Embark on a journey to create stunning web-based 3D applications and games using Babylon.js, a powerful JavaScript framework. This essentials book takes you from the ground up, teaching the theory and practical applications of 3D development, with a particular focus on ease and accessibility. By the end of this guide, you'll possess the skills to design and implement dynamic 3D experiences. What this Book will help me do Gain a fundamental understanding of TypeScript and its advantages in large-scale projects like 3D engines. Master the foundational principles of 3D development with Babylon.js, emphasized through hands-on practice and clear theory. Learn to apply materials in Babylon.js, enabling you to alter and enrich the visual appeal of 3D objects. Incorporate collision physics and gameplay dynamics by understanding essential concepts like impostors in 3D simulations. Utilize advanced Babylon.js features, such as 3D audio spatialization and rendering post-process effects, to create immersive experiences. Author(s) None Moreau-Mathis, a software developer with significant experience, has worked on developing cutting-edge applications at companies like Microsoft. Their expertise in 3D development frameworks, particularly Babylon.js, has enabled them to break down complex technical concepts into approachable lessons. Moreau-Mathis combines a passion for sharing knowledge with years of hands-on experience to provide readers with practical and inspirational advice. Who is it for? This book is perfect for developers familiar with HTML5 and seeking to begin 3D Web application and game development using Babylon.js. Readers should have basic programming knowledge, such as in Object-Oriented Programming, and familiarity with web development concepts to grasp framework architecture effectively. Whether you are enhancing your web development toolkit or starting out with 3D, this book will help you achieve your aims.

This book is designed for undergraduate and graduate students taking a first course in Dynamics of Structures, Structural Dynamics or Earthquake Engineering. It includes several topics on the theory of structural dynamics and the applications of this theory to the analysis of buildings, bridges, towers and other structures subjected to dynamic and earthquake forces. This comprehensive text demonstrates the applications of numerical solution techniques to a large variety of practical, real-world problems under dynamic loads.

Dive into the world of regression analysis guided by Python in this comprehensive book. From simple linear regression to complex models, you'll gain a deep understanding of how to analyze data and predict outcomes. By the end of this book, you will be equipped with the skills to tidy data, build models, and apply regression techniques to real-world problems. What this Book will help me do Understand and format datasets to prepare them for regression analysis efficiently. Build and implement various regression models, such as linear and logistic regression, to solve data science problems. Develop techniques to combat overfitting and ensure predictive accuracy. Learn to scale and adapt regression models to large datasets and apply incremental learning. Apply the skills gained to make informed business decisions using predictive insights from regression models. Author(s) Luca Massaron and Alberto Boschetti are seasoned data professionals with years of expertise in data science, regression analysis, and Python programming. They are passionate about teaching and have crafted this book to demystify regression for learners interested in predictive analytics. Their approachable style ensures concepts are accessible yet comprehensive. Who is it for? This book is ideal for Python developers and data scientists who have a foundational knowledge of math and statistics. Whether you're looking to delve deeper into predictive modeling or efficiently analyze datasets, this book provides step-by-step guidance. If you've dabbled in data science and wish to expand your skillset to include regression analysis, this book is for you!

Enhancing Enterprise Intelligence: Leveraging ERP, CRM, SCM, PLM, BPM, and BI takes a fresh look at the benefits of enterprise systems (ES), focusing on the fact that ES collectively contribute to enhancing the intelligence quotient of an enterprise. The book provides an overview of the characteristics and essential strengths of various categories of ES, namely, Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), Supply-Chain Management (SCM), Product LifeCycle Management (PLM), Business Process Management (BPM), and Business Intelligence (BI).

Clear, practical IPSAS guidance, explanation, and examples Interpretation and Application of IPSAS provides practical guidance on the implementation and application of the International Public Sector Accounting Standards. This book brings readers up to date on the standards, and describes their proper interpretation and real-world application. Examples and mini-case studies clarify the standards' roles throughout, giving readers a better understanding of complex processes, especially where the IPSAS deviate from IFRS. Readers also gain insight into smoothly navigating the transition for a public sector entity, which is moving to either IPSAS under accrual basis of accounting or to cash accounting IPSAS, plus an overview of IPSAS adoption status and methods around the world. Global public sector accounting is highly diversified, resulting in ongoing moves to harmonise standards worldwide. The IPSAS are international standards that largely follow the IFRS model, but differ in some key areas and include standards in places where IFRS has none. This book provides complete guidance to IPSAS, with clear explanation and expert insight. Understand the meaning and role of each standard Apply the standards to real-world scenarios Manage the process of transition to IPSAS These standards are meant to be followed by all public sector entities, including national and regional governments and local authorities. They've been adopted by the UN, NATO, the European Commission, and others, and either have been or soon will be adopted in Malaysia, Switzerland, Spain, and more.

