Dataanalyse: generelt

DELTA 2.0 er en ny og helt opdateret udgave af Delta, der i ti år har været brugt i matematiklærernes grund-, efter- og videreuddannelse.DELTA 2.0 er del af serien MATEMATIK FOR LÆRERSTUDERENDE.Ud over DELTA 2.0 består serien af fem bøger med fokus på matematisk indhold: to om Tal, algebra og funktioner, to om Geometri og en om Stokastik, hvortil kommer en bog om elever med særlige behov i matematikundervisningen. Bøgerne i serien er et ambitiøst forsøg på at sammentænke de matematikfaglige aspekter af læreruddannelsen med fagdidaktiske og professionsrettede overvejelser.DELTA 2.0 er seriens almene fagdidaktik. Der er også fagdidaktiske overvejelser i de øvrige bøger i serien, men de er knyttet til specifikt matematisk indhold. DELTA 2.0 behandler mere generelle matematikdidaktiske problemstillinger som læringsteoretiske overvejelser i forbindelse med matematik, centrale aspekter af det at undervise i matematik og digitale teknologier som værktøj til at støtte elevers faglige læring af matematik. Det er en gennemgående idé i DELTA 2.0 at formulere matematikdidaktiske fokuspunkter, som i særlig grad er vigtige for lærere at reflektere over.DELTA 2.0 henvender sig ikke blot til lærerstuderende, men kan også anvendes i matematiklærernes efter- og videreuddannelse, fx diplom-, kandidat- eller masteruddannelser. Desuden vil matematiklærere kunne finde inspiration til udvikling af egenundervisning.

Kvalitative analyseprocesser giver konkret indblik i, hvordan du kan analysere empirisk materiale. Bogen starter der, hvor mange metodebøger stopper: Når du har dit empiriske materiale i hus, hvad gør du så? Analysearbejdet er helt centralt for kvalitativ forskning – det er gennem analyserne, at resultaterne fremkommer. Alligevel er det ofte ret dunkelt, hvordan analysen egentlig er foretaget. Denne bog sætter spot på selve analyseprocessen, de analytiske greb, som er anvendt, og hvordan teoretisk begrebsarbejde har guidet analysen undervejs. På den måde inviterer bogen læseren ind i forskerens værksted og retter fokus på selve analyseprocessen med dens kringlede proces, trin og vildveje, tilrettelæggelse og strategier. Bogen er inddelt i hermeneutiske, poststrukturalistiske og dialektiske analyseperspektiver. Det er imidlertid en væsentlig pointe, at det teoretiske afsæt ikke på nogen entydig måde giver én bestemt analysemetode. Derfor præsenterer bogen forskellige analysetilgange under hver af de tre overskrifter og viser, hvordan man på forskellige måder kan arbejde sig igennem den analytiske proces. Forfatterne tilbyder desuden anvisninger i form af modeller, analytikker og spørgsmål, som man kan lade sig inspirere af, når man skal planlægge og gennemføre sine egne analyser. Kvalitative analyseprocesser henvender sig primært til studerende, der skal analysere kvalitativt empirisk materiale som led i en lang eller mellemlang videregående uddannelse eller et ph.d.-forløb. Den er især anvendelig indenfor det pædagogisk-psykologiske felt og beslægtede områder. De beskrevne analyseprocesser har dog samme relevans udover disse felter, og bogen kan derfor anvendes af alle, som ønsker at blive klædt bedre på til at gennemføre teoretisk informerede kvalitative analyser.

** THE SUNDAY TIMES BESTSELLER **'A book for anyone who finds it difficult to believe in a better future' THE TIMESFeeling anxious, powerless, or confused about the future of our planet? This book will transform how you see our biggest environmental problems -- and how we can solve them.A STYLIST BEST NON-FICTION 2024 * A GUARDIAN BIGGEST FICTION AND NON-FICTION FOR 2024 * A WATERSTONES 'BOOK YOU NEED TO READ IN 2024' * A GUARDIAN 'FIVE GREAT READS' We are bombarded by doomsday headlines that tell us the soil won't be able to support crops, fish will vanish from our oceans, that we should reconsider having children.But in this bold, radically hopeful book, data scientist Hannah Ritchie argues that if we zoom out, a very different picture emerges. The data shows we've made so much progress on these problems, and so fast, that we could be on track to achieve true sustainability for the first time in history.Packed with the latest research, practical guidance and enlightening graphics, this book will make you rethink almost everything you've been told about the environment, from the virtues of eating locally and living in the countryside, to the evils of overpopulation, plastic straws and palm oil. It will give you the tools to understand what works, what doesn't and what we urgently need to focus on so we can leave a sustainable planet for future generations.These problems are big. But they are solvable. We are not doomed. We can build a better future for everyone. Let's turn that opportunity into reality.'Practical and truly essential' MARGARET ATWOOD * 'Does for the environment what Hans Rosling did for health' BILL GATES * 'Invigorating, inspiring, often surprising' DAVID WALLACE-WELLS * 'I find it hard to express how much I love this book' RUTGER BREGMAN * 'An unmissable myth-busting book to save our planet - read it' TIM SPECTOR

