders providing insights into their professional journeys to drive innovation in the Web 3 environment. Equipped with experience in handling crypto data, you'll be able to demonstrate your skills in job interviews, academic pursuits, or when engaging potential clients.By the end of this book, you'll have the essential tools to undertake end-to-end data science projects utilizing blockchain data, empowering you to help shape the next-generation internet.What You Will Learn:Understand the core components of blockchain transactions and blocksIdentify reliable sources of on-chain and off-chain data to build robust datasetsUnderstand key Web3 business questions and how data science can offer solutionsBuild your skills to create and query NFT- and DeFi-specific datasetsImplement a machine learning toolbox with real-world use cases in the Web3 spaceWho this book is for:This book is designed for data professionals-data analysts, data scientists, or data engineers- and business professionals, aiming to acquire the skills for extracting data from the Web3 ecosystem, as it demonstrates how to effectively leverage data tools for in-depth analysis of blockchain transactional data. If you seek hands-on experience, you'll find value in the shared repository, enabling you to experiment with the provided solutions. While not mandatory, a basic understanding of statistics, machine learning, and Python will enhance your learning experience.

With the evolution of human computing, a lot of data gets generated every day and due to the humongous variety of the data, it becomes difficult to organize them in such a manner that it provides significant information regarding a specific context. To retrieve information from a collected set of data, an automated system model is required that holds the information regarding the contexts that are evaluated from the dataset. The automated system can be termed as data processing architecture. Contextual sense provides significance to the seeker from any data processing architecture. A data processing architecture is made up of two elements namely the dataset itself and the processing rules. For example, consider a sense of "hungry" and is defined by the total number of chapattis a human eats in a meal. So the rule set could be, "If the object consumes less than or equal to 5 chapattis Outcome is "hungry" else "not hungry". The rules are dependent upon the type of membership function values that are provided to the object. As briefed earlier, Context is the relevant information specific for a particular user. Context Mining is the process to extract the information with respect to a particular context. context is the origin of the demanded information and due to increasing data complexity in terms of volume, variety, and modularity, it becomes almost impossible to perform the contextual analysis manually. Hence, an automated system is required that analyses the asked content based on the stored content via a rule base architecture. Due to the high computation time of rule base architecture, propagation-based rule mining is now used in modern-day computation. The users generate their requests through an application layer. The application layer passes the request to its concerned forums via the internet. The forums are connecting to data processing centers that could be cloud data centers in this modern time frame. The processing center or the data center has a service manager that analyses the request from the user based on the context that is demanded by the user query. The context evaluation is done with the help of a trained repository and a file log is maintained to write new entry values in the system . The entire process can be termed as context mining.

Understanding the Importance of Clustering and Segmentation in Data ScienceIn the ever-evolving field of data science, engineers are constantly seeking innovative techniques to extract meaningful insights from vast amounts of data. One such technique that has gained significant popularity is clustering and segmentation. In this subchapter, we will dive deep into the importance of clustering and segmentation in data science and its applications within the niche of machine learning for data science.Clustering, in its essence, involves grouping similar data points together based on their inherent characteristics. By identifying patterns and relationships among data points, clustering enables engineers to uncover hidden structures within the data, leading to valuable insights. With the ability to handle large datasets efficiently, clustering has become an indispensable tool for engineers working on data science projects.Segmentation, on the other hand, takes clustering a step further by partitioning data into distinct subsets or segments. This process allows engineers to understand the heterogeneous nature of the data and tailor their analysis and decision-making accordingly. By segmenting data, engineers are able to identify specific target groups, understand their unique preferences, and devise personalized strategies that drive business growth and enhance customer satisfaction.The importance of clustering and segmentation in data science cannot be overstated. These techniques enable engineers to gain a comprehensive understanding of complex datasets, identify trends, and make data-driven decisions with confidence. Whether it is customer segmentation for targeted marketing campaigns, anomaly detection in cybersecurity, or analyzing patterns in healthcare data, clustering and segmentation play a vital role in extracting valuable insights that drive business success.Furthermore, with the advent of machine learning algorithms and techniques, clustering and segmentation have witnessed significant advancements. Engineers can now employ sophisticated algorithms such as k-means, hierarchical clustering, and density-based clustering to achieve more accurate results and handle complex datasets effectively. These advancements have opened up new avenues for innovation and application of clustering and segmentation techniques in various industries, including finance, healthcare, retail, and more.

