Datafangst og dataanalyse

Data is the gold of the 21st century. Buzzwords like "data culture" or "data-driven company" are on everyone's lips.Instead of dry theory, "Data for the Tiger" conveys the path to a data-driven company in the form of an entertaining fable with wild animals in a jungle.A lively story for both data specialists and data consumers who want to get more value out of their data.Are you ready for the most extraordinary textbook the data world has ever seen?

Baseball is not the only sport to use "moneyball." American football teams, fantasy football players, fans, and gamblers are increasingly using data to gain an edge on the competition. Professional and college teams use data to help identify team needs and select players to fill those needs. Fantasy football players and fans use data to try to defeat their friends, while sports bettors use data in an attempt to defeat the sportsbooks. In this concise book, Eric Eager and Richard Erickson provide a clear introduction to using statistical models to analyze football data using both Python and R. Whether your goal is to qualify for an entry-level football analyst position, dominate your fantasy football league, or simply learn R and Python with fun example cases, this book is your starting place. Through case studies in both Python and R, you'll learn to: Obtain NFL data from Python and R packages and web scraping Visualize and explore data Apply regression models to play-by-play data Extend regression models to classification problems in football Apply data science to sports betting with individual player props Understand player athletic attributes using multivariate statistics

The surging predictive analytics market is expected to grow from $10.5 billion today to $28 billion by 2026. With the rise in automation across industries, the increase in data-driven decision-making, and the proliferation of IoT devices, predictive analytics has become an operational necessity in today's forward-thinking companies. > Explore ways that predictive analytics can provide direct input back to your business Understand mathematical tools commonly used in predictive analytics Learn the development frameworks used in predictive analytics applications Appreciate the role of predictive analytics in the machine learning process Examine industry implementations of predictive analytics Build, train, and retrain predictive models using Python and TensorFlow

The identification and interpretation of adverse event root cause is a critical function supporting the development of appropriate corrective actions in high-risk industries. Aviation Human Factors (HF) professionals are interested in identifying events caused by human error in aircraft and engine assembly and maintenance to develop solutions to systemic issues. Current event classification methods are heavily dependent on manual review of report narratives, which presents an opportunity to explore automated techniques using data science (DS), rule-based classification, and machine learning (ML). In this study, automated classification models were developed, combined, and compared, using multiple event report fields as model inputs. Based on the determination that event narratives are the most valuable source of root cause information, natural language processing (NLP) and feature engineering methods were explored to identify patterns in human language associated with error causal factors.

Craft ethical AI projects with privacy, fairness, and risk assessment features for scalable and distributed systems while maintaining explainability and sustainabilityPurchase of the print or Kindle book includes a free PDF eBookKey Features:Learn risk assessment for machine learning frameworks in a global landscapeDiscover patterns for next-generation AI ecosystems for successful product designMake explainable predictions for privacy and fairness-enabled ML trainingBook Description:AI algorithms are ubiquitous and used for tasks, from recruiting to deciding who will get a loan. With such widespread use of AI in the decision-making process, it's necessary to build an explainable, responsible, transparent, and trustworthy AI-enabled system. With Platform and Model Design for Responsible AI, you'll be able to make existing black box models transparent.You'll be able to identify and eliminate bias in your models, deal with uncertainty arising from both data and model limitations, and provide a responsible AI solution. You'll start by designing ethical models for traditional and deep learning ML models, as well as deploying them in a sustainable production setup. After that, you'll learn how to set up data pipelines, validate datasets, and set up component microservices in a secure and private way in any cloud-agnostic framework. You'll then build a fair and private ML model with proper constraints, tune the hyperparameters, and evaluate the model metrics.By the end of this book, you'll know the best practices to comply with data privacy and ethics laws, in addition to the techniques needed for data anonymization. You'll be able to develop models with explainability, store them in feature stores, and handle uncertainty in model predictions.What You Will Learn:Understand the threats and risks involved in ML modelsDiscover varying levels of risk mitigation strategies and risk tiering toolsApply traditional and deep learning optimization techniques efficientlyBuild auditable and interpretable ML models and feature storesUnderstand the concept of uncertainty and explore model explainability toolsDevelop models for different clouds including AWS, Azure, and GCPExplore ML orchestration tools such as Kubeflow and Vertex AIIncorporate privacy and fairness in ML models from design to deploymentWho this book is for:This book is for experienced machine learning professionals looking to understand the risks and leakages of ML models and frameworks, and learn to develop and use reusable components to reduce effort and cost in setting up and maintaining the AI ecosystem.