Analyseteknik - instrumentering og metoder giver læseren en grundlæggende indføring i de vigtigste instrumenter og metoder i det analytiske laboratorium.Bogen dækker de tre analytiske hovedområder: Chromatografi, spektrometri og potentiometri samt områderne statistisk resultatvurdering, metodevalidering og udstyrskvalificering. Der er lagt stor vægt på bogens praktiske anvendelighed, stoffet er tæt relateret til det daglige arbejde i analyselaboratoriet, og gennemgangen af de enkelte emner er overalt suppleret med laboratorierelaterede beregningseksempler og testopgaver.Bogen kan anvendes både i undervisningssammenhæng og som håndbog i laboratorier. Derfor er stoffet gennemgået på en let tilgængelig form, og de enkelte afsnit er disponeret således, at de kan læses uafhængigt af hinanden.

MULTIMODAL INTERAKTIONSANALYSE giver mulighed for at undersøge sociale interaktioner i detaljen og dermed fremanalysere, hvordan sociale relationer, identitet og mening skabes og tillægges mening det være sig i mødelokalet, i butikken, i klasseværelset, i det offentlige rum eller om middagsbordet.Metoden er kendetegnet ved at fokusere ikke kun på det talte ord, men på alle de forskelige modaliteter, der medvirker til at skabe mening i social interaktion, såsom tale, gestikulation, blikretning, mimik og genstande. For at kunne observere og analysere dette er det nødvendigt at indsamle data gennem videoetnografisk metode.Denne bog giver en samlet forskningsbaseret introduktion til de væsentligste aspekter i et projektforløb, der indebærer videoetnografisk dataindsamling og multimodal interaktionsanalyse. Bogen gennemgår således:Hvad multimodal interaktionsanalyse kan bruges til Tilgangens afsæt i den såkaldte EMCA-tradition. Tilrettelæggelse og gennemførelse af videoetnografisk dataindsamling Bearbejdning af det indsamlede videomateriale Udarbejdelse af multimodale interaktionsanalyser Videobaseret feedback til deltagere i projektet.

Generative modeling is one of the hottest topics in AI. Its now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models.Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, youll understand how to make your models learn more efficiently and become more creative.Discover how variational autoencoders can change facial expressions in photosBuild practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generationCreate recurrent generative models for text generation and learn how to improve the models using attentionUnderstand how generative models can help agents to accomplish tasks within a reinforcement learning settingExplore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using PythonKey FeaturesGrasp the principles of data centricity and apply them to real-world scenariosGain experience with quality data collection, labeling, and synthetic data creation using PythonDevelop essential skills for building reliable, responsible, and ethical machine learning solutionsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets.This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of 'small data'. Delving into the building blocks of data-centric ML/AI, you'll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you'll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you'll get a roadmap for implementing data-centric ML/AI in diverse applications in Python.By the end of this book, you'll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.What you will learnUnderstand the impact of input data quality compared to model selection and tuningRecognize the crucial role of subject-matter experts in effective model developmentImplement data cleaning, labeling, and augmentation best practicesExplore common synthetic data generation techniques and their applicationsApply synthetic data generation techniques using common Python packagesDetect and mitigate bias in a dataset using best-practice techniquesUnderstand the importance of reliability, responsibility, and ethical considerations in ML/AIWho this book is forThis book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.Table of ContentsExploring Data-Centric Machine LearningFrom Model-Centric to Data-Centric - ML's EvolutionPrinciples of Data-Centric MLData Labeling Is a Collaborative ProcessTechniques for Data CleaningTechniques for Programmatic Labeling in Machine LearningUsing Synthetic Data in Data-Centric Machine LearningTechniques for Identifying and Removing BiasDealing with Edge Cases and Rare Events in Machine LearningKick-Starting Your Journey in Data-Centric Machine Learning

