Introduces professionals and scientists to statistics and machine learning using the programming language R Written by and for practitioners, this book provides an overall introduction to R, focusing on tools and methods commonly used in data science, and placing emphasis on practice and business use. It covers a wide range of topics in a single volume, including big data, databases, statistical machine learning, data wrangling, data visualization, and the reporting of results. The topics covered are all important for someone with a science/math background that is looking to quickly learn several practical technologies to enter or transition to the growing field of data science. The Big R-Book for Professionals: From Data Science to Learning Machines and Reporting with R includes nine parts, starting with an introduction to the subject and followed by an overview of R and elements of statistics. The third part revolves around data, while the fourth focuses on data wrangling. Part 5 teaches readers about exploring data. In Part 6 we learn to build models, Part 7 introduces the reader to the reality in companies, Part 8 covers reports and interactive applications and finally Part 9 introduces the reader to big data and performance computing. It also includes some helpful appendices. Provides a practical guide for non-experts with a focus on business users Contains a unique combination of topics including an introduction to R, machine learning, mathematical models, data wrangling, and reporting Uses a practical tone and integrates multiple topics in a coherent framework Demystifies the hype around machine learning and AI by enabling readers to understand the provided models and program them in R Shows readers how to visualize results in static and interactive reports Supplementary materials includes PDF slides based on the book’s content, as well as all the extracted R-code and is available to everyone on a Wiley Book Companion Site The Big R-Book is an excellent guide for science technology, engineering, or mathematics students who wish to make a successful transition from the academic world to the professional. It will also appeal to all young data scientists, quantitative analysts, and analytics professionals, as well as those who make mathematical models.

Elevate your storytelling with SAS Visual Analytics Data visualization is the gateway to artificial intelligence (AI) and big data. Insightful Data Visualization with SAS Viya shows how the latest SAS Viya tools can be used to create data visualizations in an easier, smarter, and more engaging way than ever before. SAS Visual Analytics combined with human creativity can produce endless possibilities. In this book, you will learn tips and techniques for getting the most from your SAS Visual Analytics investment. From beginners to advanced SAS users, this book has something for everyone. Use AI wizards to create data visualization automatically, learn to use advanced analytics in your dashboards to surface smarter insights, and learn to extend SAS Visual Analytics with advanced integrations and options. Topics covered in this book include: SAS Visual Analytics Data visualization with SAS Reports and dashboards SAS code examples Self-service analytics SAS data access Extending SAS beyond drag and drop

Discrete Networked Dynamic Systems: Analysis and Performance provides a high-level treatment of a general class of linear discrete-time dynamic systems interconnected over an information network, exchanging relative state measurements or output measurements. It presents a systematic analysis of the material and provides an account to the math development in a unified way. The topics in this book are structured along four dimensions: Agent, Environment, Interaction, and Organization, while keeping global (system-centered) and local (agent-centered) viewpoints. The focus is on the wide-sense consensus problem in discrete networked dynamic systems. The authors rely heavily on algebraic graph theory and topology to derive their results. It is known that graphs play an important role in the analysis of interactions between multiagent/distributed systems. Graph-theoretic analysis provides insight into how topological interactions play a role in achieving coordination among agents. Numerous types of graphs exist in the literature, depending on the edge set of G. A simple graph has no self-loop or edges. Complete graphs are simple graphs with an edge connecting any pair of vertices. The vertex set in a bipartite graph can be partitioned into disjoint non-empty vertex sets, whereby there is an edge connecting every vertex in one set to every vertex in the other set. Random graphs have fixed vertex sets, but the edge set exhibits stochastic behavior modeled by probability functions. Much of the studies in coordination control are based on deterministic/fixed graphs, switching graphs, and random graphs. This book addresses advanced analytical tools for characterization control, estimation and design of networked dynamic systems over fixed, probabilistic and time-varying graphs Provides coherent results on adopting a set-theoretic framework for critically examining problems of the analysis, performance and design of discrete distributed systems over graphs Deals with both homogeneous and heterogeneous systems to guarantee the generality of design results

