Informationsvisualisering

Are you currently seeking to actualize your aspirations? Would you be interested in a guaranteed method to fulfill all of your aspirations? Do you find yourself fatigued by the repetition of success narratives detailing the accomplishments attained through visualization, and yearn to personally experiment with this technique?Hence, the experienced and knowledgeable senior strategy consultant, Sam Schreim, has compiled years of expertise and profound understanding within the pages of his book, "Storytelling with Charts: A Data & Text Visualization Guide for Business, Professionals and Non-Professionals". This comprehensive guide offers step-by-step instructions on effectively utilizing the influence of compelling narratives and psychological methods to craft enthralling data stories.This book will empower you to achieve the desired results you have always aspired to, by providing a concise compilation of proven strategies that will enable you to excel in various aspects of your life. The principles delineated in this book can be effectively employed across various facets of your existence, encompassing interpersonal connections, financial matters, vocational endeavors, personal interests, and professional aspirations."

If you are inclined to do so, this book would prove to be highly instrumental in aiding you to commence your expedition of creative visualization. This book aims to enhance your comprehension of creative visualization while providing a comprehensive range of remarkable visualization techniques. By diligently practicing these methods, you can bolster your ability to envision and subsequently manifest your aspirations.The acquisition of creative visualization skills is imperative for individuals aspiring to attain the long-desired objectives that have eluded them thus far. This guide will provide comprehensive insight into the concept of creative visualization and the potential it holds for facilitating profound positive transformations in one's life, leading to the realization of a more gratifying existence characterized by greater purpose and significance.Through the acquisition of this book and meticulous perusal of its contents, you shall be positioned to accurately conceive and perceive enduring transformations. With these visualization methodologies, you will surpass all prior accomplishments, bearing witness to the realization of robust and positive modifications in your life.

Practical Charts provides concrete, easy-to-learn guidelines for creating "everyday" charts for reports and presentations, allowing you to quickly learn how to create charts that are clear, compelling, and accurate. Written in a friendly, jargon-free style by globally recognized data visualization expert and educator Nick Desbarats, Practical Charts will equip you with practical, highly specific guidelines for handling the vast majority of common chart design challenges (showing outliers, showing large numbers of values, etc.) and avoiding dozens of common mistakes (failing to annotate bars of zero length in bar charts, using sequential colors to identify non-sequential categories, etc.), covering over 30 important chart types and 180 key takeaways along the way. An essential resource for data visualization beginners and those with decades of data-handling experience alike.

Extract valuable insights from data by leveraging various analysis and visualization techniques with this comprehensive guide Purchase of the print or Kindle book includes a free PDF eBookKey Features:- Gain practical experience in conducting EDA on a single variable of interest in Python- Learn the different techniques for analyzing and exploring tabular, time series, and textual data in Python- Get well versed in data visualization using leading Python libraries like Matplotlib and seabornBook Description:In today's data-centric world, the ability to extract meaningful insights from vast amounts of data has become a valuable skill across industries. Exploratory Data Analysis (EDA) lies at the heart of this process, enabling us to comprehend, visualize, and derive valuable insights from various forms of data.This book is a comprehensive guide to Exploratory Data Analysis using the Python programming language. It provides practical steps needed to effectively explore, analyze, and visualize structured and unstructured data. It offers hands-on guidance and code for concepts such as generating summary statistics, analyzing single and multiple variables, visualizing data, analyzing text data, handling outliers, handling missing values and automating the EDA process. It is suited for data scientists, data analysts, researchers or curious learners looking to gain essential knowledge and practical steps for analyzing vast amounts of data to uncover insights.Python is an open-source general purpose programming language which is used widely for data science and data analysis given its simplicity and versatility. It offers several libraries which can be used to clean, analyze, and visualize data. In this book, we will explore popular Python libraries such as Pandas, Matplotlib, and Seaborn and provide workable code for analyzing data in Python using these libraries.By the end of this book, you will have gained comprehensive knowledge about EDA and mastered the powerful set of EDA techniques and tools required for analyzing both structured and unstructured data to derive valuable insights.What You Will Learn:- Perform EDA with leading python data visualization libraries- Execute univariate, bivariate and multivariate analysis on tabular data- Uncover patterns and relationships within time series data- Identify hidden patterns within textual data- Learn different techniques to prepare data for analysis- Overcome challenge of outliers and missing values during data analysis- Leverage automated EDA for fast and efficient analysisWho this book is for:Whether you are a data analyst, data scientist, researcher or a curious learner looking to analyze structured and unstructured data, this book will appeal to you. It aims to empower you with essential knowledge and practical skills for analyzing and visualizing data to uncover insights.It covers several EDA concepts and provides hands-on instructions on how these can be applied using various Python libraries. Familiarity with basic statistical concepts and foundational knowledge of python programming will help you understand the content better and maximize your learning experience.Table of Contents- Generating Summary Statistics- Preparing Data for EDA- Visualising Data in Python- Performing Univariate Analysis in Python- Performing Bivariate analysis in Python- Performing Multivariate analysis in Python- Analysing Time Series data- Analysing Text data- Dealing with Outliers and Missing values- Performing Automated EDA in Python