Solve real-world data problems and create data-driven workflows for easy data movement and processing at scale with Azure Data FactoryKey Features:Learn how to load and transform data from various sources, both on-premises and on cloudUse Azure Data Factory's visual environment to build and manage hybrid ETL pipelinesDiscover how to prepare, transform, process, and enrich data to generate key insightsBook Description:This new edition of the Azure Data Factory Cookbook, fully updated to reflect ADS V2, will help you get up and running by showing you how to create and execute your first job in ADF.You'll learn how to branch and chain activities, create custom activities, and schedule pipelines, as well as discovering the benefits of cloud data warehousing, Azure Synapse Analytics, and Azure Data Lake Gen2 Storage.With practical recipes, you'll learn how to actively engage with analytical tools from Azure Data Services and leverage your on-premises infrastructure with cloud-native tools to get relevant business insights. As you advance, you'll be able to integrate the most commonly used Azure Services into ADF and understand how Azure services can be useful in designing ETL pipelines. You'll familiarize yourself with the common errors that you may encounter while working with ADF and find out how to use the Azure portal to monitor pipelines. You'll also understand error messages and resolve problems in connectors and data flows with the debugging capabilities of ADF.Two new chapters covering Azure Data Explorer and key best practices have been added, along with new recipes throughout.By the end of this book, you'll be able to use ADF as the main ETL and orchestration tool for your data warehouse or data platform projects.What You Will Learn:Create an orchestration and transformation job in ADFDevelop, execute, and monitor data flows using Azure SynapseCreate big data pipelines using Databricks and Delta tablesWork with big data in Azure Data Lake using Spark PoolMigrate on-premises SSIS jobs to ADFIntegrate ADF with commonly used Azure services such as Azure ML, Azure Logic Apps, and Azure FunctionsRun big data compute jobs within HDInsight and Azure DatabricksCopy data from AWS S3 and Google Cloud Storage to Azure Storage using ADF's built-in connectorsWho this book is for:This book is for ETL developers, data warehouse and ETL architects, software professionals, and anyone else who wants to learn about the common and not-so-common challenges faced while developing traditional and hybrid ETL solutions using Microsoft's Azure Data Factory. You'll also find this book useful if you are looking for recipes to improve or enhance your existing ETL pipelines. Basic knowledge of data warehousing is a prerequisite.

Troubleshoot query performance issues, identify anti-patterns in your code, and write efficient T-SQL queries with this guide for T-SQL developersKey Features:A definitive guide to mastering the techniques of writing efficient T-SQL codeLearn query optimization fundamentals, query analysis, and how query structure impacts performanceDiscover insightful solutions to detect, analyze, and tune query performance issuesPurchase of the print or Kindle book includes a free PDF eBookBook Description:Data professionals seeking to excel in Transact-SQL (T-SQL) for Microsoft SQL Server and Azure SQL Database often lack comprehensive resources. This updated second edition of Learn T-SQL Querying focuses on indexing queries and crafting elegant T-SQL code, catering to all data professionals seeking mastery in modern SQL Server versions and Azure SQL Database.Starting with query processing fundamentals, this book lays a solid foundation for writing performant T-SQL queries. You'll explore the mechanics of the Query Optimizer and Query Execution Plans, learning how to analyze execution plans for insights into current performance and scalability. Through dynamic management views (DMVs) and dynamic management functions (DMFs), you'll build diagnostic queries. This book thoroughly covers indexing for T-SQL performance and provides insights into SQL Server's built-in tools for expedited resolution of query performance and scalability issues. Further, hands-on examples will guide you through implementing features such as avoiding UDF pitfalls, understanding predicate SARGability, Query Store, and Query Tuning Assistant.By the end of this book, you'll have developed the ability to identify query performance bottlenecks, recognize anti-patterns, and skillfully avoid such pitfalls.What You Will Learn:Identify opportunities to write well-formed T-SQL statementsFamiliarize yourself with the Cardinality Estimator for query optimizationCreate efficient indexes for your existing workloadsImplement best practices for T-SQL queryingExplore Query Execution Dynamic Management ViewsUtilize the latest performance optimization features in SQL Server 2017, 2019, and 2022Safeguard query performance during upgrades to newer versions of SQL ServerWho this book is for:This book is for database administrators, database developers, data analysts, data scientists and T-SQL practitioners who want to master the art of writing efficient T-SQL code and troubleshooting query performance issues through practical examples. A basic understanding of T-SQL syntax, writing queries in SQL Server, and using the SQL Server Management Studio tool will be helpful to get started.