Empower yourself by desgining interactive dashboards in Tableau.This book comes with downloadable code files and colored images.Key Features:In-depth end-to-end knowledge on Tableau Desktop150+ step-by-step exercises to follow alongCover data load, relationships, joins, and visualization techniquesDives deep into Calculations and FunctionsIncludes new features of Tableau 2023.1Step-by-Step guide to learning TableauDashboarding with Tableau covers how to load data and create visualizations.The book includes all the concepts and provides step-by-step exercises to practice.The book begins with basic concepts of Tableau and how Tableau assists in Business Intelligence. It takes deep dive into loading, transforming, and combining data. Readers will also learn about different types of calculations in Tableau.The book illustrates how to create different types of visualizations and assimilate them into an interactive dashboard. The book starts with basics and steers the reader to advanced concepts.Who this book forThis book is a must-have for aspiring Tableau developers, BI analysts, Data explorers, and other data enthusiasts who wants to acquire deeper data insights. No prior knowledge of Tableau or advanced IT concepts are requried for this book. A basic familiarity with MS-Excel will be helpful.Table of contentsGetting StartedThe Data PaneData TransformationCombining DataCalculations in TableauTable and LOD CalculationsDate FunctionsFilters and ParametersSortingGroups,Sets and BinsMapsVisualization Part - 1Visualization Part - 2Dashboards

Create and improve fully automated forecasts for time series data with strong seasonal effects, holidays, and additional regressors using PythonPurchase of the print or Kindle book includes a free PDF eBookKey Features:Explore Prophet, the open source forecasting tool developed at Meta, to improve your forecastsCreate a forecast and run diagnostics to understand forecast qualityFine-tune models to achieve high performance and report this performance with concrete statisticsBook Description:Forecasting Time Series Data with Prophet will help you to implement Prophet's cutting-edge forecasting techniques to model future data with high accuracy using only a few lines of code. This second edition has been fully revised with every update to the Prophet package since the first edition was published two years ago. An entirely new chapter is also included, diving into the mathematical equations behind Prophet's models. Additionally, the book contains new sections on forecasting during shocks such as COVID, creating custom trend modes from scratch, and a discussion of recent developments in the open-source forecasting community.You'll cover advanced features such as visualizing forecasts, adding holidays and trend changepoints, and handling outliers. You'll use the Fourier series to model seasonality, learn how to choose between an additive and multiplicative model, and understand when to modify each model parameter. Later, you'll see how to optimize more complicated models with hyperparameter tuning and by adding additional regressors to the model. Finally, you'll learn how to run diagnostics to evaluate the performance of your models in production.By the end of this book, you'll be able to take a raw time series dataset and build advanced and accurate forecasting models with concise, understandable, and repeatable code.What You Will Learn:Understand the mathematics behind Prophet's modelsBuild practical forecasting models from real datasets using PythonUnderstand the different modes of growth that time series often exhibitDiscover how to identify and deal with outliers in time series dataFind out how to control uncertainty intervals to provide percent confidence in your forecastsProductionalize your Prophet models to scale your work faster and more efficientlyWho this book is for:This book is for business managers, data scientists, data analysts, machine learning engineers, and software engineers who want to build time-series forecasts in Python or R. To get the most out of this book, you should have a basic understanding of time series data and be able to differentiate it from other types of data. Basic knowledge of forecasting techniques is a plus.