Personal knowledge graphs (PKGs) support the development of innovative digitalized personalized applications, which keep users updated, help them manage their day-to-day activities and facilitate informed decisions. This book systematically explores the global advanced research around PKGs from methodologies to tools and applications.

Im täglichen Leben sind wir zunehmend von Codes umgeben, die mathematisch konstruiert werden. Sie sind teils leicht erkennbar (Strichcode, ISBN, IBAN, QR) und teils eher verborgen (GPS, WLAN, CD, DVD). In diesem Buch werden solche Codes vorgestellt. Es wird dargelegt, wie sie aufgebaut sind, wie sie funktionieren und welche Mathematik zu ihrer Entwicklung und Anwendung notwendig ist. Die Lesenden lernen, eigenhändig Codes zu erstellen, Fehler zu erkennen und zu korrigieren:EAN, ISBN und deren Barcodedarstellung sowie die internationale Bankkontonummer IBAN werden erarbeitet.Kleine QR-Codes werden mit den vorgestellten Methoden (Paritätsprüfung, Linearcode, Polynomcode, zyklischer Code und Reed-Solomon Code) anschaulich realisiert.An der Herstellung einer Mini-CD mit einem CIRC-Code über einem kleinen Körper werden wesentliche Konstruktionsprinzipien von neuen Codes aus bestehenden Codes, wie z.B. Kürzen, Erweitern, Spreizen (Interleaving) und gekreuztes Spreizen (Cross-Interleaving) veranschaulicht.Das Verstehen von Mathematik wird durch diese selbstständige Erstellung und Verwendung didaktisch maßgeschneiderter Codes wesentlich gefördert.Ein besonderer Fokus des Buchs liegt auf elementaren Methoden des Rechnens mit ganzen Zahlen und Polynomen. Für diese benötigt man nur den Satz von der Division mit Rest als zentrale Aussage ¿ daher können große Abschnitte bereits mit Lernenden der Sekundarstufe II erarbeitet und die Grundlagen wesentlicher Teile der Codierungstheorie von den Lernenden mathematisch korrekt erfasst werden. Für Ausführungen, zu deren Verständnis Kenntnisse notwendig sind, die über die Mathematik der Sekundarstufe II hinausgehen, liegt ein ausführlicher Anhang vor (Vektorräume, Matrizen, Rechnen in endlichen Körpern).

This book provides an overview of the emerging field of in situ visualization, i.e. visualizing simulation data as it is generated. In situ visualization is a processing paradigm in response to recent trends in the development of high-performance computers. It has great promise in its ability to access increased temporal resolution and leverage extensive computational power. However, the paradigm also is widely viewed as limiting when it comes to exploration-oriented use cases. Furthermore, it will require visualization systems to become increasingly complex and constrained in usage. As research efforts on in situ visualization are growing, the state of the art and best practices are rapidly maturing.Specifically, this book contains chapters that reflect state-of-the-art research results and best practices in the area of in situ visualization. Our target audience are researchers and practitioners from the areas of mathematics computational science, high-performance computing, and computer science that work on or with in situ techniques, or desire to do so in future. 

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

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

The adoption of multilayer analysis techniques is rapidly expanding across all areas of knowledge, from social sciences (the first facing the complexity of such structures, decades ago) to computer science, from biology to engineering. However, until now, no book has dealt exclusively with the analysis and visualization of multilayer networks. Multilayer Networks: Analysis and Visualization provides a guided introduction to one of the most complete computational frameworks, named muxViz, with introductory information about the underlying theoretical aspects and a focus on the analytical side. Dozens of analytical scripts and examples to use the muxViz library in practice, by means of the Graphical User Interface or by means of the R scripting language, are provided. In addition to researchers in the field of network science, as well as practitioners interested in network visualization and analysis, this book will appeal to researchers without strong technical or computer science background who want to learn how to use muxViz software, such as researchers from humanities, social science and biology: audiences which are targeted by case studies included in the book. Other interdisciplinary audiences include computer science, physics, neuroscience, genetics, urban transport and engineering, digital humanities, social and computational social science.Readers will learn how to use, in a very practical way (i.e., without focusing on theoretical aspects), the algorithms developed by the community and implemented in the free and open-source software muxViz. The data used in the book is available on a dedicated (open and free) site.

