Datafangst og dataanalyse

The digital revolution in the past decade plus has made our life much easier. The computer screens have become sleeker and the machines smarter with every passing year. The high computing power coupled with artificial intelligence has made our devices much easy to use with practically zero learning curve. However, the enhanced sophistication brings with it a unique challenge for the civilised communityi.e. how to detect and prevent cybercrimes. Cyber is touted to be the 5th dimension of war after land, sea, air and space and will be the deciding factor in future conflicts. In order to build up a strong defence, it is very important that the security breach is detected at the earliest and analysed for weak links. Towards this, forensic analysis of data acquired needs to be done to identify the perpetuator and prevent future attacks.Forensics is a branch of science that deals with the evidences that can be presented in the Court of Law. Its sub-domain that deals with acquiring and analysing data from computers, smartphones and other digital devices is known as digital forensics. Digital Forensics is the art of acquiring and analysing information obtained from a computer/digital device. The word "Smartphone Forensics" synonymous with "Mobile Forensics" has a natural reference to an Android Smartphone, primarily because of its popularity. Analysis of basic phones, smart phones and feature phones is included in Mobile Forensics. Android, Windows and iOS OS are installed in 90-95% of the mobiles used across the world. Mobile Forensics is expanding its horizon with Personal Digital Assistants (PDAs) and Tablets already in its domain and sooner or later will include Internet of Things (IoT) devices and smart TV primarily because they contain a similar OS which can store lot of data. In the nascent mobile forensics days, prime focus was on getting information somehow from the phone. This data was related to call history, contacts stored in phone book, SMS, MMS in addition to data on SIM card. However with increased usage of mobile phones, the focus has shifted on extracting data related to social media posts like Facebook, WhatsApp, &nbs

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.

Empowering You to Master Business Intelligence and Solve Real-world Analytical Problems.DESCRIPTIONIn today's analytics landscape, proficiency in Excel and Power BI is practically a prerequisite for a successful career. This book provides a concise yet comprehensive exploration of these foundational elements of modern Business Intelligence (BI).Comprising ten chapters, this book covers the entire development journey of a Power BI analytical solution, spanning from data modeling and preparation to report creation, publication, and dashboard and app deployment. It offers insights into security measures and performance optimization, ensuring a well-rounded understanding of the BI ecosystem. Furthermore, it delves into advanced topics, such as leveraging data science algorithms within reports, offering readers an extensive learning experience. The book takes a holistic approach to these technologies, offering a contrast to the narrow perspectives often adopted by contemporary BI professionals who rely solely on a single tool or language. The book allows data enrichment through calculations that can be achieved using various languages, including SQL queries at the source, Power Query Formula Language, M, Python, R, and DAX. By the end of this book you will master these options but be able to also weigh their pros and cons to make informed decisions.TABLE OF CONTENTS 1. Introducing the Theoretical Background for Democratizing Analytics2. Acquiring and transforming data from multiple sources3. Power Query Transformations and Formula Language (M) Overview4. Building a simple analytical solution with Power Pivot in Excel5. Enhancing the model and business analysis with DAX6. Creating reports in Power BI Desktop7. Using the Power BI Service8. Data Science in Power BI 9. Optimizing Power BI models and reports10. Deploying, Maintaining and Securing Power BI Assets Index

Through the application of cutting-edge techniques like Big Data, Data Mining, and Data Science, it is possible to extract insights from massive datasets. These methodologies play a crucial role in enabling informed decision-making and driving transformative advancements across many fields, industries, and domains. This book offers a complete overview of the latest tools and approaches, shedding light on their practical applications and methods.