This is an important and timely book. Students of organizational behavior for the last 15 years have been asking how to integrate the technology of data gathering and data analysis with critical organizational challenges. This book shows how to do that, using the field of customer service to illustrate the broader point. This volume allows lay readers to understand telemetry and helps them enhance their data-gathering activities to strengthen customer relations.Author of The Agenda Mover: When Your Good Idea Is Not Enough and Transforming the Clunky Organization: Pragmatic Skills for Breaking Inertia (Cornell University Press)Telemetry is an automated way of collecting data at remote sites or locations, and transmitting it to collectors at receiving site for monitoring, analyzing, and driving improvement actions. This book provides the necessary knowledge and information to understand the telemetry infrastructure and associated details. It will enable readers to implement a telemetry program to address customer experience pain and improve customer experience.The authors of this book have all served in different roles and capacities in one of Silicon Valley's premier technology companies. These roles include software engineering, customer assurance, quality management, technology development, and implementation. Their paths intersected in the area of quality management, and they have witnessed first-hand how the latest technology/market transitions around Internet of Things (IoT), digitization, and telemetry are impacting the company they work, as well as the high-tech industry and global economy as a whole.The real-time nature of data and the advent of machine-learning algorithms have set the stage for a new era that the authors call adaptive customer experience. The premise of this concept is that real-time availability of customer experience data opens the door for real-time responses based on machine-learning algorithms. This creates an unprecedented opportunity to change the relationship between customers and the systems they depend on in their digital world. The proliferation of sensors and improvements in data science capabilities are creating an environment where the possibilities for telemetry are limitless. The book provides several examples of use cases and applications that help bring telemetry to life.

Object Lessons is a series of short, beautifully designed books about the hidden lives of ordinary things.Barcodes are about as ordinary as an object can be. Billions of them are scanned each day and they impact everything from how we shop to how we travel to how the global economy is managed. But few people likely give them more than a second thought. In a way, the barcode's ordinariness is the ultimate symbol of its success.However, behind the mundanity of the barcode lies an important history. Barcodes bridged the gap between physical objects and digital databases and paved the way for the contemporary Internet of Things, the idea to connect all devices to the web. They were highly controversial at points, protested by consumer groups and labor unions, and used as a symbol of dystopian capitalism and surveillance in science fiction and art installations. This book tells the story of the barcode's complicated history and examines how an object so crucial to so many parts of our lives became more ignored and more ordinary as it spread throughout the world.Object Lessons is published in partnership with an essay series in The Atlantic.

This bestseller can help anyone whose role is to try to find specific causes for failures.It provides detailed steps for solving problems, focusing more heavily on the analytical process involved in finding the actual causes of problems. It does this using figures, diagrams, and tools useful for helping to make our thinking visible. This increases our ability to see what is truly significant and to better identify errors in our thinking. In the sections on finding root causes, this second edition now includes more examples on the use of multi-vari charts; how thought experiments can help guide data interpretation; how to enhance the value of the data collection process; cautions for analyzing data; and what to do if one can't find the causes. In its guidance on solution identification, biomimicry and TRIZ have been added as potential solution identification techniques. In addition, the appendices have been revised to include: an expanded breakdown of the 7 M's, which includes more than 50 specific possible causes; forms for tracking causes and solutions, which can help maintain alignment of actions; techniques for how to enhance the interview process; and example responses to problem situations that the reader can analyze for appropriateness.

More organizations and their leaders are looking to big data to transform processes and elevate the quality of products and services. Yet, gathering and storing large amounts of data isn't the quick fix often sought after. Without analysts-the human component-to interpret that data, the cost of incorrect or misinterpreted data can greatly impact organizations.In this book, William Mawby examines the claims of big data analysis in detail. Using examples to illustrate potential problems that may lead to inefficient and inaccurate results, Mawby helps practitioners avoid potential pitfalls and offers application methods to incorporate big data analytics into your company that will enhance your analytic efforts.William D. Mawby, Ph.D. has extensive consulting, teaching, and project experience and has taught more than 200 courses on many subjects in statistics and mathematics. He is currently writing, teaching courses on climate change and big data, and volunteering at the American Association for the Advancement of Science and the Union of Concerned Scientists.

