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Datafangst og dataanalyse

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  • - og få succes med Big Data
    af Per Østergaard Jacobsen, Pernille Rydén & Torsten Ringberg
    358,95 kr.

    Organisationer fokuserer i stigende grad på, hvordan de kan bruge Big Data strategisk. De investerer milliarder af kroner i denne ny teknologi, men værdien og afkastet viser sig tit at være begrænset. Sådanne udfordringer kan meget vel skyldes ledernes mindset, der begrænser hvordan organisationen bruger Big Data-teknologi til at styrke interaktionen og samspillet med kunder og andre interessenter.Det første skridt på vejen mod at få succes med Big Data er derfor at identificere og udfordre sit ledelsesmæssige mindset. I denne bog identificerer og diskuterer vi fire dominerende ledelsesmæssige mindset, der hver især fører til fire unikke Big Data strategier. Når ledere inden for samme team har forskellige mindset, kan det føre til manglende mental og organisatorisk alignment og problemer med at håndtere disruption. Da mindsets ofte er ubevidste, har vi udviklet en onlinetest, der hjælper dig og dine kolleger med at identificere jeres dominerende mindset. Det vil give jer et det bedste afsæt til at vurdere og beslutte hvad der er jeres optimale strategiske vej til Big Data succes.Omtale"Bogen giver en praktisk og teoretisk indførelse i big data. (…) Bogen har mange eksempler, som understøtter bogens budskaber."5/6 stjerner- anmeldt af Henrik Ørholst, Børsen, 1. februar 2018“Det er næsten provokerende, at sammenhængen mellem lederes mindset og succesfuld innovation kan være så markant. Læs bogen og få rystet rundt på dit eget mindset.”- Camilla Laudrup, leder af DRs public outreach sekretariat

  • - Teaching Machines to Paint, Write, Compose, and Play
    af David Foster
    632,95 kr.

    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

  • af Melissa Perri
    322,95 kr.

    Product Operations is the discipline of helping your Product Management function scale well. Surrounding the team with all of the essential inputs to set strategy, prioritize, and streamline ways of working.Many companies want to reap the benefits of economies of scale that comes with being a product-led company. As our businesses change shape to focus more on software, so do our ways of working. We need to make sure we're breaking down these silos of information and capabilities that arise at scale. To react quickly and set great Product Strategies, leaders and team members alike need access to high quality data and a process to implement their decisions.In this book, Melissa Perri and Denise Tilles explain how Product Operations helps solve many of scaling issues companies face today, like:Connecting financial metrics back to the delivery of software featuresBetter working relationships between go-to-market teams and software teamsGetting the right information to set and monitor company and product strategiesStreamlining the flow of user insights into the right teamsHow to set up a Product Management Operating ModelExecutives and leaders of SAAS scale up and enterprise companies will discover the benefits of the Three Pillars of Product Operations as they read through the stories of real companies who have implemented the function, like Stripe, Uber, athenahealth, Oscar Health, and Fidelity. This book covers the following topics:What is Product Operations and why is it an essential function in scaling software companiesThe Three Pillars of Product Operations: Data and Insights; Customer and Market Insights; and Process and GovernanceHow to Introduce and Build a Team for Product Operations

  • af Michael Law
    957,95 kr.

    Learn the latest version of ArcGIS Pro with the newest edition of this bestselling series. Getting to Know ArcGIS Pro 2.8 introduces the tools and functions of ArcGIS Pro, the powerful desktop GIS application. Geographic information system (GIS) software is making a huge impact in businesses and organizations with mapping and analytic capabilities.  Getting to Know ArcGIS Pro 2.8 uses practical project workflows to teach best practices to readers of all skill levels. Readers will explore data visualizations, 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 2.8 clarifies complicated processes such as developing a geoprocessing model, using Python to write a script tool, and creating space-time cubes for analysis. Throughout the book, short sidebars about real-world GIS scenarios in specific industries help readers understand how ArcGIS Pro can be applied widely to solve problems. At the end of each chapter, a summary and glossary help reinforce the skills learned.  This edition has been completely updated for use with ArcGIS Pro 2.8. Other updates include new chapters on ArcGIS Online and geocoding. The Getting to Know series has been teaching readers about GIS for more than 20 years. Ideal for students, self-learners, and professionals who want to learn the premier GIS desktop application, Getting to Know ArcGIS Pro 2.8 is a textbook and desk reference designed to show users how they can use ArcGIS Pro successfully on their own.