Une bible magistrale sur SASCe livre présente le socle de connaissances communes à tous les utilisateurs de SAS, le progiciel d'informatique décisionnelle le plus utilisé au monde. Il traite tout particulièrement des fonctionnalités de Base SAS, module au c1/2ur du système SAS. Pédagogique et complet, il peut servir aussi bien de guide d'initiation pour les utilisateurs débutants que d'ouvrage de référence pour les plus expérimentés, et concerne aussi bien les utilisateurs de SAS Foundation que ceux de SAS Enterprise Guide, SAS Studio et SAS University Edition.Cet ouvrage couvre les programmes des certifications SAS Certified Specialist: Base Programming Using SAS 9.4 et Advanced Programming for SAS 9.Parmi les sujets développés: la création, la manipulation et la gestion des tables de données;les procédures d'exploration des données: construction de tableaux, de rapports, de graphiques au moyen des procédures ODS Graphics;la production de documents au format HTML, PowerPoint, RTF, XLSX ou PDF avec ODS;la procédure PROC SQL et le langage SQL de SAS;le langage macro spécifique à SAS.Pour vous aider à bien assimiler tous les concepts, le livre comprend près de 500 programmes d'exemples, plus de 150 exercices et des liens vers une centaine d'articles en ligne.Une 4e édition mise à jour et augmentéeEnrichie de plus d'une centaine de pages, cette nouvelle édition propose des mises à jour importantes sur: les passerelles entre SAS et Excel;l'optimisation des ressources;la production de graphiques au moyen de PROC SGPLOT et PROC SGPANEL;la création et gestion de vos tables au moyen de PROC SQL.Le livre, qui porte essentiellement sur la version 9.4 de SAS, est également compatible avec les versions 9.2 et 9.3.Ce livre a le soutien de SAS France.À qui s'adresse cet ouvrage ?Aux professionnels souhaitant découvrir ou approfondir leurs connaissances de la programmation SASAux étudiants qui débutent avec SAS ou qui souhaitent préparer les examens de certification SAS Cert

���� Are you ready to embark on an exhilarating journey through the captivating realm of data science? Look no further! ����In today's data-driven world, mastering the art of data science is not just an advantage; it's essential. Whether you're an aspiring data wizard or a seasoned professional, this Practitioner's Guide to Data Science is your key to unlocking the limitless potential hidden within 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.

Smart Spaces covers the latest concepts and technologies surrounding smart spaces, providing technical personnel engaged in smart space related research and industries a more in-depth understanding of smart spaces. This book can be used as a reference for practicing this emerging discipline, but it will also be useful for researchers, scientists, developers, practitioners, and graduate students working in the fields of smart spaces and artificial intelligence. It combines the study of working or living spaces with computing, information equipment, and multimodal sensing devices, and with natural and convenient interactive interfaces to support how people can easily obtain services from computer systems. People's work and life in smart spaces use computer systems; it is a process of uninterrupted interaction between people and the computer system. In this process, the computer is no longer just an information processing tool that passively executes explicit human operation commands but a collaborator with people to complete tasks - a partner to human beings. International research on smart spaces is quite extensive, which shows the important role of smart spaces in ubiquitous computing research.

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.

Your life online is their product.In the past, colonialism was a landgrab of natural resources, exploitative labour and private property from countries around the world. It promised to modernise and civilise, but actually sought to control. It stole from native populations and made them sign contracts they didn't understand. It took resources just because they were there.Colonialism has not disappeared - it has taken on a new form.In the new world order, data is the new oil. Big Tech companies are grabbing our most basic natural resources - our data - exploiting our labour and connections, and repackaging our information to control our views, track our movements, record our conversations and discriminate against us. Every time we unthinkingly click 'Accept' on Terms and Conditions, we allow our most personal information to kept indefinitely, repackaged by big Tech companies to control and exploit us for their own profit.In this searing, cutting-edge guide, two leading global researchers - and founders of the concept of data colonialism - reveal how history can help us both to understand the emerging future and to fight back.