Rise above the competition and excel in your next interview with this one-stop guide to Python, SQL, version control, statistics, machine learning, and much moreKey FeaturesAcquire highly sought-after skills of the trade, including Python, SQL, statistics, and machine learningGain the confidence to explain complex statistical, machine learning, and deep learning theoryExtend your expertise beyond model development with version control, shell scripting, and model deployment fundamentalsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe data science job market is saturated with professionals of all backgrounds, including academics, researchers, bootcampers, and Massive Open Online Course (MOOC) graduates. This poses a challenge for companies seeking the best person to fill their roles. At the heart of this selection process is the data science interview, a crucial juncture that determines the best fit for both the candidate and the company.Cracking the Data Science Interview provides expert guidance on approaching the interview process with full preparation and confidence. Starting with an introduction to the modern data science landscape, you'll find tips on job hunting, resume writing, and creating a top-notch portfolio. You'll then advance to topics such as Python, SQL databases, Git, and productivity with shell scripting and Bash. Building on this foundation, you'll delve into the fundamentals of statistics, laying the groundwork for pre-modeling concepts, machine learning, deep learning, and generative AI. The book concludes by offering insights into how best to prepare for the intensive data science interview.By the end of this interview guide, you'll have gained the confidence, business acumen, and technical skills required to distinguish yourself within this competitive landscape and land your next data science job.What you will learnExplore data science trends, job demands, and potential career pathsSecure interviews with industry-standard resume and portfolio tipsPractice data manipulation with Python and SQLLearn about supervised and unsupervised machine learning modelsMaster deep learning components such as backpropagation and activation functionsEnhance your productivity by implementing code versioning through GitStreamline workflows using shell scripting for increased efficiencyWho this book is forWhether you're a seasoned professional who needs to brush up on technical skills or a beginner looking to enter the dynamic data science industry, this book is for you. To get the most out of this book, basic knowledge of Python, SQL, and statistics is necessary. However, anyone familiar with other analytical languages, such as R, will also find value in this resource as it helps you revisit critical data science concepts like SQL, Git, statistics, and deep learning, guiding you to crack through data science interviews.Table of ContentsExploring the Modern Data Science LandscapeFinding a Job in Data ScienceProgramming with PythonVisualizing Data and Data StorytellingQuerying Databases with SQLScripting with Shell and Bash Commands in LinuxUsing Git for Version ControlMining Data with Probability and StatisticsUnderstanding Feature Engineering and Preparing Data for ModelingMastering Machine Learning ConceptsBuilding Networks with Deep LearningImplementing Machine Learning Solutions with MLOpsMastering the Interview RoundsNegotiating Compensation

Design and implement a results-driven data strategy with this five-stage guide to leveraging existing business assets and creating value through data projects.

Turn raw data into meaningful solutions¿DESCRIPTION Learn Data Science from Scratch equips you with the essential tools and techniques, from Python libraries to machine learning algorithms, to tackle real-world problems and make informed decisions.This book provides a thorough exploration of essential data science concepts, tools, and techniques. Starting with the fundamentals of data science, you will progress through data collection, web scraping, data exploration and visualization, and data cleaning and pre-processing. You will build the required foundation in statistics and probability before diving into machine learning algorithms, deep learning, natural language processing, recommender systems, and data storage systems. With hands-on examples and practical advice, each chapter offers valuable insights and key takeaways, empowering you to master the art of data-driven decision making.By the end of this book, you will be well-equipped with the essential skills and knowledge to navigate the exciting world of data science. You will be able to collect, analyze, and interpret data, build and evaluate machine learning models, and effectively communicate your findings, making you a valuable asset in any data-driven environment.WHAT YOU WILL LEARN¿ Master key data science tools like Python, NumPy, Pandas, and more.¿ Build a strong foundation in statistics and probability for data analysis.¿ Learn and apply machine learning, from regression to deep learning.¿ Expertise in NLP and recommender systems for advanced analytics.¿ End-to-end data project from data collection to model deployment, with planning and execution.WHO THIS BOOK IS FORThis book is ideal for beginners with a basic understanding of programming, particularly in Python, and a foundational knowledge of mathematics. It is well-suited for aspiring data scientists and analysts.