Good data is a source of myriad opportunities, while bad data is a tremendous burden. Companies that manage their data effectively are able to achieve a competitive advantage in the marketplace, while bad data, like cancer, can weaken and kill an organization.In this comprehensive book, Rupa Mahanti provides guidance on the different aspects of data quality with the aim to be able to improve data quality. Specifically, the book addresses: Causes of bad data quality, bad data quality impacts, and importance of data quality to justify the case for data quality Butterfly effect of data quality A detailed description of data quality dimensions and their measurement Data quality strategy approach Six Sigma - DMAIC approach to data quality Data quality management techniques Data quality in relation to data initiatives like data migration, MDM, data governance, etc. Data quality myths, challenges, and critical success factorsStudents, academicians, professionals, and researchers can all use the content in this book to further their knowledge and get guidance on their own specific projects. It balances technical details (for example, SQL statements, relational database components, data quality dimensions measurements) and higher-level qualitative discussions (cost of data quality, data quality strategy, data quality maturity, the case made for data quality, and so on) with case studies, illustrations, and real-world examples throughout.About the AuthorRupa Mahanti, Ph.D. is a Business and Information Management consultant and has worked in different solution environments and industry sectors in the United States, United Kingdom, India, and Australia. She helps clients with activities such as business process mapping, information management, data quality, and strategy. Having a work experience (academic, industry, and research) of more than a decade and half, Rupa has guided a doctoral dissertation and published a large number of research articles. She is an associate editor with the journal Software Quality Professional and a reviewer for several international journals."This is not the kind of book that you'll read one time and be done with. So scan it quickly the first time through to get an idea of its breadth. Then dig in on one topic of special importance to your work. Finally, use it as a reference to guide your next steps, learn details, and broaden your perspective."from the foreword by Thomas C. Redman, Ph.D., the Data DocDr. Mahanti provides a very detailed and thorough coverage of all aspects of data quality management that would suit all ranges of expertise from a beginner to an advanced practitioner. With plenty of examples, diagrams, etc. the book is easy to follow and will deepen your knowledge in the data domain. I will certainly keep this handy as my go-to reference. I can't imagine the level of effort and passion that Dr. Mahanti has put into this book that captures so much knowledge and experience for the benefit of the reader. I would highly recommend this book for its comprehensiveness, depth, and detail. A must-have for a data practitioner at any level.Clint D'Souza, CEO and Director, CDZM Consulting

This applied research project provides the Security, Suitability, and Credentialing (SSC) Performance Accountability Council Program Management Office (PAC PMO) with an examination of how U.S. government vetting processes and procedures could be enhanced by application of mobile technologies and platforms. The research team identified key mobile platform and security factors to consider when communicating with and screening candidates within the SSC process. This report also describes relevant private-sector practices on talent acquisition, applicant tracking systems, screening methods, and communication strategies with candidates prior to the onboarding process. The RAND research team performed the following tasks for the PAC PMO: (1) categorize emerging mobile technology platforms according to SSC process relevance and ability to assist government vetting of personnel; (2) illustrate relevant practices and lessons learned for integrating security applications with mobile platforms; and (3) provide recommendations on how best to incorporate potentially useful private-sector screening practices to create efficiency within initial stages of the SSC process.

Implement real-world machine learning in a microservices architecture as well as design, build, and deploy intelligent microservices systems using examples and case studiesPurchase of the print or Kindle book includes a free PDF eBookKey Features:Design, build, and run microservices systems that utilize the full potential of machine learningDiscover the latest models and techniques for combining microservices and machine learning to create scalable systemsImplement machine learning in microservices architecture using open source applications with pros and consBook Description:With the rising need for agile development and very short time-to-market system deployments, incorporating machine learning algorithms into decoupled fine-grained microservices systems provides the perfect technology mix for modern systems. Machine Learning in Microservices is your essential guide to staying ahead of the curve in this ever-evolving world of technology.The book starts by introducing you to the concept of machine learning microservices architecture (MSA) and comparing MSA with service-based and event-driven architectures, along with how to transition into MSA. Next, you'll learn about the different approaches to building MSA and find out how to overcome common practical challenges faced in MSA design. As you advance, you'll get to grips with machine learning (ML) concepts and see how they can help better design and run MSA systems. Finally, the book will take you through practical examples and open source applications that will help you build and run highly efficient, agile microservices systems.By the end of this microservices book, you'll have a clear idea of different models of microservices architecture and machine learning and be able to combine both technologies to deliver a flexible and highly scalable enterprise system.What You Will Learn:Recognize the importance of MSA and ML and deploy both technologies in enterprise systemsExplore MSA enterprise systems and their general practical challengesDiscover how to design and develop microservices architectureUnderstand the different AI algorithms, types, and models and how they can be applied to MSAIdentify and overcome common MSA deployment challenges using AI and ML algorithmsExplore general open source and commercial tools commonly used in MSA enterprise systemsWho this book is for:This book is for machine learning solution architects, system and machine learning developers, and system and solution integrators of private and public sector organizations. Basic knowledge of DevOps, system architecture, and artificial intelligence (AI) systems is assumed, and working knowledge of the Python programming language is highly desired.