Are you ready to embark on a thrilling journey into the world of data science, where raw data is transformed into invaluable insights? Dive headfirst into "Data Science Concepts and Practice," a groundbreaking book that demystifies the complex world of data science and equips you with the skills you need to succeed in this data-driven era.¿¿¿¿ Discover the Secrets of Data Analysis: From data wrangling to visualization, you'll master the art of extracting actionable knowledge from vast datasets.¿¿¿¿ Embrace Machine Learning: Unleash the potential of artificial intelligence as you delve into algorithms, predictive modeling, and the endless possibilities of AI.¿¿¿¿ Harness Big Data: Learn to navigate the sea of information with ease, turning big data into a powerful asset for your business or projects.¿¿¿¿ Practical Applications: Real-world examples and hands-on exercises ensure you not only understand the concepts but can apply them effectively.¿¿¿¿ Uncover Insights: Dive deep into data mining techniques to unearth hidden patterns and trends that drive smart decision-making.¿¿¿¿ Join the Data Revolution: Position yourself as a sought-after data scientist with the knowledge and expertise needed in today's competitive job market.Whether you're a beginner looking to enter the world of data science or a seasoned professional seeking to expand your skillset, this comprehensive guide is your roadmap to success. Packed with easy-to-follow explanations, captivating examples, and expert tips, "Data Science Concepts and Practice" is your essential companion on the path to becoming a data science wizard.Don't miss out on this opportunity to transform your career and elevate your understanding of data science. Join the ranks of data pioneers who are shaping the future today. Your journey to data enlightenment starts here!

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.

Work through practical examples to unlock the full potential of web scraping with Python and gain valuable insights from high-quality dataKey Features:Build an initial portfolio of web scraping projects with detailed explanationsGrasp Python programming fundamentals related to web scraping and data extractionAcquire skills to code web scrapers, store data in desired formats, and employ the data professionallyPurchase of the print or Kindle book includes a free PDF eBookBook Description:Web scraping is a powerful tool for extracting data from the web, but it can be daunting for those without a technical background. Designed for novices, this book will help you grasp the fundamentals of web scraping and Python programming, even if you have no prior experience.Adopting a practical, hands-on approach, this updated edition of Hands-On Web Scraping with Python uses real-world examples and exercises to explain key concepts. Starting with an introduction to web scraping fundamentals and Python programming, you'll cover a range of scraping techniques, including requests, lxml, pyquery, Scrapy, and Beautiful Soup. You'll also get to grips with advanced topics such as secure web handling, web APIs, Selenium for web scraping, PDF extraction, regex, data analysis, EDA reports, visualization, and machine learning.This book emphasizes the importance of learning by doing. Each chapter integrates examples that demonstrate practical techniques and related skills. By the end of this book, you'll be equipped with the skills to extract data from websites, a solid understanding of web scraping and Python programming, and the confidence to use these skills in your projects for analysis, visualization, and information discovery.What You Will Learn:Master web scraping techniques to extract data from real-world websitesImplement popular web scraping libraries such as requests, lxml, Scrapy, and pyqueryDevelop advanced skills in web scraping, APIs, PDF extraction, regex, and machine learningAnalyze and visualize data with Pandas and PlotlyDevelop a practical portfolio to demonstrate your web scraping skillsUnderstand best practices and ethical concerns in web scraping and data extractionWho this book is for:This book is for beginners who want to learn web scraping and data extraction using Python. No prior programming knowledge is required, but a basic understanding of web-related concepts such as websites, browsers, and HTML is assumed. If you enjoy learning by doing and want to build a portfolio of web scraping projects and delve into data-related studies and application, then this book is tailored for your needs.

Build scalable and reliable data ecosystems using Data Mesh, Databricks Spark, and KafkaKey Features:Develop modern data skills used in emerging technologiesLearn pragmatic design methodologies such as Data Mesh and data lakehousesGain a deeper understanding of data governancePurchase of the print or Kindle book includes a free PDF eBookBook Description:Modern Data Architectures with Python will teach you how to seamlessly incorporate your machine learning and data science work streams into your open data platforms. You'll learn how to take your data and create open lakehouses that work with any technology using tried-and-true techniques, including the medallion architecture and Delta Lake.Starting with the fundamentals, this book will help you build pipelines on Databricks, an open data platform, using SQL and Python. You'll gain an understanding of notebooks and applications written in Python using standard software engineering tools such as git, pre-commit, Jenkins, and Github. Next, you'll delve into streaming and batch-based data processing using Apache Spark and Confluent Kafka. As you advance, you'll learn how to deploy your resources using infrastructure as code and how to automate your workflows and code development. Since any data platform's ability to handle and work with AI and ML is a vital component, you'll also explore the basics of ML and how to work with modern MLOps tooling. Finally, you'll get hands-on experience with Apache Spark, one of the key data technologies in today's market.By the end of this book, you'll have amassed a wealth of practical and theoretical knowledge to build, manage, orchestrate, and architect your data ecosystems.What You Will Learn:Understand data patterns including delta architectureDiscover how to increase performance with Spark internalsFind out how to design critical data diagramsExplore MLOps with tools such as AutoML and MLflowGet to grips with building data products in a data meshDiscover data governance and build confidence in your dataIntroduce data visualizations and dashboards into your data practiceWho this book is for:This book is for developers, analytics engineers, and managers looking to further develop a data ecosystem within their organization. While they're not prerequisites, basic knowledge of Python and prior experience with data will help you to read and follow along with the examples.