Uncover valuable business insights by leveraging the power of Kibana to navigate and interpret datasets for improved decision makingKey FeaturesGain profound understanding of the end-to-end workings of KibanaExplore the powerful administration features in Kibana 8.x for managing and supporting data ingestion pipelinesBuild your own analytics and visualization solution from scratchPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionUnleash the full potential of Kibana-an indispensable tool for data analysts to seamlessly explore vast datasets, uncover key insights, identify trends and anomalies, and share results. This book guides you through its user-friendly interface, interactive visualizations, and robust features, including real-time data monitoring and advanced analytics, showing you how Kibana revolutionizes your approach to navigating and analyzing complex datasets.Starting with the foundational steps of installing, configuring, and running Kibana, this book progresses systematically to explain the search and data visualization capabilities for data stored in the Elasticsearch cluster. You'll then delve into the practical details of creating data views and optimizing spaces to better organize the analysis environment. As you advance, you'll get to grips with using the discover interface and learn how to build different types of extensive visualizations using Lens.By the end of this book, you'll have a complete understanding of how Kibana works, helping you leverage its capabilities to build an analytics and visualization solution from scratch for your data-driven use case.What you will learnCreate visualizations using the Visualize interface in KibanaBuild shareable search dashboards to drill down and perform advanced analysis and reportingSearch data to make correlations and identify and explain trendsEmbed dashboards, share links, and export PNG, PDF, or CSV files and send as an attachmentConfigure and tweak advanced settings to best manage saved objects in KibanaImplement several types of aggregations working behind the scenes of extensive visualizationsWho this book is forIf you're a data analyst or a data engineer, this book is for you. It's also a useful resource to database administrators, analysts, and business users looking to build a foundation in creating intuitive dashboards using Kibana 8.x and data analysis techniques for improved decision making. Foundational knowledge of Elasticsearch fundamentals will provide an added advantage.Table of ContentsIntroduction to KibanaCreating Data Views and Introducing SpacesDiscovering Data through DiscoverHow About We Visualize?Powering Visualizations with Near Real-Time UpdatesData Analysis with Machine LearningGraph VisualizationFinally, the DashboardES|QL and Advanced Kibana ConceptsQuery DSL and Management through Kibana

The development of the World Wide Web has huge and increasing amount of data and information. This huge data provided by the web has become an important information resource for all internet users. Furthermore, the low cost of web data makes it more attractive to researchers. Users can retrieve web data by browsing and keyword searching. Still, it may have lots of limitations to these techniques. Normally, several web links are getting users while they browsing for one data so it is hard for researchers to retrieve data efficiently. Mostly, pages in web contain both hyperlinks and text documents to other documents. Furthermore, mailing lists, newsgroups, and forums are considered as another form of data sources. Thus, web mining can also support web mining design and implementation but this becomes provocation for people with extracting web information. Web mining is able to support the web information sources based on user needs, including knowing availability, importance and relevance of web systems; it should be able to select extracted data, because both related and not related information are present in Web sites; it should be easy to collect data and then analyze and help to build models and produce validity. Internet users have improved significantly over the last decade and endure to growth. Through the user numbers, data available in web, persistent to increase exponentially. E- commerce is one of the application areas related to information mining. Fast growth of internet users has improved e-business applications. Many attempts are made on "breaking the syntax barrier" in web and many of them depend on text corpora of the semantic information used completely by statistical techniques. Ontology framework plays a key role in semantic web along with the artificial intelligence purposes and these contain Resource Description Framework (RDF) and XML. Ontology has developed into a necessary model tool for applying various intelligent systems which represent domain knowledge, which is easily understandable by both humans and machines. Ontologies play important role to make interoperability efficient and smooth, among heterogeneous systems. Ontology basically provides the link between particular domain concepts. The aim of ontology is to attain good knowledge about that system that can be circulated between people and application framework and intend to gain knowledge of domain and their role involves the semantics design exactly in a generic method, offering the agreement premise inside the area. In general, Ontology covers four key components namely: instances, concepts, axioms and relations. Concept was key element and is a basic domain with collection of group or objects or abstract set and normally means a common knowledge shared among group of members.