Build your own robo-advisor in Python to manage your investments and get up and running in no timePurchase of the print or Kindle book includes a free PDF eBookKey Features:Explore the use cases, workflow, and features that make up robo-advisorsLearn how to build core robo-advisor capabilities for goals, risk questions, portfolios, and projectionsDiscover how to operate the automated processes of a built and deployed robo-advisorBook Description:Robo-advisors are becoming table stakes for the wealth management industry across all segments, from retail to high-net-worth investors. Robo-advisors enable you to manage your own portfolios and financial institutions to create automated platforms for effective digital wealth management. This book is your hands-on guide to understanding how Robo-advisors work, and how to build one efficiently. The chapters are designed in a way to help you get a comprehensive grasp of what Robo-advisors do and how they are structured with an end-to-end workflow.You'll begin by learning about the key decisions that influence the building of a Robo-advisor, along with considerations on building and licensing a platform. As you advance, you'll find out how to build all the core capabilities of a Robo-advisor using Python, including goals, risk questionnaires, portfolios, and projections. The book also shows you how to create orders, as well as open accounts and perform KYC verification for transacting. Finally, you'll be able to implement capabilities such as performance reporting and rebalancing for operating a Robo-advisor with ease.By the end of this book, you'll have gained a solid understanding of how Robo-advisors work and be well on your way to building one for yourself or your business.What You Will Learn:Explore what Robo-advisors do and why they existCreate a workflow to design and build a Robo-advisor from the bottom upBuild and license Robo-advisors using different approachesOpen and fund accounts, complete KYC verification, and manage ordersBuild Robo-advisor features for goals, projections, portfolios, and moreOperate a Robo-advisor with P&L, rebalancing, and fee managementWho this book is for:If you are a finance professional or a data professional working in wealth management and are curious about how robo-advisors work, this book is for you. It will be helpful to have a basic understanding of Python and investing concepts. This is a great handbook for developers interested in building their own robo-advisor to manage personal investments or build a platform for their business to operate, as well as for product managers and business leaders in financial services looking to lease, buy, or build a robo-advisor.

Discover the use of graph networks to develop a new approach to data science using theoretical and practical methods with this expert guide using Python, printed in colorKey Features:Create networks using data points and informationLearn to visualize and analyze networks to better understand communitiesExplore the use of network data in both - supervised and unsupervised machine learning projectsPurchase of the print or Kindle book includes a free PDF eBookBook Description:Network analysis is often taught with tiny or toy data sets, leaving you with a limited scope of learning and practical usage. Network Science with Python helps you extract relevant data, draw conclusions and build networks using industry-standard - practical data sets.You'll begin by learning the basics of natural language processing, network science, and social network analysis, then move on to programmatically building and analyzing networks. You'll get a hands-on understanding of the data source, data extraction, interaction with it, and drawing insights from it. This is a hands-on book with theory grounding, specific technical, and mathematical details for future reference.As you progress, you'll learn to construct and clean networks, conduct network analysis, egocentric network analysis, community detection, and use network data with machine learning. You'll also explore network analysis concepts, from basics to an advanced level.By the end of the book, you'll be able to identify network data and use it to extract unconventional insights to comprehend the complex world around you.What You Will Learn:Explore NLP, network science, and social network analysisApply the tech stack used for NLP, network science, and analysisExtract insights from NLP and network dataGenerate personalized NLP and network projectsAuthenticate and scrape tweets, connections, the web, anddata streamsDiscover the use of network data in machine learning projectsWho this book is for:Network Science with Python demonstrates how programming and social science can be combined to find new insights. Data scientists, NLP engineers, software engineers, social scientists, and data science students will find this book useful. An intermediate level of Python programming is a prerequisite. Readers from both - social science and programming backgrounds will find a new perspective and add a feather to their hat.