Ethics in Online AI-Based Systems: Risks and Opportunities in Current Technological Trends creates a space to explore the ethical relevance that new technologies under development may have. Stimulating reflection and considerations with respect to the design, deployment, and use of technology helps readers guide current and future technological advancements from an ethically informed position to ensure that such advancements contribute towards solving current global and social challenges that we, as a society, have today. This will not only be useful for researchers and professional engineers, but also for educators, policy makers, and ethicists. Recent technological advancements have deeply transformed society and the way people interact with each other. Instantaneous communication platforms have allowed connections with other people, forming global communities and creating unprecedented opportunities in many sectors, making access to online resources more ubiquitous by reducing limitations imposed by geographical distance and temporal constrains. These technological developments bear ethically relevant consequences with their deployment, and legislations often lag behind such advancements. Because the appearance and deployment of these technologies happen much faster than legislative procedures, the way these technologies affect social interactions have profound ethical effects before any legislative regulation can be built in order to prevent and mitigate those effects.

Dive into the world of cutting-edge data science with this captivating and comprehensive guide. Unveil the power of data analysis and interpretation as you embark on a journey through the realm of Julia programming. Whether you're a seasoned data enthusiast or a curious newcomer, this book is your ultimate companion to mastering the art of extracting insights from complex datasets.Inside these pages, you'll discover a treasure trove of techniques that unlock the true potential of data. From foundational concepts to advanced methodologies, the author expertly navigates through the intricacies of Julia, leading you towards data-driven discoveries. With a focus on practicality, each chapter seamlessly blends theory with real-world applications, making learning an exhilarating and fruitful experience.Breathe life into your analytical prowess as you explore essential topics such as machine learning, statistical modeling, and data visualization. Through hands-on examples and engaging exercises, you'll effortlessly grasp complex ideas, transforming them into tangible results. Harness the potential of Julia's high-performance capabilities and harness the insights hidden within your data like never before.What sets this book apart is its unparalleled dedication to demystifying the data science landscape. You'll uncover a holistic approach that not only hones your technical skills but also nurtures your creative problem-solving abilities. As you progress, you'll find yourself seamlessly transitioning from a reader to a practitioner, equipped with the confidence to tackle real-world challenges head-on.Whether you're a data scientist, analyst, researcher, or simply someone intrigued by the world of data, this book promises to be your beacon of knowledge. Let your curiosity guide you as you embark on an enlightening adventure through the art and science of data analysis. Are you ready to revolutionize your understanding of data? Your journey starts here.