While big data has already made significant advances in business and government, data analytics is also beginning to transform education. This O’Reilly report explores how the use of analytics has already helped several educational programs, such as personalized learning and massive open online courses (MOOCs), for students of all ages. Of course, that’s only part of the story. As author Taylor Martin explains, researchers, educators, and private practitioners in the field have also run into several challenges in bringing the education field up to speed. Issues such as building data infrastructures, integrating data sources, and assuring student privacy still need to be resolved—as does the problem of teaching a new generation of data scientists about the challenges and opportunities unique to education. Download this report and find out what educators and analysts have accomplished so far, and how they hope data analytics will help improve outcomes for students, parents, schools, and teachers in the near future. Taylor Martin is a professor of Instructional Technology and Learning Sciences at Utah State University. She researches how people learn from active participation, both physical and social. Currently on rotation at the National Science Foundation, Dr. Martin focuses on a variety of efforts to understand how big data is impacting research in education and across the STEM disciplines.

Companies are collecting more data than ever. But, given how difficult it is to unify the many internal and external data streams they’ve built, more data doesn’t necessarily translate into better analytics. The real challenge is to provide deep and broad access to “a single source of truth” in their data that the typically slow ETL process for data warehousing cannot achieve. More than just fast access, analysts need the ability to explore data at a granular level. In this O’Reilly report, author Courtney Webster presents a roadmap to data centralization that will help your organization make data accessible, flexible, and actionable. Building a genuine data-driven culture depends on your company’s ability to quickly act upon new findings. This report explains how. Identify stakeholders: build a culture of trust and awareness among decision makers, data analysts, and quality management Create a data plan: define your needs, specify your metrics, identify data sources, and standardize metric definitions Centralize the data: evaluate each data source for existing common fields and, if you can, minor variances, and standardize data references Find the right tool(s) for the job: choose from legacy architecture tools, managed and cloud-only services, and data visualization or data exploration platforms Courtney Webster is a reformed chemist in the Washington, D.C. metro area. She spent a few years after grad school programming robots to do chemistry and is now managing web and mobile applications for clinical research trials.

Provides a point of entry for understanding the basics of the SAS Intelligence Platform. It discusses the benefits of the SAS Intelligence Platform to businesses, describes the architecture, and provides an overview of each software component. This document can be used alone or as an introduction to the deployment and administration guides.

Key Business Analytics will help managers apply tools to turn data into insights that help them better understand their customers, optimise their internal processes and identify cost savings and growth opportunities.

How do you take your data analysis skills beyond Excel to the next level? By learning just enough Python to get stuff done. This hands-on guide shows non-programmers like you how to process information that’s initially too messy or difficult to access. You don't need to know a thing about the Python programming language to get started. Through various step-by-step exercises, you’ll learn how to acquire, clean, analyze, and present data efficiently. You’ll also discover how to automate your data process, schedule file- editing and clean-up tasks, process larger datasets, and create compelling stories with data you obtain. Quickly learn basic Python syntax, data types, and language concepts Work with both machine-readable and human-consumable data Scrape websites and APIs to find a bounty of useful information Clean and format data to eliminate duplicates and errors in your datasets Learn when to standardize data and when to test and script data cleanup Explore and analyze your datasets with new Python libraries and techniques Use Python solutions to automate your entire data-wrangling process

This book dives into the practical application of R's power combined with Shiny's simplicity to build web-based analytics and interactive data summary tools. By following this step-by-step guide, you'll go from the basics of building with R and Shiny to creating sophisticated custom dashboards and interactive web apps. What this Book will help me do Create interactive web apps and dashboards using Shiny with impressive user interfaces. Integrate Shiny applications into custom HTML and CSS-based web pages for enhanced flexibility. Produce user-friendly Shiny applications extended with JavaScript and jQuery for added functionality. Develop web solutions that include interactive graphics, maps, and data analysis summaries. Deliver and deploy web apps securely using cloud solutions or self-hosted servers. Author(s) Chris Beeley, an experienced R developer and teacher, has a robust background in statistical programming and data analysis. Chris is passionate about sharing knowledge through practical examples and hands-on exercises. As the author of this book, Chris ensures that readers receive a clear and approachable entry into web application development using Shiny. Who is it for? This book is ideal for data enthusiasts, analysts, and developers looking to transition their analytic skills to the web. It caters to readers with basic programming knowledge but does not require prior experience with R or Shiny. It is perfect for professionals and learners wanting to create interactive analytics tools, dashboards, or data-driven web applications.