This book is a self-guided tour of MATLAB for engineers and life scientists. It introduces the most commonly used programming techniques through biologically inspired examples. Although the text is written for undergraduates, graduate students and academics, as well as those in industry, will find value in learning MATLAB.The book takes the emphasis off of learning syntax so that the reader can focus more on algorithmic thinking. Although it is not assumed that the reader has taken differential equations or a linear algebra class, there are short introductions to many of these concepts. Following a short history of computing, the MATLAB environment is introduced. Next, vectors and matrices are discussed, followed by matrix-vector operations. The core programming elements of MATLAB are introduced in three successive chapters on scripts, loops, and conditional logic. The last three chapters outline how to manage the input and output of data, create professional quality graphics and find and use MATLAB toolboxes. Throughout, biomedical and life science examples are used to illustrate MATLAB's capabilities.

In today's world, organizations face a multitude of problems that require an unprecedented need for tools to share information and work better together. In Working Beyond Borders: GIS for Geospatial Collaboration, see how government, industries, and others, are using location intelligence and GIS to interconnect people across jurisdictions and sectors, to respond to some of our most critical issues, such as climate change, sustainable development, racial equity, emergency management, conservation, and public health and safety.Readers will also see how organizations are integrating geospatial infrastructure to improve efficiency, drive innovation, and empower every day decision-making in communities around the world.Edited by Dr. Jill Saligoe-Simmel and Maria JordanApplying GIS The Applying GIS series explains how to become a spatial thinker with ideas and strategies for building location intelligence into your profession, industry, or discipline. Each book is divided into relevant topic areas that include real-life case studies that will inspire new ways to solve complex problems.

Top 20 Essential Skills for ArcGIS® Pro introduces the most important skills you need to get up and running with geographic information systems (GIS).With its location data advantage, geographic information system (GIS) software is a tool to help with key projects, decisions, and problems. But how do you start using GIS in your projects? If you need a fast track to using this valuable tool, learn and get experience with Top 20 Essential Skills for ArcGIS Pro. With this book, you can start using ArcGIS Pro quickly and successfully.Easy to understand, step-by-step exercisesConversational, upbeat language with lots of illustrations and tipsInteresting user stories covering how GIS is applied in many situationsExercises for working with spatial data, creating maps, and doing basic analysisThis handy resource with easy to follow, how-to steps will help you build your skill set to become adept at understanding and using ArcGIS Pro.Bonnie Shrewsbury, MA, GISP, is the GIS manager for the City of Manhattan Beach, California. She has more than 26 years of experience in GIS, including 16 years of coteaching a GIS course with her coauthor at the University of Southern California (USC) for graduate-level planning and public policy students.Barry Waite has almost 40 years of local government experience as an administrator and city planner. He has a master's degree in public administration from the University of Southern California where he teaches GIS with Bonnie. He is also a city council member for the City of Lomita, California.

This classic ArcGIS® exercise book has been revised and streamlined for learning the latest ArcGIS® Pro tools and workflows.GIS Tutorial for ArcGIS Pro 3.1 is the book of choice for classrooms and self-learners seeking to develop their expertise with Esri's premier desktop geographic information system (GIS) technology-no prior experience is necessary.This fifth edition, revised to ArcGIS Pro 3.1, features new datasets, exercises, and instructional text guiding you step by step through the latest tools and workflows. Updated to explain core skills through progressive learning, the book's examples use current, real-world scenarios as you learn to make maps and find, create, and analyze spatial data while using ArcGIS Pro and ArcGIS Online.Downloadable video lectures and teaching slides that complement this book are also available.Wilpen L. Gorr is emeritus professor of public policy and management information systems at the School of Public Policy and Management, H. John Heinz III College, Carnegie Mellon University, where he taught and researched GIS applications. He was also chairman of the school's Master of Science in Public Policy and Management program and editor of the International Journal of Forecasting.Kristen S. Kurland is a Teaching Professor of Architecture, Information Systems, and Public Policy at the H. John Heinz III College and School of Architecture, Carnegie Mellon University, where she teaches GIS, building information modeling, computer-aided design, 3D visualization, infrastructure management, and enterprise data analytics.