  • af Andy Petrella
    523,95 kr.

    Quickly detect, troubleshoot, and prevent a wide range of data issues through data observability, a set of best practices that enables data teams to gain greater visibility of data and its usage. If you're a data engineer, data architect, or machine learning engineer who depends on the quality of your data, this book shows you how to focus on the practical aspects of introducing data observability in your everyday work. Author Andy Petrella helps you build the right habits to identify and solve data issues, such as data drifts and poor quality, so you can stop their propagation in data applications, pipelines, and analytics. You'll learn ways to introduce data observability, including setting up a framework for generating and collecting all the information you need.

  • af Lauren F. Klein & Catherine D'Ignazio
    227,95 kr.

  • af Katharine Jarmul
    523,95 kr.

    Between major privacy regulations like the GDPR and CCPA and expensive and notorious data breaches, there has never been so much pressure to ensure data privacy. Unfortunately, integrating privacy into data systems is still complicated. This essential guide will give you a fundamental understanding of modern privacy building blocks, like differential privacy, federated learning, and encrypted computation. Based on hard-won lessons, this book provides solid advice and best practices for integrating breakthrough privacy-enhancing technologies into production systems. Practical Data Privacy answers important questions such as: What do privacy regulations like GDPR and CCPA mean for my data workflows and data science use cases? What does "anonymized data" really mean? How do I actually anonymize data? How does federated learning and analysis work? Homomorphic encryption sounds great, but is it ready for use? How do I compare and choose the best privacy-preserving technologies and methods? Are there open-source libraries that can help? How do I ensure that my data science projects are secure by default and private by design? How do I work with governance and infosec teams to implement internal policies appropriately?

  • af Alexander Loth
    225,95 kr.

  • - Import, Tidy, Transform, Visualize, and Model Data
    af Hadley Wickham & Garrett Grolemund
    632,95 kr.

    Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible.Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. Youll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what youve learned along the way.Youll learn how to:Wrangletransform your datasets into a form convenient for analysisProgramlearn powerful R tools for solving data problems with greater clarity and easeExploreexamine your data, generate hypotheses, and quickly test themModelprovide a low-dimensional summary that captures true "e;signals"e; in your datasetCommunicatelearn R Markdown for integrating prose, code, and results

  • af Jonas Christensen
    483,95 kr.

    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

  • af Dmitry Foshin
    541,95 kr.

    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.

  • af Gus Frazer
    369,95 kr.

    Unlock the full potential of your data by mastering the art of cleaning, preparing, and transforming data with Power BI for smarter insights and data visualizationsKey Features:Implement best practices for connecting, preparing, cleaning, and analyzing multiple sources of data using Power BIConduct exploratory data analysis (EDA) using DAX, PowerQuery, and the M languageApply your newfound knowledge to tackle common data challenges for visualizations in Power BIPurchase of the print or Kindle book includes a free PDF eBookBook Description:Microsoft Power BI offers a range of powerful data cleaning and preparation options through tools such as DAX, Power Query, and the M language. However, despite its user-friendly interface, mastering it can be challenging. Whether you're a seasoned analyst or a novice exploring the potential of Power BI, this comprehensive guide equips you with techniques to transform raw data into a reliable foundation for insightful analysis and visualization.This book serves as a comprehensive guide to data cleaning, starting with data quality, common data challenges, and best practices for handling data. You'll learn how to import and clean data with Query Editor and transform data using the M query language. As you advance, you'll explore Power BI's data modeling capabilities for efficient cleaning and establishing relationships. Later chapters cover best practices for using Power Automate for data cleaning and task automation. Finally, you'll discover how OpenAI and ChatGPT can make data cleaning in Power BI easier.By the end of the book, you will have a comprehensive understanding of data cleaning concepts, techniques, and how to use Power BI and its tools for effective data preparation.What You Will Learn:Connect to data sources using both import and DirectQuery optionsUse the Query Editor to apply data transformationsTransform your data using the M query languageDesign clean and optimized data models by creating relationships and DAX calculationsPerform exploratory data analysis using Power BIAddress the most common data challenges with best practicesExplore the benefits of using OpenAI, ChatGPT, and Microsoft Copilot for simplifying data cleaningWho this book is for:If you're a data analyst, business intelligence professional, business analyst, data scientist, or anyone who works with data on a regular basis, this book is for you. It's a useful resource for anyone who wants to gain a deeper understanding of data quality issues and best practices for data cleaning in Power BI. If you have a basic knowledge of BI tools and concepts, this book will help you advance your skills in Power BI.