The mathematical theory of tangles, the centrepiece of the celebrated Robertson-Seymour theory of graph minors, finds precise structure in imprecise data. Assuming only basic undergraduate mathematics, this book shows how tangles can identify, relate, and structure types in data: of behaviour, political views, texts, or proteins.

Take your organization's data maturity to the next level by operationalizing data governanceKey FeaturesDevelop the mindset and skills essential for successful data stewardshipApply practical advice and industry best practices, spanning data governance, quality management, and compliance, to enhance data stewardshipFollow a step-by-step program to develop a data operating model and implement data stewardship effectivelyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionIn the competitive data-centric world, mastering data stewardship is not just a requirement-it's the key to organizational success. Unlock strategic excellence with Data Stewardship in Action, your guide to exploring the intricacies of data stewardship and its implementation for maximum efficiency.From business strategy to data strategy, and then to data stewardship, this book shows you how to strategically deploy your workforce, processes, and technology for efficient data processing. You'll gain mastery over the fundamentals of data stewardship, from understanding the different roles and responsibilities to implementing best practices for data governance. You'll elevate your data management skills by exploring the technologies and tools for effective data handling. As you progress through the chapters, you'll realize that this book not only helps you develop the foundational skills to become a successful data steward but also introduces innovative approaches, including leveraging AI and GPT, for enhanced data stewardship.By the end of this book, you'll be able to build a robust data governance framework by developing policies and procedures, establishing a dedicated data governance team, and creating a data governance roadmap that ensures your organization thrives in the dynamic landscape of data management.What you will learnEnhance your job prospects by understanding the data stewardship field, roles, and responsibilitiesDiscover how to develop a data strategy and translate it into a functional data operating modelDevelop an effective and efficient data stewardship programGain practical experience of establishing a data stewardship initiativeImplement purposeful governance with measurable ROIPrioritize data use cases with the value and effort matrixWho this book is forThis book is for professionals working in the field of data management, including business analysts, data scientists, and data engineers looking to gain a deeper understanding of the data steward role. Senior executives who want to (re)establish the data governance body in their organizations will find this resource invaluable. While accessible to both beginners and professionals, basic knowledge of data management concepts, such as data modeling, data warehousing, and data quality, is a must to get started.Table of ContentsFrom Business Strategy to Data Strategy to Data StewardshipHow Data Stewardship can help your organizationGetting Started with the Data Stewardship ProgramDeveloping a Comprehensive Data Management StrategyPeople, Process, TechnologyEstablishing a Data Governance OrganizationData Steward Roles and ResponsibilitiesEffective Data StewardshipSupercharge Data Governance and Stewardship with GPTData Stewardship Best PracticesTheory versus Real LifeCase Studies

Transform your data into insights with must-know techniques and mathematical concepts to unravel the secrets hidden within your dataKey FeaturesLearn practical data science combined with data theory to gain maximum insights from dataDiscover methods for deploying actionable machine learning pipelines while mitigating biases in data and modelsExplore actionable case studies to put your new skills to use immediatelyPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionPrinciples of Data Science bridges mathematics, programming, and business analysis, empowering you to confidently pose and address complex data questions and construct effective machine learning pipelines. This book will equip you with the tools to transform abstract concepts and raw statistics into actionable insights.Starting with cleaning and preparation, you'll explore effective data mining strategies and techniques before moving on to building a holistic picture of how every piece of the data science puzzle fits together. Throughout the book, you'll discover statistical models with which you can control and navigate even the densest or the sparsest of datasets and learn how to create powerful visualizations that communicate the stories hidden in your data.With a focus on application, this edition covers advanced transfer learning and pre-trained models for NLP and vision tasks. You'll get to grips with advanced techniques for mitigating algorithmic bias in data as well as models and addressing model and data drift. Finally, you'll explore medium-level data governance, including data provenance, privacy, and deletion request handling.By the end of this data science book, you'll have learned the fundamentals of computational mathematics and statistics, all while navigating the intricacies of modern ML and large pre-trained models like GPT and BERT.What you will learnMaster the fundamentals steps of data science through practical examplesBridge the gap between math and programming using advanced statistics and MLHarness probability, calculus, and models for effective data controlExplore transformative modern ML with large language modelsEvaluate ML success with impactful metrics and MLOpsCreate compelling visuals that convey actionable insightsQuantify and mitigate biases in data and ML modelsWho this book is forIf you are an aspiring novice data scientist eager to expand your knowledge, this book is for you. Whether you have basic math skills and want to apply them in the field of data science, or you excel in programming but lack the necessary mathematical foundations, you'll find this book useful. Familiarity with Python programming will further enhance your learning experience.Table of ContentsData Science TerminologyTypes of DataThe Five Steps of Data ScienceBasic MathematicsImpossible or Improbable - A Gentle Introduction to ProbabilityAdvanced ProbabilityWhat are the Chances? An Introduction to StatisticsAdvanced StatisticsCommunicating Data How to Tell if Your Toaster is Learning - Machine Learning EssentialsPredictions Don't Grow on Trees, or Do They?Introduction to Transfer Learning and Pre-trained ModelsMitigating Algorithmic Bias and Tackling Model and Data DriftAI GovernanceNavigating Real-World Data Science Case Studies in Action