Are you suffering from Data Presentation Zombification?Billions of dollars and thousands of hours are lost every year during ineffective business meetings worldwide. Data practitioners painstakingly present their valuable analytical insights, only to fall flat, inspiring more yawns than yeses.In Present Beyond Measure: Design, Visualize, and Deliver Data Stories That Inspire Action, Lea Pica provides a 4-phase, step-by-step blueprint for planning, designing, visualizing, and delivering compelling data storytelling in business presentations. Following her blueprint, you will learn how to use neuroscience and cinematic storytelling techniques to galvanize your stakeholders into action.By the final page, you'll know exactly how to:* Choose the data that matters most to your decision-makers* Speak to different stakeholder audience personality types (even the most challenging)* Infuse your data presentation with a persuasive narrative storyline* Craft strategic recommendations that get approved and implemented* Design simple, stunning slides that communicate without confusing* Transmit your data story with best-practice data visualization techniques* Avoid the most common data visualization violations and charting pitfalls* Prepare for and deliver your presentation like a professional speaker* Navigate challenging meeting conversations and logistics with easeWhether you work with little or big data, this book will show you how to prevent presentation zombies and inspire the action and credibility you and your organization deserve.

A hands-on guide to working on use cases helping you ingest, analyze, and serve insightful data from IoT as well as telemetry data sources using Azure Synapse Data ExplorerFree PDF included with this bookKey Features:Augment advanced analytics projects with your IoT and application dataExpand your existing Azure Synapse environments with unstructured dataBuild industry-level projects on integration, experimentation, and dashboarding with Azure SynapseBook Description:Large volumes of data are generated daily from applications, websites, IoT devices, and other free-text, semi-structured data sources. Azure Synapse Data Explorer helps you collect, store, and analyze such data, and work with other analytical engines, such as Apache Spark, to develop advanced data science projects and maximize the value you extract from data.This book offers a comprehensive view of Azure Synapse Data Explorer, exploring not only the core scenarios of Data Explorer but also how it integrates within Azure Synapse. From data ingestion to data visualization and advanced analytics, you'll learn to take an end-to-end approach to maximize the value of unstructured data and drive powerful insights using data science capabilities. With real-world usage scenarios, you'll discover how to identify key projects where Azure Synapse Data Explorer can help you achieve your business goals. Throughout the chapters, you'll also find out how to manage big data as part of a software as a service (SaaS) platform, as well as tune, secure, and serve data to end users.By the end of this book, you'll have mastered the big data life cycle and you'll be able to implement advanced analytical scenarios from raw telemetry and log data.What You Will Learn:Integrate Data Explorer pools with all other Azure Synapse servicesCreate Data Explorer pools with Azure Synapse Studio and Azure PortalIngest, analyze, and serve data to users using Azure Synapse pipelinesIntegrate Power BI and visualize data with Synapse StudioConfigure Azure Machine Learning integration in Azure SynapseManage cost and troubleshoot Data Explorer pools in Synapse AnalyticsSecure Synapse workspaces and grant access to Data Explorer poolsWho this book is for:If you are a data engineer, data analyst, or business analyst working with unstructured data and looking to learn how to maximize the value of such data, this book is for you. If you already have experience working with Azure Synapse and want to incorporate unstructured data into your data science project, you'll also find plenty of useful information in this book. To maximize your learning experience, familiarity with data and performing simple queries using SQL or KQL is recommended. Basic knowledge of Python will help you get more from the examples.

