Unstructured data is the most voluminous form of data in the world, and several elements are critical for any advanced analytics practitioner leveraging SAS software to effectively address the challenge of deriving value from that data. This book covers the five critical elements of entity extraction, unstructured data, entity resolution, entity network mapping and analysis, and entity management. By following examples of how to apply processing to unstructured data, readers will derive tremendous long-term value from this book as they enhance the value they realize from SAS products.

Learning is an empirical phenomenon whereby people or organisations undergo a level of efficiency improvement with recurring tasks. Alan Jones pragmatic guide to this important element within estimating introduces two key learning curve models: Wright and Crawford and explains where, how and when to apply them.

Random Number Generators, Principles and Practices has been written for programmers, hardware engineers, and sophisticated hobbyists interested in understanding random numbers generators and gaining the tools necessary to work with random number generators with confidence and knowledge. Using an approach that employs clear diagrams and running code examples rather than excessive mathematics, random number related topics such as entropy estimation, entropy extraction, entropy sources, PRNGs, randomness testing, distribution generation, and many others are exposed and demystified. If you have ever Wondered how to test if data is really random Needed to measure the randomness of data in real time as it is generated Wondered how to get randomness into your programs Wondered whether or not a random number generator is trustworthy Wanted to be able to choose between random number generator solutions Needed to turn uniform random data into a different distribution Needed to ensure the random numbers from your computer will work for your cryptographic application Wanted to combine more than one random number generator to increase reliability or security Wanted to get random numbers in a floating point format Needed to verify that a random number generator meets the requirements of a published standard like SP800-90 or AIS 31 Needed to choose between an LCG, PCG or XorShift algorithm Then this might be the book for you.

This practical guide explores the business analyst role including typical responsibilities and necessary skills. It signposts useful tools and commonly used methodologies and techniques. A visual career roadmap for business analysts is also included, along with case studies and interviews with practising business analysts.

Prepare for the SAS Base Programming for SAS 9 exam with the official guide by the SAS Global Certification Program. New and experienced SAS users who want to prepare for the SAS Base Programming for SAS 9 exam will find this guide to be a valuable resource that covers all of the objectives tested on the exam. Now in its fifth edition, the guide has been extensively updated and revised to streamline explanations. Major topics include importing data, creating and modifying SAS data sets, and identifying and correcting data syntax and programming logic errors. The chapter quizzes have been thoroughly updated and full solutions are included at the back of the book. In addition, links are provided to the exam objectives, practice exams, and other helpful resources, such as the updated Base SAS glossary and an expanded collection of practice data sets.

"Security has become a ""big data"" problem. The growth rate of malware has accelerated to tens of millions of new files per year while our networks generate an ever-larger flood of security-relevant data each day. In order to defend against these advanced attacks, you'll need to know how to think like a data scientist. In Malware Data Science, security data scientist Joshua Saxe introduces machine learning, statistics, social network analysis, and data visualization, and shows you how to apply these methods to malware detection and analysis. You'll learn how to: • Analyze malware using static analysis• Observe malware behavior using dynamic analysis• Identify adversary groups through shared code analysis• Catch 0-day vulnerabilities by building your own machine learning detector• Measure malware detector accuracy• Identify malware campaigns, trends, and relationships through data visualization Whether you're a malware analyst looking to add skills to your existing arsenal, or a data scientist interested in attack detection and threat intelligence, Malware Data Science will help you stay ahead of the curve."

This book will provide methods to display space-time data using R. The code of each method will be detailed and commented through practical examples with real data. The second edition will discuss new interactive R packages and Add introductory sections with easier examples to show the basics of the most important packages and functions.

This book focuses on why one draws graphics to display data and which graphics to draw (and uses R to do so). Graphical data analysis is useful for data cleaning, exploring data structure, detecting outliers and unusual groups, identifying trends and clusters, spotting local patterns, evaluating modelling output, and presenting results. All the datasets are available in R or one of its packages and the R code is available online. Color graphics are used throughout the book.

This book provides an understanding of the different types of healthcare service providers, corresponding information technologies, analytic methods, and data issues that play a vital role in transforming the healthcare industry. A follow-up to Healthcare Informatics: Improving Efficiency and Productivity, this latest book includes new content that examines the evolution of Big Data and how it is revolutionizing the healthcare industry. Presenting strategies for achieving national goals for the meaningful use of health information technology, the book describes how to enhance process efficiency by linking technologies, data, and analytics with strategic initiatives.

This book discusses important structural filter classes including the median filter and a number of its extensions (e.g., weighted and recursive median filters), and Volterra filters based on polynomial nonlinearities. Using results from algebra and the theory of functional equations to construct and characterize behaviorally defined nonlinear filter classes, the text first introduces Python programming, and then proposes practical, bottom-up strategies for designing more complex and capable filters from simpler components in a way that preserves the key properties of these components.