Understanding Big Data and Real-Time AnalyticsIn today's digital age, data is being generated at an unprecedented rate. Every day, we create 2.5 quintillion bytes of data, and this number is expected to increase exponentially in the coming years. This explosion of data has given rise to the concept of Big Data Analytics, which involves extracting useful insights from massive datasets. In this subchapter, we will delve into the world of Big Data and explore the fascinating field of real-time analytics.Big Data refers to extremely large and complex datasets that cannot be effectively processed using traditional data processing techniques. It encompasses structured, semi-structured, and unstructured data from various sources, including social media, sensors, and transactional systems. The immense volume, velocity, and variety of Big Data pose significant challenges in terms of storage, processing, and analysis.Real-time analytics, on the other hand, is the practice of analyzing data as it is generated, enabling organizations to make immediate, data-driven decisions. Real-time analytics leverages advanced technologies such as machine learning, artificial intelligence, and predictive modeling to extract valuable insights from streaming data. By analyzing data in real-time, organizations can detect anomalies, identify trends, and respond quickly to changing market conditions.The combination of Big Data and real-time analytics offers numerous benefits across industries. For instance, in healthcare, real-time analytics can help monitor patient vitals in real-time, enabling doctors to detect any abnormalities and provide timely interventions. In finance, real-time analytics can detect fraudulent transactions and trigger immediate alerts to prevent financial losses. In retail, real-time analytics can provide personalized recommendations to customers based on their browsing and purchase history.To effectively harness the power of Big Data and real-time analytics, organizations need robust infrastructure and advanced analytics tools. This includes distributed storage systems like Hadoop, which can handle large volumes of data, and stream processing frameworks like Apache Kafka, which can ingest and process real-time data streams. Additionally, organizations need skilled data scientists and analysts who can interpret the results and translate them into actionable insights.

The present era, oftentimes referred to as the data age, is characterized by an enormous volume of data across various sectors. Similar to how oil has shaped the industrial age in the 19th century, data are now the crucial resource for gaining competitive advantages. However, harnessing this potential requires thorough analysis and domain knowledge to extract valuable information from these data. To optimally leverage this knowledge, domain experts have to be involved in the entire analysis process.This doctoral thesis introduces the user-centric data analysis approach, empowering domain experts to navigate the full-featured analytical journey, from selecting data sources to data preprocessing, data mining, and reporting - without the need for extensive technical knowledge. This holistic approach encompasses not only a reference model for user-centric data analysis but furthermore includes concepts, prototypical implementations as well as comprehensive evaluations for several phases of the analysis.The user-centric data analysis approach is systematically compared to various state-of-the-art approaches, such as process models or visual analytics, based on six different dimensions. This comparison reveals that, through the introduced approach, domain experts are significantly better integrated into the analysis process, resulting in faster insights and competitive advantages.

Analytics made easy with Base and Advance SASData Analytics with SAS is an attempt to learn concepts of Data Analytics with SAS tool. Starting with the fundamentals, the book introduces you to SAS by explaining its architecture, components, libraries and graphical user interface. It then delves into abilities like manipulating and exploring data, where both basic and advanced techniques are covered.The book outlines concepts and functions for data manipulation. Data manipulation is important as without it, we cannot define data in a proper format. Moreover, data without a proper format and features cannot be used for further analysis.The book outlines concepts and functions of data exploration. Data exploration or Exploratory Data Analysis (EDA) is the first step in data analysis. It is a very critical step as it helps us get insights from data to understand past behaviors. To facilitate a practical learning experience with SAS, the book offers examples and code snippets.In conclusion, this comprehensive guidebook serves as a valuable resource for individuals interested in data analytics using SAS. It caters to both novices and seasoned users alike while preparing them for roles, within the field of Data Analytics.WHAT YOU WILL LEARN¿ Get familiar with the functions for insightful data exploration.¿ Shape and transform data using data manipulation functions.¿ Improve efficiency of SAS Operations by combining power of SQL with SAS.¿ Learn how to automate data analysis tasks and share insights across your team with SAS macros.¿ Learn how to visualize your data with impact using a variety of data visualization functions.WHO THIS BOOK IS FORThis book is meant for Data Analysts, Data Engineers, Business Analysts, Data Scientists, Business Intelligence Experts, Data journalists, Market researchers, Financial analysts, Risk analysts and anyone who wants to pursue a career in Analytics.

This volume is a collection of empirical and theoretical research papers regarding Economics, Public Finance and Business written by researchers from several different universities. The studies include a wide range of topics from issues in Economics, Public Finance and Business. The book is aimed at educators, researchers, and students interested in Public Finance, Business and Economics.

Explore the Core of Computing with "30 Essential Algorithms in Python". Dive into the world of algorithms with this insightful guide, perfect for anyone passionate about computer science. Whether you're a student starting your journey, a professional seeking to deepen your understanding, or a coding enthusiast eager to explore, this book is your gateway to mastering some of the most fundamental and powerful algorithms in computing. Unlock the Secrets of Algorithms:From sorting and searching to graph theory and beyond, discover the mechanisms behind 30 essential algorithms.Programmed Solutions in Python. Each algorithm is accompanied by its implementation in Python, providing practical, ready-to-use solutions.Real-World Applications: Understand not just the "how" but also the "why" as each algorithm is tied to its real-world applications. "Essential Algorithms: A Practical Approach" is more than a book; it's a journey into the heart of algorithmic thinking. Equip yourself with the knowledge and skills to tackle real-world problems with confidence and creativity. Embrace the world of algorithms today!