Master Tableau fundamentals and get the one and only Tableau certification that never expires, while expediting your journey from zero to certificationKey Features:Learn how Tableau works inside and out for basic as well as intermediate uses of the applicationGain knowledge from a Tableau visionary and ambassador who successfully passed the examination in 2021Understand what is needed to pass a knowledge-based examination without having to use Tableau in the processBook Description:The Tableau Desktop Specialist certification is fundamental for any data visualization professional who works in the field with Tableau.This book gets you started by covering the exam format, Tableau basics, and best practices for preparing data for analysis and visualization. It also builds on your knowledge of advanced Tableau topics to get you up to speed with the essential domains and domain objectives. Although the guide provides an outline and starting point to key in on what needs to be understood before the examination, it also delivers in context to give you a strong understanding of each piece before taking the exam. Instructions on how to get hands on with examples, a common data source, and suggested elements are also included. Understanding the concepts will not only assist you in passing the examination, but will also help you work effectively with the tool in your workspace.By the end of this book, you'll be able to efficiently prepare for the certification exam with the help of mock tests, detailed explanations, and expert advice from the author.What You Will Learn:Understand how to add data to the applicationExplore data for insights in TableauDiscover what charts to use when visualizing for audiencesUnderstand functions, calculations and the basics of parametersWork with dimensions, measures and their variationsContextualize a visualization with marksShare insights and focus on editing a Tableau visualizationWho this book is for:If you're a data analyst, data scientist, or if you just want to enhance your data visualization tool stack, this book is for you. It's designed for those without prior and those with minimal exposure to Tableau, which also means it's useful for anyone moving into their first role that relies on data visualization.

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

Make the most of Splunk 9.x to build insightful reports and dashboards with a detailed walk-through of its extensive features and capabilitiesKey Features:Be well-versed with the Splunk 9. x architecture, installation, onboarding, and indexing data featuresCreate advanced visualizations using the Splunk search processing languageExplore advanced Splunk administration techniques, including clustering, data modeling, and container managementBook Description:Splunk 9 improves on the existing Splunk tool to include important features such as federated search, observability, performance improvements, and dashboarding. This book helps you to make the best use of the impressive and new features to prepare a Splunk installation that can be employed in the data analysis process.Starting with an introduction to the different Splunk components, such as indexers, search heads, and forwarders, this Splunk book takes you through the step-by-step installation and configuration instructions for basic Splunk components using Amazon Web Services (AWS) instances. You'll import the BOTS v1 dataset into a search head and begin exploring data using the Splunk Search Processing Language (SPL), covering various types of Splunk commands, lookups, and macros. After that, you'll create tables, charts, and dashboards using Splunk's new Dashboard Studio, and then advance to work with clustering, container management, data models, federated search, bucket merging, and more.By the end of the book, you'll not only have learned everything about the latest features of Splunk 9 but also have a solid understanding of the performance tuning techniques in the latest version.What You Will Learn:Install and configure the Splunk 9 environmentCreate advanced dashboards using the flexible layout options in Dashboard StudioUnderstand the Splunk licensing modelsCreate tables and make use of the various types of charts available in Splunk 9.xExplore the new configuration management featuresImplement the performance improvements introduced in Splunk 9.xIntegrate Splunk with Kubernetes for optimizing CI/CD managementWho this book is for:The book is for data analysts, Splunk users, and administrators who want to become well-versed in the data analytics services offered by Splunk 9. You need to have a basic understanding of Splunk fundamentals to get the most out of this book.

Web developers-this is your all-in-one guide to HTML and CSS! Learn to use HTML to format text and structure web pages. Understand the HTML document skeleton before creating forms, referencing hyperlinks, embedding active content, and more. Then style your pages with CSS: Create consistent designs with selectors, the box model, the cascade algorithm, and inheritance. Round out your client-side development experience by getting to know JavaScript. With detailed code examples, you'll master HTML and CSS in no time!Highlights include:1) HTML syntax and structure 2) HTML elements3) Tables, links, and images4) HTML forms5) CSS selectors6) Cascade and inheritance 7) CSS box model8) CSS preprocessors9) Responsive layout design10) Testing11) JavaScript12) AjaxHighlights:HTML syntax and structure HTML elementsTables, links, and imagesHTML formsCSS selectorsCascade and inheritance CSS box modelCSS preprocessorsResponsive layout designTestingJavaScriptAjax