Using a Lyapunov-based approach, this book introduces advanced tools for the stability analysis of nonlinear systems. It first discusses standard stability techniques and their shortcomings and then introduces recent developments in stability analysis that can improve the applicability of standard techniques. Finally, the book proposes the stability analysis of special classes of nonlinear systems. Coverage includes the stability of ordinary time-invariant differential equations and time-invariant systems as well as the stability analysis of time-delayed systems and fuzzy linguistic systems models.

This book is designed for users who already know R or MATLAB® and now need to learn the other platform. The author covers essential tasks, such as working with matrices and vectors, writing functions and other programming concepts, graphics, numerical computing, and file input/output. He highlights important differences between the two platforms and explores common mistakes that are easy to make when transitioning from one platform to the other.

This practical guide shows how to use R and its add-on packages to obtain numerical solutions to complex mathematical problems commonly faced by scientists and engineers. Providing worked examples and code, the text not only addresses necessary aspects of the R programming language but also demonstrates how to produce useful graphs and statistically analyze and fit data to linear and nonlinear models. It covers Monte Carlo, stochastic, and deterministic methods and explores topics such as numerical differentiation and integration, interpolation and curve fitting, and optimization.

"Hands-On Dashboard Development with Shiny" provides a focused and practical guide for developing polished and professional dashboards using R and the Shiny framework. Through applied examples, you'll explore techniques in HTML, CSS, and Bootstrap to elevate your Shiny apps' interface and functionality. The book ensures a comprehensive learning experience by focusing on creating custom interfaces and layouts. What this Book will help me do Create Shiny interfaces using pure HTML to achieve full customization Enhance your R Shiny dashboards with powerful layout functions for professional designs Utilize Bootstrap themes in Shiny for consistent and aesthetically pleasing interfaces Generate interactive dashboards complete with icons and notification elements Develop and distribute R Markdown reports directly from Shiny applications Author(s) Chris Beeley is an experienced data scientist and a frequent R Shiny practitioner. With years of experience building Shiny applications and working on data visualization in professional settings, Chris brings practical and precise teaching to this book. He is passionate about making knowledge accessible and guiding learners through hands-on and engaging content. Who is it for? This book is ideally suited for developers and data analysts who have prior experience with Shiny and wish to delve deeper into customizing their applications' design. If you want to extend your Shiny apps using HTML, CSS, and layouts via Bootstrap, this book is perfect for you. It equips you with the skills to build advanced dashboards efficiently. Whether you're looking to create more visually appealing apps or enhance your UI skills, this resource caters to your needs.

Pentaho Data Integration Quick Start Guide offers a comprehensive introduction to Pentaho's Extract-Transform-Load (ETL) tools. Through this book, you will learn to design, execute, and monitor data transformations and seamlessly integrate data across various sources. It is designed to simplify and streamline the process for developers and analysts. What this Book will help me do Understand the functionality and usage of Pentaho Data Integration tools to manage your ETL workflows. Utilize tools like Spoon to design, execute, and manage transformations effectively. Learn to connect to and process data from diverse data sources, including files and databases. Develop skills in transforming data using various techniques provided by PDI to create meaningful outcomes. Master creating jobs to sequence tasks and automate data workflows efficiently. Author(s) The author, Carina Roldán, is an experienced professional specializing in data integration and ETL processes using Pentaho tools. She leverages her extensive experience to craft this book with clarity and accessibility, making it simple for readers to grasp key concepts. Her instructional style is straightforward and geared toward minimizing beginners' challenges. Who is it for? This book is ideal for data analysts, business intelligence developers, and software engineers who want to utilize Pentaho Data Integration for ETL tasks and data workflows. No prior in-depth experience with Pentaho is necessary, but familiarity with basic data concepts is recommended. Readers will benefit most if they are seeking practical skills in data integration to solve real-world problems and streamline data processes.

With "Qlik Sense Cookbook," you will gain practical knowledge to harness the capabilities of Qlik Sense for effective business intelligence. This book is packed with step-by-step recipes that guide you in leveraging this powerful tool's data analytics features to create intuitive interactive dashboards and derive actionable insights. What this Book will help me do Master the process of sourcing, previewing, and distributing data through efficient interactive dashboards. Utilize the latest visualization options and learn best practices for creating impactful visuals. Develop scripts for automation and customize functionality using Qlik Sense subroutines. Enhance your Qlik Sense dashboard with advanced UI customizations and interactive elements. Leverage Qlik Sense's advanced aggregation functions like AGGR to perform multidimensional insights. Author(s) The authors of "Qlik Sense Cookbook" bring years of professional expertise in business intelligence and analytics. They have extensive experience working with Qlik platforms and have authored numerous industry-relevant resources. With a practical and accessible writing style, they thrive in breaking down complex concepts into manageable, actionable knowledge. Who is it for? This book is perfect for data analysts, business intelligence specialists, and Qlik Sense practitioners who want to advance their skills. It's suitable for beginners aiming to develop proficiency in Qlik Sense, as well as for professionals experienced with other tools like QlikView. Basic business intelligence knowledge is recommended for getting the most out of this book.