Make the most of your data using the power of Excel When you think of data, do you think of endless rows and columns in spreadsheets? Excel Dashboards and Reports For Dummies, 3 shows you how to make the most of your data—and puts an end to mind-numbing spreadsheets by exploring new ways to conceptualize and present key information. There's often a gap between handling data and synthesizing it into meaningful reports, and this approachable text bridges this gap with quick and accessible information that answers key questions, like how to meaningfully capture data trends, how to show relationships in data, and when it's better to show variances than actual data values. rd Edition As a leading spreadsheet application, Microsoft Excel is the go-to data software. This tool allows you to use dashboard reports that leverage gauges, maps, charts, sliders, and other visual elements to present complex data in a manner that's easy to understand. Using Excel dashboards effectively can improve your professional capabilities by leaps and bounds. Analyze and report on large amounts of data in a meaningful way Look at data from different perspectives, and better visualize the information you're presenting by quickly slicing data on the fly Automate redundant reporting and analysis functions, making your data analysis and reporting routine more efficient Create visualizations, dashboards, and what-if analyses that are as visually appealing as they are substantial Excel Dashboards and Reports For Dummies, 3 is a fantastic resource if you're looking to spice up your reporting! rd Edition

Transform your organization's data into actionable insights with Tableau Tableau is designed specifically to provide fast and easy visual analytics. The intuitive drag-and-drop interface helps you create interactive reports, dashboards, and visualizations, all without any special or advanced training. This all new edition of Tableau Your Data! is your Tableau companion, helping you get the most out of this invaluable business toolset. Tableau Your Data! shows you how to build dynamic, best of breed visualizations using the Tableau Software toolset. This comprehensive guide covers the core feature set for data analytics, and provides clear step-by-step guidance toward best practices and advanced techniques that go way beyond the user manual. You'll learn how Tableau is different from traditional business information analysis tools, and how to navigate your way around the Tableau 9.0 desktop before delving into functions and calculations, as well as sharing with the Tableau Server. Analyze data more effectively with Tableau Desktop Customize Tableau's settings for your organization's needs with detailed real-world examples on data security, scaling, syntax, and more Deploy visualizations to consumers throughout the enterprise - from sales to marketing, operations to finance, and beyond Understand Tableau functions and calculations and leverage Tableau across every link in the value chain Learn from actual working models of the book's visualizations and other web-based resources via a companion website Tableau helps you unlock the stories within the numbers, and Tableau Your Data! puts the software's full functionality right at your fingertips.

"QlikView Essentials" is your guide to mastering QlikView, a versatile and powerful business intelligence tool. This practical book walks you through the complete QlikView workflow, from loading data and creating effective data models to designing interactive dashboards and deploying BI applications. Gain confidence in handling data and deriving actionable insights using QlikView. What this Book will help me do Understand the full QlikView workflow, encompassing data loading, visualization, and analysis. Utilize QlikView's capabilities to load data from various sources effectively, including JSON and QVD files. Implement robust solutions for addressing common data modeling challenges in BI projects. Design engaging and accessible dashboards that visualize crucial insights and support decision-making. Successfully deploy QlikView applications securely for optimal usage and adoption within your organization. Author(s) None Sinha is an experienced data analyst and QlikView expert with a passion for making data accessible and actionable for businesses. With years of experience working with business intelligence platforms, Sinha has provided invaluable insights and strategies to organizations in leveraging their data effectively. This book reflects their dedication to simplifying complex concepts and empowering readers with hands-on skills. Who is it for? This book is ideal for professionals who work with data and want to leverage QlikView for business intelligence tasks. If you are a data analyst, manager, or IT professional familiar with BI concepts but looking to enhance your QlikView expertise, this guide is for you. Even if you're new to QlikView but have a basic understanding of data manipulation, you will find this book an excellent resource. It's tailored to anyone who wishes to turn data into meaningful insights easily and effectively.

This book illustrates the application of perfect simulation ideas and algorithms to a wide range of problems. The author describes numerous protocol methodologies for designing algorithms for specific problems. He first examines the commonly used acceptance/rejection (AR) protocol for creating perfect simulation algorithms. He then covers other major protocols, including coupling from the past (CFTP); the Fill, Machida, Murdoch, and Rosenthal (FMMR) method; the randomness recycler; retrospective sampling; and partially recursive AR, along with multiple variants of these protocols.

A step-by-step guide to data mining applications in CRM. Following a handbook approach, this book bridges the gap between analytics and their use in everyday marketing, providing guidance on solving real business problems using data mining techniques. The book is organized into three parts. Part one provides a methodological roadmap, covering both the business and the technical aspects. The data mining process is presented in detail along with specific guidelines for the development of optimized acquisition, cross/ deep/ up selling and retention campaigns, as well as effective customer segmentation schemes. In part two, some of the most useful data mining algorithms are explained in a simple and comprehensive way for business users with no technical expertise. Part three is packed with real world case studies which employ the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Case studies from industries including banking, retail and telecommunications are presented in detail so as to serve as templates for developing similar applications. Key Features: Includes numerous real-world case studies which are presented step by step, demystifying the usage of data mining models and clarifying all the methodological issues. Topics are presented with the use of three leading data mining tools: IBM SPSS Modeler, RapidMiner and Data Mining for Excel. Accompanied by a website featuring material from each case study, including datasets and relevant code. Combining data mining and business knowledge, this practical book provides all the necessary information for designing, setting up, executing and deploying data mining techniques in CRM. Effective CRM using Predictive Analytics will benefit data mining practitioners and consultants, data analysts, statisticians, and CRM officers. The book will also be useful to academics and students interested in applied data mining.