Turn your R code into packages that others can easily download and use. This practical book shows you how to bundle reusable R functions, sample data, and documentation together by applying author Hadley Wickhams package development philosophy. In the process, youll work with devtools, roxygen, and testthat, a set of R packages that automate common development tasks. Devtools encapsulates best practices that Hadley has learned from years of working with this programming language.Ideal for developers, data scientists, and programmers with various backgrounds, this book starts you with the basics and shows you how to improve your package writing over time. Youll learn to focus on what you want your package to do, rather than think about package structure.Learn about the most useful components of an R package, including vignettes and unit testsAutomate anything you can, taking advantage of the years of development experience embodied in devtoolsGet tips on good style, such as organizing functions into filesStreamline your development process with devtoolsLearn the best way to submit your package to the Comprehensive R Archive Network (CRAN)Learn from a well-respected member of the R community who created 30 R packages, including ggplot2, dplyr, and tidyr

This report assesses the cost-effectiveness of a U.S. Coast Guard rule that requires any facility handling certain dangerous cargoes to biometrically verify the identity of anyone accessing a secure area of that facility.

Tauchen Sie tief in den architektonischen Ansatz, die bewährten Verfahren und die wichtigsten Überlegungen ein, die zum Verstehen, Bewerten und Entwerfen einer effizienten, skalierbaren und nachhaltigen Salesforce-basierten Lösung erforderlich sind. Dieses Buch wirft einen umfassenden Blick auf die sieben architektonischen Bereiche, die bei der Architektur einer Salesforce-basierten Lösung berücksichtigt werden müssen, und versetzt Sie in die Lage, die Artefakte zu entwickeln, die für einen durchgängigen Unternehmensarchitektur-Entwurf für die Salesforce-Implementierung und DevOps erforderlich sind.Dieses unentbehrliche Handbuch hilft Salesforce-Experten bei der Implementierung und Verwaltung von Salesforce in ihrem Unternehmen. Sie lernen die Salesforce-Architektur kennen: Lösungsarchitektur, Datenarchitektur, Sicherheitsarchitektur, Integrationsarchitektur, Identitäts- und Zugriffsmanagementarchitektur und Strategien, die für Salesforce-basierte mobile Anwendungen verwendet werden können.Neben den wichtigsten Architekturkonzepten bietet das Buch auch branchenübliche Best Practices und den empfohlenen Rahmen für die Herangehensweise, das Management, die Bereitstellung und die kontinuierliche Verbesserung einer Salesforce-Lösung mithilfe des Salesforce Development & Deployment Lifecycle.Sie werden:· Verschaffen Sie sich einen detaillierten Überblick über das mandantenfähige, metadatengesteuerte Architektur-Framework von Salesforce und die zugrunde liegende Technologie, die Salesforce unterstützt· Sie kennen die sieben Architekturbereiche, ihre Feinheiten und die Überlegungen, die beim Entwurf einer Salesforce-Lösung in jedem Bereich angestellt werden müssen.· Sie verfügen über eine architektonische Denkweise und die erforderlichen Artefakte, um eine durchgängige Implementierung von Salesforce auf Unternehmensebene zu konzipieren.· Vertraut sein mit den gängigsten Salesforce-Produkten, Lizenzen, AppExchange-Produkten und den wichtigsten Erwägungen bei der Verwendung von sofort einsatzbereiten deklarativen Funktionen gegenüber benutzerdefinierten programmatischen Funktionen von Salesforce· Verständnis der Überlegungen zur Gestaltung der Datenarchitektur, einschließlich der Datenmodellierung in Salesforce, der Identifizierung und Entschärfung von Problemen bei großen Datenmengen sowie der wichtigsten Überlegungen zur Datenmigration und DatenarchivierungsstrategienDieses Buch stellt die Übersetzung einer englischsprachigen Originalausgabe dar. Die Übersetzung wurde mit Hilfe von künstlicher Intelligenz erstellt (maschinelle Übersetzung mit DeepL.com). Eine anschließende manuelle Überarbeitung erfolgte vor allem nach inhaltlichen Gesichtspunkten, so dass das Buch stilistisch von einer herkömmlichen Übersetzung abweichen kann.