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.

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

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.

An introduction to the Python programming language and its most popular tools for scientists, engineers, students, and anyone who wants to use Python for research, simulations, and collaboration.Doing Science with Python introduces readers to the most popular coding tools for scientific research, such as Anaconda, Spyder, Jupyter Notebooks, and JupyterLab, as well as dozens of important Python libraries for working with data, including NumPy, matplotlib, and pandas. No prior programming experience is required! You'll be guided through setting up a professional coding environment, then get a crash course on programming with Python, and explore the many tools and libraries ideal for working with data, designing visualizations, simulating natural events, and more. In the book's applied projects, you'll use these tools to write programs that perform tasks like counting tree rings, creating an interactive science slideshow, and simulating the foraging patterns of animals.

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.

Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using PythonKey Features- Grasp the principles of data centricity and apply them to real-world scenarios- Gain experience with quality data collection, labeling, and synthetic data creation using Python- Develop essential skills for building reliable, responsible, and ethical machine learning solutions- Purchase 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 learn- Understand the impact of input data quality compared to model selection and tuning- Recognize the crucial role of subject-matter experts in effective model development- Implement data cleaning, labeling, and augmentation best practices- Explore common synthetic data generation techniques and their applications- Apply synthetic data generation techniques using common Python packages- Detect and mitigate bias in a dataset using best-practice techniques- Understand 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 Contents- Exploring Data-Centric Machine Learning- From Model-Centric to Data-Centric - ML's Evolution- Principles of Data-Centric ML- Data Labeling Is a Collaborative Process- Techniques for Data Cleaning- Techniques for Programmatic Labeling in Machine Learning- Using Synthetic Data in Data-Centric Machine Learning- Techniques for Identifying and Removing Bias- Dealing with Edge Cases and Rare Events in Machine Learning- Kick-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 code- Learn query optimization fundamentals, query analysis, and how query structure impacts performance- Discover insightful solutions to detect, analyze, and tune query performance issues- Purchase of the print or Kindle book includes a free PDF eBookBook Description:Data professionals seeking to excel in Transact-SQL for Microsoft SQL Server and Azure SQL Database often lack comprehensive resources. Learn T-SQL Querying second edition focuses on indexing queries and crafting elegant T-SQL code enabling data professionals gain mastery in modern SQL Server versions (2022) and Azure SQL Database. The book covers new topics like logical statement processing flow, data access using indexes, and best practices for tuning T-SQL queries.Starting with query processing fundamentals, the book lays a foundation for writing performant T-SQL queries. You'll explore the mechanics of the Query Optimizer and Query Execution Plans, learning to analyze execution plans for insights into current performance and scalability. Using dynamic management views (DMVs) and dynamic management functions (DMFs), you'll build diagnostic queries. The book covers indexing and delves into SQL Server's built-in tools to expedite resolution of T-SQL query performance and scalability issues. Hands-on examples will guide you to avoid UDF pitfalls and understand features like 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 avoid pitfallsWhat You Will Learn:- Identify opportunities to write well-formed T-SQL statements- Familiarize yourself with the Cardinality Estimator for query optimization- Create efficient indexes for your existing workloads- Implement best practices for T-SQL querying- Explore Query Execution Dynamic Management Views- Utilize the latest performance optimization features in SQL Server 2017, 2019, and 2022- Safeguard 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.Table of Contents- Understanding Query Processing- Mechanics of the Query Optimizer- Exploring Query Execution Plans- Indexing for T-SQL Performance- Writing Elegant T-SQL Queries- Discovering T-SQL Anti-patterns in Depth - Building diagnostic queries using DMVs and DMFs- Building XEvent profiler traces- Comparative analysis of Query Plans- Tracking performance history with Query Store- Troubleshooting Live Queries- Managing optimizer changes with the Query Tuning Assistant

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

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 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 Features:- Learn practical data science combined with data theory to gain maximum insights from data- Discover methods for deploying actionable machine learning pipelines while mitigating biases in data and models- Explore actionable case studies to put your new skills to use immediately- Purchase of the print or Kindle book includes a free PDF eBookBook Description:Principles 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 Learn:- Master the fundamentals steps of data science through practical examples- Bridge the gap between math and programming using advanced statistics and ML- Harness probability, calculus, and models for effective data control- Explore transformative modern ML with large language models- Evaluate ML success with impactful metrics and MLOps- Create compelling visuals that convey actionable insights- Quantify and mitigate biases in data and ML modelsWho this book is for:If 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 Contents- Data Science Terminology- Types of Data- The Five Steps of Data Science- Basic Mathematics- Impossible or Improbable - A Gentle Introduction to Probability- Advanced Probability- What are the Chances? An Introduction to Statistics- Advanced Statistics- Communicating Data- How to Tell if Your Toaster is Learning - Machine Learning Essentials- Predictions Don't Grow on Trees, or Do They?- Introduction to Transfer Learning and Pre-trained Models- Mitigating Algorithmic Bias and Tackling Model and Data Drift- AI Governance- Navigating 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.