Unveiling insights, unleashing potential: Navigating the depths of data warehousing and mining for a data-driven tomorrow¿¿DESCRIPTION Data warehouse and data mining are essential technologies in the field of data analysis and business intelligence. Data warehouse provides a centralized repository of structured data and facilitates data storage and retrieval. Data mining, on the other hand, utilizes various algorithms and techniques to extract valuable patterns, trends, and insights from large datasets.The book explains the ins and outs of data warehousing by discussing its principles, benefits, and components, differentiating it from traditional databases. The readers will explore warehouse architecture, learn to navigate OLTP and OLAP systems, grasping the crux of the difference between ROLAP and MOLAP. The book is designed to help you discover data mining secrets with techniques like classification and clustering. You will be able to advance your skills by handling multimedia, time series, and text, staying ahead in the evolving data mining landscape. By the end of this book, you will be equipped with the skills and knowledge to confidently translate business questions into actionable strategies, extracting valuable insights for informed decisions.WHAT YOU WILL LEARN¿ Designing and building efficient data warehouses¿ Handling diverse data types for comprehensive insights¿ Mastering various data mining techniques¿ Translating business questions into mining strategies¿ Techniques for pattern discovery and knowledge extractionWHO THIS BOOK IS FORFrom aspiring data analysts, data professionals, IT managers, to business intelligence practitioners, this book caters to a diverse audience.

Former Wall Street Journal technology reporter Yukari Iwatani Kane delves deep inside Apple in the two years since Steve Jobs's death, revealing the tensions and challenges CEO Tim Cook and his team face as they try to sustain Jobs's vision and keep the company moving forward.Steve Jobs's death raised one of the most pressing questions in the tech and business worlds: Could Apple stay great without its iconic leader? Many inside the company were eager to prove that Apple could be just as innovative as it had been under Jobs. Others were painfully aware of the immense challenge ahead. As its business has become more complex and global, Apple has been subject to intense scrutiny, much of it critical. Maintaining market leadership is crucial as it tries to conquer new frontiers and satisfy the public's insatiable appetite for "insanely great" products.Based on more than two hundred interviews with current and former executives, business partners, Apple watchers, and others, Haunted Empire is an illuminating portrait of Apple today that offers clues to its future. With nuanced insights and colorful details that only a seasoned journalist could glean, Kane goes beyond the myths and headlines. She explores Tim Cook's leadership and its impact on Jobs's loyal lieutenants, new product development, and Apple's relationships with Wall Street, the government, tech rivals, suppliers, the media, and consumers. Hard-hitting yet fair, Haunted Empire reveals the perils and opportunities an iconic company faces when it loses its visionary leader.