  • af Pedro Lopes
    406,95 kr.

    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.

  • af Leondra R. Gonzalez
    316,95 kr.

    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

  • af Pratheerth Padman
    402,95 kr.

    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.

  • af L. D. Knowings
    193,95 - 262,95 kr.

  • af Krishna Shah
    387,95 kr.

    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

  • af Michael Olafusi
    432,95 kr.

    Unleash the full potential of Microsoft Excel's latest version and elevate your data-driven prowess with this comprehensive resourceKey Features- Create robust and automated dashboards in Excel for M365- Apply data visualization principles and employ dynamic charts and tables to create constantly updated and informative dashboards for your organization- Uncover the best practices for effective dashboard creation- Purchase of the print or Kindle book includes a free PDF eBookBook DescriptionM365 Excel is a modern Excel version that is constantly updated with features that make creating and automating analyses, reports, and dashboards very easy compared with older Excel versions. This book will help you leverage its full capabilities, beginning with a quick overview of what dashboards are and how they are different from other types of reports. Then, you'll familiarize yourself with the different standard dashboards currently available and what they are meant to accomplish for organizations. As you progress, you'll get to grips with the use of new powerful tools such as Power Query and dynamic array formulae in the automation of analysis, gaining insights into the right approach to take in building effective dashboards. You'll equip yourself with not only all the essential formulae, charts, and non-chart visuals but also learn how to set up your dashboard perfectly. Along the way, you'll build a couple of awesome dashboards from scratch to utilize your newfound knowledge.By the end of this book, you will be able to carry out an impressive and robust level of analysis on business data that may come from multiple sources or files, using better processes, formulae, and best practices in M365 to create insightful dashboards faster.What you will learn- Understand the importance of dashboards in today's business analytics environment- Delve into the various essential formulae in Excel- Utilize Power Query to shape and transform data to extract insights easily- Explore the power of the new dynamic array functions in M365- Employ PivotTable and Power Pivot to automate your dashboards- Master the setup and optimization of your dashboard canvas- Discover best practices for visualization, charts, and effective dashboard creation- Consolidate your knowledge through a hands-on concluding projectWho this book is forThis book is for Microsoft Excel users, especially those tasked with creating dynamic reports and dashboards that require data and help support to decision-makers within an organization with visually engaging and actionable insights. Financial analysts, data analysts, business analysts, and BI professionals will also greatly benefit from this book. Some familiarity with the Microsoft Excel interface is a prerequisite.Table of Contents- Dashboards, Reports, and M365 Excel- Common Dashboards in Large Companies- The Importance of Connecting Directly to the Primary Data Sources- Power Query: the Ultimate Data Transformation Tool- PivotTable and Power Pivot- Must-Know Legacy Excel Functions- Dynamic Array Functions and Lambda Functions- Getting Comfortable with the 19 Excel Charts- Non-Chart Visuals- Setting Up the Dashboard's Data Model- Perfecting the Dashboard- Best Practices for Real-World Dashboard Building

  • af Adeyemi Abel Ajibesin
    2.152,95 kr.