Mathematik für die Informatik: Dieses Buch bringt Ihnen die Grundlagen beiDieses Lehrbuch bietet eine Einführung in die grundlegenden Begriffe und Strukturen der Mathematik, welche am Anfang eines Informatikstudiums relevant sind. Weiterhin demonstriert es Anwendungen von mathematischen Konzepten und Methoden in der Informatik. Diese betreffen insbesondere formale Methoden der Programmverifikation und -entwicklung und den Entwurf von generischen Programmen. Ein spezielles Konzept mit einer leicht verständlichen Vermittlung des Stoffs, vielen Beispielen mit Rückgriffen auf die Schulmathematik und detaillierten Beweisen (verbunden mit der Erklärung des logischen Hintergrunds) erleichtert den Einstieg in die Mathematik an einer wissenschaftlichen Hochschule. Dadurch werden die Studierenden auch auf spätere Begriffe und tiefergehende Anwendungen der Mathematik in der Informatik gut vorbereitet. Die 5. Auflage erweitert den Inhalt um die Grundlagen der linearen Algebra. Die Übungsaufgaben zu den einzelnen Kapitel helfen, das Erlernte zu festigen und zu kontrollieren. Zahlreiche Lösungsvorschläge am Ende des Buchs ermöglichen die Überprüfung der eigenen Lösungen.Der InhaltMengentheoretische Grundlagen ¿ Logische Grundlagen ¿ Allgemeine direkte Produkte und Datenstrukturen ¿ Mathematische Beweise ¿ Spezifikation und Programmverifikation ¿ Spezielle Funktionen ¿ Spezielle Relationen und gerichtete Graphen ¿ Elementare Kombinatorik und ungerichtete Graphen ¿ Diskrete Wahrscheinlichkeitstheorie ¿ Generische Programmierung ¿ Grundbegriffe algebraischer Strukturen ¿ Formale Einführung der natürlichen Zahlen - Grundbegriffe der linearen AlgebraMit diesem Buch gelingt der Einstieg ins Informatik-StudiumMit diesem Buch schaffen Sie eine solide Basis für die Mathematikausbildung im Rahmen desInformatikstudiums. Zudem sind Sie durch die vorgestellten Problemstellungen in der Lage, selbstständig mathematische Konzepte und Methoden anzuwenden. Zielgruppen dieses Buchs über Mathematik in der Informatik sind Bachelor-Studierende in den ersten Studiensemestern folgender Fachbereiche:InformatikMathematikIngenieurwissenschaften

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.

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.

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.

- A comprehensive overview of the various fields of application of data science and artificial intelligence.- Case studies from practice to make the described concepts tangible.- Practical examples to help you carry out simple data analysis projects.- BONUS in print edition: E-Book insideData Science, Big Data, Artificial Intelligence and Generative AI are currently some of the most talked-about concepts in industry, government, and society, and yet also the most misunderstood. This book will clarify these concepts and provide you with practical knowledge to apply them.Using exercises and real-world examples, it will show you how to apply data science methods, build data platforms, and deploy data- and ML-driven projects to production. It will help you understand - and explain to various stakeholders - how to generate value from such endeavors. Along the way, it will bring essential data science concepts to life, including statistics, mathematics, and machine learning fundamentals, and explore crucial topics like critical thinking, legal and ethical considerations, and building high-performing data teams.Readers of all levels of data familiarity - from aspiring data scientists to expert engineers to data leaders - will ultimately learn: how can an organization become more data-driven, what challenges might it face, and how can they as individuals help make that journey a success.The team of authors consists of data professionals from business and academia, including data scientists, engineers, business leaders and legal experts. All are members of the Vienna Data Science Group (VDSG), an NGO that aims to establish a platform for exchanging knowledge on the application of data science, AI and machine learning, and raising awareness of the opportunities and potential risks of these technologies.WHAT'S INSIDE //- Critical Thinking and Data Culture: How evidence driven decision making is the base for effective AI.- Machine Learning Fundamentals: Foundations of mathematics, statistics, and ML algorithms and architectures- Natural Language Processing and Computer Vision: How to extract valuable insights from text, images and video data, for real world applications.- Foundation Models and Generative AI: Understand the strengths and challenges of generative models for text, images, video, and more.- ML and AI in Production: Turning experimentation into a working data science product.- Presenting your Results: Essential presentation techniques for data scientists.