Business Analytics: A Data-Driven Decision Making Approach for Business-Part I,/i> provides an overview of business analytics (BA), business intelligence (BI), and the role and importance of these in the modern business decision-making. The book discusses all these areas along with three main analytics categories: (1) descriptive, (2) predictive, and (3) prescriptive analytics with their tools and applications in business. This volume focuses on descriptive analytics that involves the use of descriptive and visual or graphical methods, numerical methods, as well as data analysis tools, big data applications, and the use of data dashboards to understand business performance. The highlights of this volume are: Business analytics at a glance; Business intelligence (BI), data analytics; Data, data types, descriptive analytics; Data visualization tools; Data visualization with big data; Descriptive analytics-numerical methods; Case analysis with computer applications.

Introduces the basic concepts of FEM in an easy-to-use format so that students and professionals can use the method efficiently and interpret results properly Finite element method (FEM) is a powerful tool for solving engineering problems both in solid structural mechanics and fluid mechanics. This book presents all of the theoretical aspects of FEM that students of engineering will need. It eliminates overlong math equations in favour of basic concepts, and reviews of the mathematics and mechanics of materials in order to illustrate the concepts of FEM. It introduces these concepts by including examples using six different commercial programs online. The all-new, second edition of Introduction to Finite Element Analysis and Design provides many more exercise problems than the first edition. It includes a significant amount of material in modelling issues by using several practical examples from engineering applications. The book features new coverage of buckling of beams and frames and extends heat transfer analyses from 1D (in the previous edition) to 2D. It also covers 3D solid element and its application, as well as 2D. Additionally, readers will find an increase in coverage of finite element analysis of dynamic problems. There is also a companion website with examples that are concurrent with the most recent version of the commercial programs. Offers elaborate explanations of basic finite element procedures Delivers clear explanations of the capabilities and limitations of finite element analysis Includes application examples and tutorials for commercial finite element software, such as MATLAB, ANSYS, ABAQUS and NASTRAN Provides numerous examples and exercise problems Comes with a complete solution manual and results of several engineering design projects Introduction to Finite Element Analysis and Design, 2nd Edition is an excellent text for junior and senior level undergraduate students and beginning graduate students in mechanical, civil, aerospace, biomedical engineering, industrial engineering and engineering mechanics.

The first book to discuss robust aspects of nonlinear regression—with applications using R software Robust Nonlinear Regression: with Applications using R covers a variety of theories and applications of nonlinear robust regression. It discusses both parts of the classic and robust aspects of nonlinear regression and focuses on outlier effects. It develops new methods in robust nonlinear regression and implements a set of objects and functions in S-language under SPLUS and R software. The software covers a wide range of robust nonlinear fitting and inferences, and is designed to provide facilities for computer users to define their own nonlinear models as an object, and fit models using classic and robust methods as well as detect outliers. The implemented objects and functions can be applied by practitioners as well as researchers. The book offers comprehensive coverage of the subject in 9 chapters: Theories of Nonlinear Regression and Inference; Introduction to R; Optimization; Theories of Robust Nonlinear Methods; Robust and Classical Nonlinear Regression with Autocorrelated and Heteroscedastic errors; Outlier Detection; R Packages in Nonlinear Regression; A New R Package in Robust Nonlinear Regression; and Object Sets. The first comprehensive coverage of this field covers a variety of both theoretical and applied topics surrounding robust nonlinear regression Addresses some commonly mishandled aspects of modeling R packages for both classical and robust nonlinear regression are presented in detail in the book and on an accompanying website Robust Nonlinear Regression: with Applications using R is an ideal text for statisticians, biostatisticians, and statistical consultants, as well as advanced level students of statistics.

Examine business problems and use a practical analytical approach to solve them by implementing predictive models and machine learning techniques using SAS and the R analytical language. This book is ideal for those who are well-versed in writing code and have a basic understanding of statistics, but have limited experience in implementing predictive models and machine learning techniques for analyzing real world data. The most challenging part of solving industrial business problems is the practical and hands-on knowledge of building and deploying advanced predictive models and machine learning algorithms. Applied Analytics through Case Studies Using SAS and R is your answer to solving these business problems by sharpening your analytical skills. What You'll Learn Understand analytics and basic data concepts Use an analytical approach to solve Industrial business problems Build predictive model with machine learning techniques Create and apply analytical strategies Who This Book Is For Data scientists, developers, statisticians, engineers, and research students with a great theoretical understanding of data and statistics who would like to enhance their skills by getting practical exposure in data modeling.