Uncover the Power of Data: Your Journey Starts Here!Are you fascinated by the world of data and its incredible potential to transform industries and shape the future? Dive into the captivating realm of data science with our comprehensive guide, "Introduction to Data Science."In this engaging and accessible book, you will embark on a thrilling journey through the intricate world of data and learn how it drives innovation, informs decisions, and fuels discoveries across various domains. Whether you're a curious beginner or a professional looking to expand your knowledge, this book is your gateway to mastering the art and science of data analysis.What You'll Discover Inside:The Data Universe Unveiled: Delve into the fundamental concepts of data science and gain a deep understanding of the data lifecycle, types of data, and the role of data in modern society.Cracking the Code of Data Analysis: Learn the essential tools and techniques used by data scientists to collect, clean, and analyze data effectively. From Python to data visualization, you'll acquire the skills needed to extract valuable insights from raw data.The Power of Machine Learning: Explore the exciting world of machine learning and its applications. Discover how algorithms can predict trends, recommend products, and even enable self-driving cars.Real-World Applications: See data science in action through captivating case studies from diverse fields, such as healthcare, finance, marketing, and beyond. Witness how data-driven solutions are solving complex problems and shaping industries.Ethics and Responsible Data Science: Understand the ethical considerations and responsible practices that every data scientist must adhere to. Explore the implications of AI bias, privacy concerns, and the ethical use of data.Your Path to Data Science Mastery: Find guidance on how to pursue a career in data science, build a strong portfolio, and stay updated in this rapidly evolving field.With clear explanations, practical examples, and hands-on exercises, "Introduction to Data Science" makes complex concepts accessible and empowers you to harness the power of data in your personal and professional life. Whether you dream of becoming a data scientist or simply want to become a more data-savvy individual, this book is your first step toward unlocking the endless possibilities that data science offers.Join the data revolution today and embark on your exciting journey into the world of data science. Start turning raw data into actionable insights, and become a key player in shaping the future with "Introduction to Data Science."

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.

Build, design, and improve advanced business intelligence solutions using Tableau's newest updates, including new Tableau Desktop, Tableau Prep, and Tableau Server featuresPurchase of the print or Kindle book includes a free PDF eBookKey FeaturesMaster new Tableau 2023 features to solve real-world analytics challengesLearn how to use both pre-defined and your own Machine Learning models in TableauHow to manage Data Governance and secure high data qualityBook DescriptionThis edition of the bestselling Tableau guide will teach you how to leverage Tableau's newest features and offerings in various paradigms of the BI domain. Updated with fresh topics, including the newest features in Tableau Server,Prep, and Desktop, as well as up-to-date examples, this book will take you from mastering essential Tableau concepts to advance functionalities. A chapter on data governance has also been added.Throughout this book, you'll learn how to use Tableau Hyper files and Prep Builder to easily perform data preparation and handling, as well as complex joins, spatial joins, unions, and data blending tasks using practical examples. You'll also get to grips with executing data densification and explore other expert-level examples to help you with calculations, mapping, and visual design using Tableau extensions.Later chapters will teach you all about improving dashboard performance, connecting to Tableau Server, and understanding data visualization with examples. Finally, you'll cover advanced use cases, such as self-service analysis, time series analysis, geo-spatial analysis, and how to connect Tableau to Python and R to implement programming functionalities within Tableau.By the end of this book, you'll have mastered Tableau 2023 and be able to tackle common and advanced challenges in the BI domain.What you will learnLearn about various Tableau components, such as calculated fields, table calculations, and LOD expressionsMaster ETL (Extract, Transform, Load) techniques using Tableau Prep BuilderExplore and implement data storytelling with Python and RUnderstand Tableau Exchange by using accelerators, extensions, and connectorsInteract with Tableau Server to understand its functionalitiesStudy advanced visualizations and dashboard creation techniquesBrush up on powerful self-service analytics, time series analytics, and geo-spatial analyticsFind out why data governance matters and how to implement itWho this book is forThis book is designed for business analysts, business intelligence professionals, and data analysts who want to master Tableau to solve a range of data science and business intelligence problems. Prior exposure to Tableau will help you get to grips with the features more quickly, but it's not a prerequisite.Table of ContentsReviewing the BasicsGetting Your Data ReadyUsing Tableau Prep BuilderLearning about Joins, Blends, and Data StructuresIntroducing Table CalculationsUtilizing OData, Data Densification, Big Data, and Google BigQueryPracticing Level of Detail CalculationsGoing Beyond the BasicsWorking with MapsPresenting with TableauDesigning Dashboards and Best Practices for VisualizationsLeveraging Advanced AnalyticsImproving PerformanceExploring Tableau Server and Tableau CloudIntegrating Programming LanguagesDeveloping Data Governance Practices

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.