    In today's highly competitive and rapidly evolving global landscape, the quest for efficiency has become a crucial factor in determining the success of organizations across various industries. Data Envelopment Analysis (DEA) Methods for Maximizing Efficiency is a comprehensive guide that delves into the powerful mathematical tool of DEA, is designed to assess the relative efficiency of decision-making units (DMUs), and provides valuable insights for performance improvement. This book presents a systematic overview of DEA models and techniques, from fundamental concepts to advanced methods, showcasing their practical applications through real-world examples and case studies. Catering to a broad audience, this book is designed for students, researchers, consultants, decision-makers, and enthusiasts in the field of efficiency analysis and performance measurement. Consultants and practitioners will gain practical insights for applying DEA in various contexts, and decision-makers will be equipped to make informed decisions for maximizing efficiency. Additionally, individuals with a general interest in data analysis and performance measurement will find this book accessible and informative. This book covers a wide range of topics, including mathematical foundations of DEA, DEA models and variations, DEA efficiency and productivity measures, DEA applications in various industries such as healthcare, finance, supply chain management, environmental management, education management, and public sector management.

  • af Harinder Singh
    352,95 kr.

    The development in medical field leads to produce massive amount of medical data. In 2002, more than 12000 images a day were produced by the Department of Radiology of a hospital in Geneva. The medical datasets are available for further exploration and research which have far reaching impact on the progress and execution of health programs. The information archived from exploring medical datasets paves the way for health administration, e-health diagnosis and therapy. So, there is urgent need to accentuate the research in medical data. The medical data is a huge growing industry and its size normally lies in terabytes. Such a big data puts forward many challenges and issues due to its large volume, variety, velocity, value and variability. Moreover, the working of traditional file management systems is slowing down due to its incapability of managing unstructured, variable and complex big data. The managing of such big data is very cumbersome and time-consuming task which requires new computing techniques. So, the exponential growth of medical data has necessitated a paradigm shift in the way the data is managed and processed. The recent technological advancements influenced the way of storing and processing big data. This motivated us to think about finding new solutions for managing volumetric medical datasets and to obtain valuable information efficiently. Hadoop is a top-level Apache project and is written in Java. Hadoop was developed by Doug Cutting as a collection of open-source projects. It is presently used on massive amount of unstructured data. With Hadoop, the data can be harnessed that was previously difficult to analyze. Hadoop has the ability to process extremely large data with changing structure. Hadoop is composed of different modules like HBase, Pig, HCatalog, Hive, Zookeeper, Oozie and Kafka, but the most common paradigms for big data are Hadoop Distributed File sSystem (HDFS) and MapReduce.

  • af Rob Botwright
    352,95 kr.

    Unlock the Full Potential of Your Data with the "Power BI Data Mastery Made Easy" Book Bundle!Are you ready to transform your data into actionable insights and make informed decisions that drive success? Look no further! Introducing the "Power BI Data Mastery Made Easy" book bundle, a comprehensive collection of resources that will empower you to harness the true power of Microsoft's leading business intelligence and data visualization tool-Power BI.Here's what you'll discover in this incredible bundle:Book 1 - Power BI Essentials: A Beginner's Guide to Data Visualization Mastery· Ideal for beginners: Build a solid foundation in data visualization.· Learn to import and transform data from various sources.· Master the art of data analysis and visualization.Book 2 - Mastering Power BI: Advanced Techniques and Best Practices for Analysts· Elevate your skills to the next level with advanced techniques.· Discover best practices for tackling complex analytical challenges.· Master DAX formulas and optimize data models.· Become an analytics expert and excel in your field.Book 3 - Power BI Data Modeling: Building Robust Datasets for Effective Analysis· Unlock the full potential of Power BI with robust data modeling.· Design efficient and flexible data models.· Gain the skills to create powerful data sets for effective analysis.Book 4 - Expert Power BI: Advanced Analytics and Custom Visualizations Mastery· Dive into the world of advanced analytics and custom visuals.· Explore machine learning integration and geographic analysis.· Become a Power BI expert and stand out in your field.Grab the "Power BI Data Mastery Made Easy" book bundle today and start your exciting adventure into the world of Power BI-where data mastery is within reach for everyone!

  • af Pinde Fu
    523,95 kr.