Navigate the fascinating intersection of healthcare and data science with the book "Healthcare Analytics Made Simple." This comprehensive guide empowers you to use Python and machine learning techniques to analyze and improve real healthcare systems. Demystify intricate concepts with Python code and SQL to gain actionable insights and build predictive models for healthcare. What this Book will help me do Understand healthcare incentives, policies, and datasets to ground your analysis in practical knowledge. Master the use of Python libraries and SQL for healthcare data analysis and visualization. Develop skills to apply machine learning for predictive and descriptive analytics in healthcare. Learn to assess quality metrics and evaluate provider performance using robust tools. Get acquainted with upcoming trends and future applications in healthcare analytics. Author(s) The authors, None Kumar and None Khader, are experts in data science and healthcare informatics. They bring years of experience teaching, researching, and applying data analytics in healthcare. Their approach is hands-on and clear, aiming to make complex topics accessible and engaging for their audience. Who is it for? This book is perfect for data science professionals eager to specialize in healthcare analytics. Additionally, clinicians aiming to leverage computing and data analytics in improving healthcare processes will find valuable insights. Programming enthusiasts and students keen to enter healthcare analytics will also greatly benefit. Tailored for beginners in this field, it is an educational yet robust resource.

Mastering Kibana 6.x is your guide to leveraging Kibana for creating impactful data visualizations and insightful dashboards. From setting up basic visualizations to exploring advanced analytics and machine learning integrations, this book equips you with the necessary skills to dive deep into your data and gain actionable insights at scale. You'll also learn to effectively manage and monitor data with powerful tools such as X-Pack and Beats. What this Book will help me do Build sophisticated dashboards to visualize elastic stack data effectively. Understand and utilize Timelion expressions for analyzing time series data. Incorporate X-Pack capabilities to enhance security and monitoring in Kibana. Extract, analyze, and visualize data from Elasticsearch for advanced analytics. Set up monitoring and alerting using Beats components for reliable data operations. Author(s) With extensive experience in big data technologies, the author brings a practical approach to teaching advanced Kibana topics. Having worked on real-world data analytics projects, their aim is to make complex concepts accessible while showing how to tackle analytics challenges using Kibana. Who is it for? This book is ideal for data engineers, DevOps professionals, and data scientists who want to optimize large-scale data visualizations. If you're looking to manage Elasticsearch data through insightful dashboards and visual analytics, or enhance your data operations with features like machine learning, then this book is perfect for you. A basic understanding of the Elastic Stack is helpful, though not required.

Uncover the power of Microsoft Power BI with this accessible and practical guide. This book introduces you to the concepts of data modeling, transformation, and visualization, ensuring that you can build effective dashboards and gain valuable insights. You'll be empowered to productively utilize Power BI in your organization to achieve your analytics goals. What this Book will help me do Connect to various data sources and harness the capabilities of the Query Editor. Transform and clean data for analysis, learning to use languages like M and R. Build robust data models with relationships and powerful DAX expressions. Create impactful reports with efficient and custom visualizations in Power BI. Deploy and administer Power BI solutions both in the cloud and on-premise. Author(s) The authors, Devin Knight, Mitchell Pearson, and Manuel Quintana, are seasoned experts in Business Intelligence and Power BI. They bring years of experience simplifying complex data challenges. Their writing is approachable and hands-on, equipping readers with the skills to solve real-world problems. Who is it for? This book is perfectly suited for professionals in Business Intelligence roles, data analysts, or those aiming to adopt Power BI solutions. Whether you're new to Power BI or have basic BI knowledge, this guide will take you from fundamentals to advanced implementations. Ideal for anyone aiming to unlock actionable insights from their data.

As the impact of data science continues to grow on society there is an increased need to discuss how data is appropriately used and how to address misuse. Yet, ethical principles for working with data have been available for decades. The real issue today is how to put those principles into action. With this report, authors Mike Loukides, Hilary Mason, and DJ Patil examine practical ways for making ethical data standards part of your work every day. To help you consider all of possible ramifications of your work on data projects, this report includes: A sample checklist that you can adapt for your own procedures Five framing guidelines (the Five C’s) for building data products: consent, clarity, consistency, control, and consequences Suggestions for building ethics into your data-driven culture Now is the time to invest in a deliberate practice of data ethics, for better products, better teams, and better outcomes. Get a copy of this report and learn what it takes to do good data science today.

Explains how to increase the modularity, flexibility, and maintainability of your SAS code using the SAS macro facility. Provides complete information about macro language elements, interfaces between the SAS macro facility and other parts of SAS software, and macro processing in general.