Spatial statistics empower you to go beyond visual analysis to answer questions confidently and make data-driven decisions.Spatial Statistics Illustrated is an introductory book for learning the concepts behind the powerful spatial statistics tools in ArcGIS.With approachable explanations and uncomplicated drawings, Spatial Statistics Illustrated gives readers an accessible understanding of some of the most widely used spatial statistics methods, including how they work and when to use them. In a friendly, conversational tone, the authors share techniques that can help you explore your data in meaningful ways; quantify patterns and relationships; understand trends, and make informed, impactful decisions. This book is a perfect complement to more traditional, technical statistics and spatial statistics texts. From seasoned data scientists looking to explore the value that spatial thinking brings to the GIS analyst looking to expand into spatial statistics, this book has something for everyone.Dr. Lauren Bennett leads the Spatial Analysis and Data Science product engineering team at Esri. Lauren received a BA in Geography from McGill University, an MS in Geographic and Cartographic Science from George Mason University, and her PhD in Information Systems and Technology from Claremont Graduate University.Flora Vale is a product engineer on Esri's Spatial Analysis and Data Science team. In addition to building software, Flora loves teaching analytical methods through conceptual illustrations. Flora studied Geography and GIS at University of Maryland, and is currently pursuing a PhD in Information Systems and Technology at Claremont Graduate University.

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.

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.

Praise for PRESENT BEYOND MEASURE "Simple, clear and constantly overlooked wisdom. It's time to stop wasting time and start making a change happen."-Seth Godin, Author of This is Marketing"Lea delivers an evidence-based approach to presenting data in a practical and engaging manner that is sure to help the reader make their presentation audiences Indistractable."-Nir Eyal, Bestselling Author of Hooked and Indistractable "Lea's given the world a gift with Present Beyond Measure. It shows you how to turn bored, jaded, phone-addicted data presentation skeptics into eager, engaged, edge-of-their-seat fans."-Rand Fishkin, CEO and Cofounder of SparkToro "Infused with wisdom from Lea's own influential work in the field, Present Beyond Measure is a must-read for all data professionals looking to take their work to the next level through the perfect combination of storytelling, visualization, and communication."-Christina Stathopolous, Data Evangelist, Advisor, and Educator "Lea is an absolute master at showing people how to present their fact-based ideas and data in creative, visual, and persuasive ways. Present Beyond Measure is a must-have book for our time."-Garr Reynolds, Author of Presentation Zen "Present Beyond Measure is an empowering and transformative guide for data practitioners, providing the necessary tools to succeed at presenting data effectively."-Kate Strachnyi, Founder of DATAcated and author of ColorWise

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.

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 features- Create advanced visualizations using the Splunk search processing language- Explore 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 environment- Create advanced dashboards using the flexible layout options in Dashboard Studio- Understand the Splunk licensing models- Create tables and make use of the various types of charts available in Splunk 9.x- Explore the new configuration management features- Implement the performance improvements introduced in Splunk 9.x- Integrate 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.Table of Contents- Introduction to Splunk and its Core Components- Setting Up the Splunk Environment- Onboarding and Normalizing Data- Introduction to SPL- Reporting Commands, Lookups, and Macros- Creating Tables and Charts Using SPL- Creating Dynamic Dashboards- Licensing, Indexing, and Buckets- Clustering and Advanced Administration- Data Models, Acceleration, and Other Ways to Improve Performance- Multisite Splunk Deployments and Federated Search- Container Management

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

Create useful and usable map apps that your users will love.Turn your next map app project into a success with Designing Map Interfaces: Patterns for Building Effective Map Apps. Whether you're configuring an out-of-the-box solution, building an app using one of the readily available app builder tools, or working on a custom app project, this book will guide you toward developing more useful and usable apps. Current courses for application development focus on technology and architecture rather than the tenets of interface design. This book teaches GIS professionals, developers, and designers the principles and best practices that will help them create stunning consumer-grade apps.Designing Map Interfaces provides a language for planning and building map apps. The elements of this language are made up of user interface (UI) patterns. Each pattern describes a solution to an observed and recurring problem in UI design. This book explains when to use the pattern, why it is important, and what to consider-and in turn will help readers make educated decisions on what, why, when, and how to solve problems to make their apps work. Throughout the book, patterns are illustrated through real-world examples.Key topics include:getting started with design,selecting the right layout,interacting with the map, dealing with complex data,designing for mobile devices,building single-purpose apps, andcommon mistakes and how to avoid them.This book is aimed at anyone who configures (solution engineers, GIS professionals) or builds (developer community, designers) map apps, especially the fast-growing group of users who employ application builders and tools that create apps to publish their own data and maps on the web. Designing Map Interfaces fills the gap that documentation lacks-practical tips on how to assemble a meaningful UI.This book is the essential guide to designing map interfaces that are usable and efficient, and that look good in the process.