    Getting to Know Mobile GIS empowers readers to creatively use mobile GIS and drive innovation in this fast-advancing field."Mobile-first" and the "post-PC era" aren't just buzzwords; they represent today's reality. Within the GIS industry, the mobile internet and mobile GIS have profoundly transformed how we acquire, visualize, analyze, and disseminate geospatial information. With its ubiquitous use and location-aware capabilities, the mobile platform is an essential part of enterprise GIS across industries, making mobile GIS crucial to the business operations and future success of wide-ranging organizations.Getting to Know Mobile GIS helps GIS professionals and GIS students grasp the opportunities presented by mobile GIS. It provides principles and detailed tutorials to teach today's cutting-edge mobile GIS technologies. Using a no-code, low-code approach, it makes mobile GIS easy and fun to learn. Readers can immediately become productive in creating enterprise GIS solutions, ranging from online and offline spatial data visualization, data collection, location sharing, and workforce coordination to integration with enterprise workflows.Pinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities, including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won special achievement awards. Fu is the author of Getting to Know Web GIS (Esri Press, 2022) and lead author of Web GIS: Principles and Applications (Esri Press, 2010).

  • af Raul Sidnei Wazlawick
    680,95 kr.

    Object-Oriented Analysis and Design for Information Systems, Second Edition clearly explains real object-oriented programming in practice. Expert author Raul Sidnei Wazlawick explains concepts such as object responsibility, visibility, and the real need for delegation in detail. The object-oriented code generated by using these concepts in a systematic way is concise, organized and reusable.The patterns and solutions presented in this book are based in research and industrial applications. You will come away with clarity regarding processes and use cases and a clear understanding of how to expand a use case. Wazlawick clearly explains how to build meaningful sequence diagrams. Object-Oriented Analysis and Design for Information Systems illustrates how and why building a class model is not just placing classes into a diagram. You will learn the necessary organizational patterns so that your software architecture will be maintainable. The Second Edition includes all new content shifting the focus of the book to agile software development, including Scrum software project management, BPMN diagrams, user stories, and Python code examples.

  • af Mahak
    337,95 kr.

    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.

  • af Jessica Turner
    217,95 kr.

    Data has become the key to innovation andadvancement in the ever-changing digital world.Every click, purchase, and interaction produces aplethora of data, and extracting valuable insightsfrom this enormous ocean of information has evolvedinto a game-changing ability. Here at "InsightsUnveiled: A Practical Guide to Data Analytics," we'll betaking a close look at the methods, strategies, andconcepts that enable people and businesses to usedata to make well- informed decisions.The amount and prevalence of data in our day and age are unparalleled. The convergence of technology, networking, and digitalization has brought about a data revolution. This has created both opportunities and challenges. Acknowledgingthe revolutionary power of data, "Insights Unveiled" acts as a compass, pointing readers toward the nuances of data analytics.The goal of equipping decision-makers with the knowledge and abilities to navigate the complicated data world successfully is at the core of this book. The subtitle, "Harnessing Data for Informed Decision-Making," aims to provide people with the hands-on skills necessary to convert unstructured data into strategic insights. This guide is intended to be your all-in-one resource whether you are a company professional looking to optimize operations, a student just starting in the field of analytics, or an interested enthusiast ready to dive into data-driven decision-making."Insights Unveiled" approaches data analytics holistically, acknowledging that theoretical knowledge is inadequate. Readers are taken on a journey via each chapter that blends practicalapplication with fundamental understanding.Starting from the foundation, we examine the manykinds of data, the essential instruments, and the basicideas that make up analytics. Next, we get down tobusiness, walking readers through assembling areliable analytics environment, preparing andcleaning data, and doing exploratory data analysis(EDA).

  • af Pui Shing Lee
    542,95 kr.

    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

  • af Sinan Ozdemir
    542,95 kr.

    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

  • af Jugnesh Kumar
    381,95 kr.

    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.

    1.512,95 kr.

    This book discusses and addresses anomaly detection in the context of artificial intelligence and machine learning advancements. Building on the existing literature, this thorough and timely work is an invaluable resource. It highlights various problems, offers workable solutions to those problems, and allows academic and professional researchers and practitioners to engage in new technologies linked to anomaly detection. This book demystifies the challenges and presents solutions for detecting and understanding network anomalies. Whether you are a seasoned network professional or an enthusiast keen on cyber security, this volume promises insights that will fortify our connected futures. Join us in navigating the complexities of modern networks and championing a safer, more transparent digital era.

  • af Yukari Iwatani Kane
    297,95 kr.

    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.

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