Fundamentals of Big Data Network Analysis for Research and Industry Hyunjoung Lee, Institute of Green Technology, Yonsei University, Republic of Korea Il Sohn, Material Science and Engineering, Yonsei University, Republic of Korea Presents the methodology of big data analysis using examples from research and industry There are large amounts of data everywhere, and the ability to pick out crucial information is increasingly important. Contrary to popular belief, not all information is useful; big data network analysis assumes that data is not only large, but also meaningful, and this book focuses on the fundamental techniques required to extract essential information from vast datasets. Featuring case studies drawn largely from the iron and steel industries, this book offers practical guidance which will enable readers to easily understand big data network analysis. Particular attention is paid to the methodology of network analysis, offering information on the method of data collection, on research design and analysis, and on the interpretation of results. A variety of programs including UCINET, NetMiner, R, NodeXL, and Gephi for network analysis are covered in detail. Fundamentals of Big Data Network Analysis for Research and Industry looks at big data from a fresh perspective, and provides a new approach to data analysis. This book: Explains the basic concepts in understanding big data and filtering meaningful data Presents big data analysis within the networking perspective Features methodology applicable to research and industry Describes in detail the social relationship between big data and its implications Provides insight into identifying patterns and relationships between seemingly unrelated big data Fundamentals of Big Data Network Analysis for Research and Industry will prove a valuable resource for analysts, research engineers, industrial engineers, marketing professionals, and any individuals dealing with accumulated large data whose interest is to analyze and identify potential relationships among data sets.

This book discusses the current status of QoE research; reporting latest advances from various standardisation bodies (ITU, ETSI, IEEE, IETF) Multimedia Quality of Experience (QoE): Current Status and Future Requirements discusses the current status of QoE (Quality of Experience) research, providing guidelines on QoE assessment and management practice. Moreover, the book covers many different aspects of QoE research, including definition, standardization (ITU, ETSI, IEEE, IETF, etc.), measurement, management, and architectures. In addition, the authors bring together contributions from recognized experts (worldwide) in the area of subjective and objective QoE video assessment. Topics/chapters include: (1) QoE Definition and its relation/mapping to QoS; QoE Standardization activities; QoE Metrics for various media applications; QoE Subjective and Objective Evaluation Methodologies; QoE Control, Monitoring and Management strategies; End-to-End QoE Architectures in heterogeneous environments. Discusses the current status of QoE research; reporting latest advances from various standardisation bodies (e.g. ITU, ETSI, IEEE, IETF) Provides guidelines on QoE assessment and management practice Explores methods, means and architectures of QoE Addresses multiple technologies and requires input from multiple disciplines such as engineering, sociology and psychology Brings together contributions from recognized experts (worldwide) in the area of subjective and objective QoE video assessment Considers future requirements of QoE

This book is about the concept of “Quality of Life”. What is necessary for quality of life, and how can it be measured? The approach is a multicriterial scheme reduction which prevents as much information loss as possible when shifting from the set of partial criteria to their convolution. This book is written for researchers, analysts and graduate and postgraduate students of mathematics and economics.

The apparent contradiction between statistical significance and biological relevance has diminished the value of statistical methods as a whole in toxicology. Moreover, recommendations for statistical analysis are imprecise in most toxicological guidelines. Addressing these dilemmas, Statistics in Toxicology Using R explains the statistical analysis of selected experimental data in toxicology and presents assay-specific suggestions, such as for the in vitro micronucleus assay. Mostly focusing on hypothesis testing, the book covers standardized bioassays for chemicals, drugs, and environmental pollutants. It is organized according to selected toxicological assays, including:Short-term repeated toxicity studiesLong-term carcinogenicity assaysStudies on reproductive toxicityMutagenicity assaysToxicokinetic studiesThe book also discusses proof of safety (particularly in ecotoxicological assays), toxicogenomics, the analysis of interlaboratory studies and the modeling of dose-response relationships for risk assessment. For each toxicological problem, the author describes the statistics involved, matching data example, R code, and outcomes and their interpretation. This approach allows you to select a certain bioassay, identify the specific data structure, run the R code with the data example, understand the test outcome and interpretation, and replace the data set with your own data and run again.Supporting material for this title can be downloaded here.