Learn how to use R for everything from workload automation and creating online reports, to interpreting data, map making, and more.Written by the founder of a very popular online training platform for the R programming language!The R programming language is a remarkably powerful tool for data analysis and visualization, but its steep learning curve can be intimidating for some. If you just want to automate repetitive tasks or visualize your data, without the need for complex math, R for the Rest of Us is for you.Inside you’ll find a crash course in R, a quick tour of the RStudio programming environment, and a collection of real-word applications that you can put to use right away. You’ll learn how to create informative visualizations, streamline report generation, and develop interactive websites—whether you’re a seasoned R user or have never written a line of R code.You’ll also learn how to:• Manipulate, clean, and parse your data with tidyverse packages like dplyr and tidyr to make data science operations more user-friendly• Create stunning and customized plots, graphs, and charts with ggplot2 to effectively communicate your data insights• Import geospatial data and write code to produce visually appealing maps automatically• Generate dynamic reports, presentations, and interactive websites with R Markdown and Quarto that seamlessly integrate code, text, and graphics• Develop custom functions and packages tailored to your specific needs, allowing you to extend R’s functionality and automate complex tasksUnlock a treasure trove of techniques to transform the way you work. With R for the Rest of Us, you’ll discover the power of R to get stuff done. No advanced statistics degree required.

Data analysis refers to the process of cleansing, modeling, inspecting and transforming data in order to identify useful information, derive informed conclusions and assist in decision-making. It has several dimensions and approaches, and is applied in a variety of sectors like social science, business and science. One of the fundamental concerns in information and artificial intelligence is intelligent data analysis (IDA). It reveals previously hidden facts and extracts potentially valuable information or facts from huge amounts of data. It also aids in decision-making. IDA is primarily based on artificial intelligence, records and visualization technology, machine learning and pattern recognition. It also assists in obtaining relevant information, necessary data and models from a large amount of data available online for making the best decisions. There are three steps involved in IDA including data preparation, data mining, and data validation and explanation of data. This book unravels the recent studies on intelligent data analysis. It will help the readers in keeping pace with the rapid changes in this area of study.

Intelligent data analysis (IDA) is a method for discovering useful and intriguing information from data to aid in decision making. An IDA task comprises prediction, knowledge discovery, process or system modeling, and constructing knowledge based systems. There are numerous applications of IDA methods in various fields, including finance, marketing, agriculture, and medicine. The demand and availability of more advanced IDA methods has grown in response to the rapidly increasing volume of real-time data that is generated due to an increase in the usage of electronic commerce, web, and multimedia technologies. There are a number of challenges in the application of IDA methods in real-world, such as lack of tools suitable for large data, and lack of user friendly and effective post processing tools. The other challenges include lack of a truly integrated data analysis environment and lack of efficient and automatic preprocessing tools. This book elucidates the challenges and solutions with respect to intelligent data analysis. The topics covered herein offer the readers new insights on this subject.

Learn to use ArcGIS Pro confidently, quickly, and effectively with the newest edition in this best-selling series. Getting to Know ArcGIS Pro 3.2 introduces readers to ArcGIS Pro, the world's most powerful desktop GIS. Getting to Know ArcGIS Pro 3.2 walks readers through how to build a geodatabase, discover 3D GIS, create maps for web and physical presentations, and more. With more than 300 full-color images, Getting to Know ArcGIS Pro 3.2 clarifies complicated processes such as developing a geoprocessing model, using Python to write a script tool, and creating space-time cubes for analysis in easy-to-follow workflows. This edition has been completely updated for use with ArcGIS Pro 3.2.Michael Law is a cartographer and GIS professional with more than 16 years of experience. He lives in Toronto, Canada. Amy Collins is a writer and editor who lives in Northern California.

No matter what electronic tools you take into the field, everyone takes a notebook or a guidebook. Along with all your equipment, be sure to include this fieldwork guide.   Fieldwork Handbook gives you tips, best practices, and activities that make your work in the field more productive and successful. No matter how you use location intelligence in the field, you can up your game with this book and improve your experience. Covering preparation, equipment, workflows, and data, this book will show you how to Organize and reconcile field dataEffectively coordinate and dispatch field resourcesRefine fieldwork workflowsSync the office with the fieldThis handbook is your friend in the field.

"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"--