The Importance of Data in Health CareIn the world of health care, data plays a crucial role in ensuring the delivery of efficient and effective services. From patient records to clinical trials, numerical data is invaluable in shaping policies, making informed decisions, and improving health outcomes. As students embarking on a journey in data analysis within the health care field, understanding the importance of data is essential.One of the primary uses of numerical data in health care is in patient care. By collecting and analyzing data on patients' medical history, symptoms, and treatment plans, healthcare professionals can gain insights into patterns and trends. This information enables them to make accurate diagnoses, develop personalized treatment plans, and monitor the progress of patients over time. Moreover, data analysis helps identify risk factors and predict potential health issues, allowing for proactive interventions and preventive measures.Another critical aspect of data in health care is research and development. Clinical trials and studies collect vast amounts of numerical data to test the efficacy and safety of new treatments, medications, and medical devices. This data is analyzed to determine the effectiveness of interventions, identify side effects, and provide evidence for regulatory approvals. Through rigorous data analysis, researchers can contribute to medical advancements, shaping the future of health care.Furthermore, data plays a crucial role in health care management. Administrators and policymakers rely on data to evaluate the performance of healthcare facilities, identify areas for improvement, and allocate resources effectively. By analyzing data on patient satisfaction, wait times, and cost-effectiveness, administrators can enhance the quality of care, streamline operations, and optimize resource allocation. This not only ensures better patient outcomes but also contributes to the overall sustainability of the health care system.

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.

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.

In diesem Buch werden wir uns ausführlich mit der Verbindung von Cybersicherheit und Künstlicher Intelligenz auseinandersetzen. Wir werden die jüngsten Entwicklungen in der Bedrohung durch KI-basierte Tools untersuchen, proaktive Verteidigungsstrategien erkunden und ethische Sicherheitsvorkehrungen betonen. Wir werden Fallstudien analysieren und einen Blick in die Zukunft werfen, um zu verstehen, wie KI unsere digitale Welt sicherer machen kann.Willkommen in einer Welt, in der Technologie nicht nur eine Bedrohung darstellt, sondern auch eine mächtige Verteidigungslinie. Lassen Sie uns gemeinsam diese Reise antreten und die 4 proaktive Verteidigungsstrategien erforschen, die die Cybersicherheit durch und gegen KI stärken.

Explore the vast field of data science with "A Complete Guide to Data Science Essentials," an easy-to-use guide that breaks down the complicated parts of this ever-changing field. This book, written by Miguel, a seasoned data science expert, takes you step-by-step through the most important ideas, methods, and tools that will shape the data-driven world in 2023.Learn about important things like machine learning, statistical analysis, and computer languages (like Python, R, etc.) to get the skills you need to work in the ever-changing field of data science. Learn the mathematical ideas that support data science to build a strong base for advanced analytics and fixing problems.Stay ahead of the curve by learning how to use popular computer languages to do cutting edge data manipulation, analysis, and visualization. The book puts a lot of stress on practical scripting and coding, making sure that readers learn how to turn raw data into insights that they can use.Master the art of cleaning and preparing data to make it more reliable and of higher quality. Learn the newest ways to collect and source data. With an emphasis on real-world uses, the guide talks about the problems that come up when you have to deal with missing values and outliers and gives you useful ways to make your data analysis stronger."A Complete Guide to Data Science Essentials" is the book you need whether you're an experienced data professional or a beginner who can't wait to start your data science path. This is an important resource for staying current in the data-driven era. It includes the most popular keywords and methods that define the field of data science right now.

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.

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"--

These proceedings reflect 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.

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.

Machine learning is an advanced field of data analytics that teaches computers to learn from their experiences similar to humans and animals. It utilizes two techniques, namely, unsupervised learning and supervised learning. The former makes use of the internal structures or hidden patterns in the input data whereas the latter involves training a model using known input and output data for predicting the future outcomes. Geoscience refers to the study of the Earth and all its natural structures and phenomena including oceans, atmosphere, rivers and lakes, ice sheets and glaciers, soils, complex surface, and rocky interior. Geographic information systems (GISs) are used extensively in studying the Earth. Machine learning is being used in GIS for segmentation, classification and prediction. Machine learning combined with remote sensing can enhance the automation of data analysis, uncover novel insights from large data sets, predict the behavior of environmental systems and lead to better management of resources. This book is a compilation of chapters that discuss the most vital concepts and emerging trends in the use of machine learning in geosciences. It will provide comprehensive knowledge to the readers.

Big data refers to a collection of data that is enormous in volume and is expanding at exponential rates with the passage of time. It is a dataset that is too large and complex for traditional data management tools to process or store it effectively. There are various types of big data including semi-structured, structured and unstructured. Big data technologies are used in a number of industries and fields including banking, communication, healthcare, geosciences, astrophysics, and trade. The geographic information systems (GISs) are an important application of big data in geosciences, which aid in creating, managing, analyzing and mapping all types of geospatial data. GISs generate huge amounts of data, which are analyzed using big data technologies. Big data computing for GIS data enables analysis and decision making from massive datasets by using spatiotemporal data mining, algorithms and query processing. This book elucidates the concepts and innovative models around prospective developments with respect to big data techniques and technologies used in geoscience. It will serve as a reference to a broad spectrum of readers.

"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"--