Developing predictive models through data mining is a multifaceted process that involves extracting valuable insights and patterns from vast datasets to make informed predictions about future trends or outcomes. At its core, data mining encompasses various techniques and methodologies aimed at uncovering hidden patterns and relationships within data. The journey begins with data collection, where a diverse range of information is amassed from various sources. This raw data then undergoes preprocessing, involving cleaning, integration, normalization, and feature selection, to ensure its suitability for analysis. The selected features may include numerical, textual, or categorical data, each requiring specific transformations. Machine learning algorithms are then employed to build predictive models based on historical data patterns, allowing the model to learn and generalize from past experiences. Supervised learning approaches, such as regression and classification, are common for predictive modeling, while unsupervised learning techniques, like clustering, can reveal hidden structures within the data. The model's performance is assessed through rigorous evaluation metrics, and iterative refinement may be performed to enhance accuracy. Cross-validation techniques are often employed to assess the model's robustness and generalizability. Additionally, ensembling methods, combining multiple models, can further improve predictive accuracy. Interpretability is crucial, and efforts are made to comprehend and explain the model's decision-making processes, especially in fields where transparency is essential. The development of predictive models through data mining finds applications across diverse domains, including finance, healthcare, marketing, and more. Ultimately, this process empowers decision-makers with valuable insights, enabling them to anticipate future trends, mitigate risks, and make informed decisions based on the patterns extracted from the wealth of available data.

Data preprocessing is a crucial step in the realm of software agents and classification data mining, playing a pivotal role in enhancing the accuracy and efficiency of the subsequent analysis. This multifaceted process involves the transformation and manipulation of raw data into a format suitable for classification algorithms, ensuring that the extracted patterns and insights are meaningful and reliable. The initial phase encompasses data cleaning, where missing values, outliers, and inconsistencies are addressed, preventing these anomalies from influencing the classification results. Subsequently, data integration combines disparate sources, providing a comprehensive dataset for analysis. The normalization or scaling phase standardizes numerical features, eliminating potential biases introduced by varying scales. Feature selection then optimizes the dataset by identifying and retaining the most relevant attributes, reducing computational complexity and enhancing model interpretability. Textual and categorical data are often transformed through techniques like tokenization and one-hot encoding to facilitate the application of classification algorithms. Dimensionality reduction methods, such as principal component analysis (PCA), may be employed to further streamline the dataset. Additionally, addressing issues of class imbalance ensures that the classifier is not skewed towards the majority class, fostering a more accurate representation of the underlying patterns. Through these meticulous preprocessing steps, software agents are equipped with a refined dataset that sets the stage for effective classification in data mining, allowing for the extraction of meaningful patterns and insights from complex and diverse datasets.

Multimedia is nothing but Analysis of variety of multimedia data to extract patterns based on statistical relationship. It's a melody vocal by accord with multi-channel and multi-model bits of information construction. Its crucial role is to notify, educate and-or entertain every one. Multimedia is pervade, exciting and involving means of info edutainment with multiple facet and extended approbation. Multimedia data commonly use in the field of TSE (information science, and engineering), geography, modern biology, medicine, weather forecast, biometrics, manufacturing, digital libraries and retailing, journalism, art-entertainment, social-sciences and space learning. Multimedia or Interactive media database structure incorporates a sight and sound records supervision framework which handles and gives base to putting away, extricating and controlling mixed media information from mixed media information. Multimedia-data contains structured information and un-structured information, for example, text, audio, graphs, images, video and media. Multimedia data extracting is a sub-field of DM which is use to discover fascinating data of hidden understanding from multimedia data. Multimedia DM is a form of data-mining. Data- mining algorithms use to segment data to categorize helpful patterns and to forecast. Regardless of the achievement in many areas, data-mining demanding task.

Define security in the context of software applications Security in the context of software applications is the ability to protect application users and their data from unauthorized access, use, modification, or destruction. Secure applications are reliable and useful, and can be used by their users without fear.** Here are some specific examples of security threats that software applications can face: Unauthorized access: This is when someone who is not authorized to use an application gains access to it. This can be done through a variety of methods, such as hacking, social engineering, or exploiting vulnerabilities in the application's code.Unauthorized use: This is when someone who is authorized to use an application uses it for unauthorized purposes. For example, a user might use an application to access sensitive data that they are not authorized to see.Modification: This is when someone changes the data or code of an application. This can be done to steal data, insert malicious code, or simply to disrupt the application's functionality.Destruction: This is when someone intentionally destroys an application or its data. This can be done through a variety of methods, such as deleting files, corrupting data, or disabling the application's infrastructure.¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿, ¿¿¿¿¿¿, ¿¿¿¿¿¿, ¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿. ¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿, ¿¿¿¿¿¿ ¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿¿¿¿¿¿ ¿¿¿¿¿¿