Learn how to use R 4, write and save R scripts, read in and write out data files, use built-in functions, and understand common statistical methods. This in-depth tutorial includes key R 4 features including a new color palette for charts, an enhanced reference counting system (useful for big data), and new data import settings for text (as well as the statistical methods to model text-based, categorical data). Each chapter starts with a list of learning outcomes and concludes with a summary of any R functions introduced in that chapter, along with exercises to test your new knowledge. The text opens with a hands-on installation of R and CRAN packages for both Windows and macOS. The bulk of the book is an introduction to statistical methods (non-proof-based, applied statistics) that relies heavily on R (and R visualizations) to understand, motivate, and conduct statistical tests and modeling. Beginning R 4 shows the use of R in specific cases such as ANOVA analysis, multiple and moderated regression, data visualization, hypothesis testing, and more. It takes a hands-on, example-based approach incorporating best practices with clear explanations of the statistics being done. You will: Acquire and install R and RStudio Import and export data from multiple file formats Analyze data and generate graphics (including confidence intervals) Interactively conduct hypothesis testing Code multiple and moderated regression solutions Who This Book Is For Programmers and data analysts who are new to R. Some prior experience in programming is recommended.

"Mastering SAS Programming for Data Warehousing" dives into the effective use of SAS for handling large-scale data environments like data warehouses and data lakes. You will learn to design and manage ETL processes using SAS, standardize workflows with macros and arrays, and connect SAS to other systems to enhance reporting and data visualization. What this Book will help me do Master efficient data input/output management in SAS environments. Design and maintain robust ETL pipelines using SAS macros and arrays. Identify and address data warehouse user requirements. Utilize Output Delivery System (ODS) to create professional reports. Integrate SAS with external systems for optimized data processing. Author(s) Monika Wahi brings extensive SAS programming experience coupled with a strong background in data warehousing and data analysis. Her insightful approach demystifies complex topics, focusing on equipping readers with practical skills. Her collaborative writing style makes advanced concepts accessible and applicable to real-world scenarios. Who is it for? This book is designed for data professionals such as architects, managers leading data-intensive projects, and SAS programmers or developers. It's ideal for those with foundational SAS experience who aspire to manage, maintain, or develop data lakes, marts, or warehouses effectively. The book offers a logical progression from basic concepts to advanced implementations, tailored for ambitious learners.

This easy-to-follow guide provides R and Python recipes to help you learn and apply the top languages in the field of data analytics to your work in Microsoft Power BI. Data analytics expert and author Ryan Wade shows you how to use R and Python to perform tasks that are extremely hard, if not impossible, to do using native Power BI tools. For example, you will learn to score Power BI data using custom data science models and powerful models from Microsoft Cognitive Services. The R and Python languages are powerful complements to Power BI. They enable advanced data transformation techniques that are difficult to perform in Power BI in its default configuration but become easier by leveraging the capabilities of R and Python. If you are a business analyst, data analyst, or a data scientist who wants to push Power BI and transform it from being just a business intelligence tool into an advanced data analytics tool, then this is the book to help you do that. What You Will Learn Create advanced data visualizations via R using the ggplot2 package Ingest data using R and Python to overcome some limitations of Power Query Apply machine learning models to your data using R and Python without the need of Power BI premium capacity Incorporate advanced AI in Power BI without the need of Power BI premium capacity via Microsoft Cognitive Services, IBM Watson Natural Language Understanding, and pre-trained models in SQL Server Machine Learning Services Perform advanced string manipulations not otherwise possible in Power BI using R and Python Who This Book Is For Power users, data analysts, and data scientists who want to go beyond Power BI’s built-in functionality to create advanced visualizations, transform data in ways not otherwise supported, and automate data ingestion from sources such as SQL Server and Excel in a more concise way

Predictive Intelligence in Biomedical and Health Informatics focuses on imaging, computer-aided diagnosis and therapy as well as intelligent biomedical image processing and analysis. It develops computational models, methods and tools for biomedical engineering related to computer-aided diagnostics (CAD), computer-aided surgery (CAS), computational anatomy and bioinformatics. Large volumes of complex data are often a key feature of biomedical and engineering problems and computational intelligence helps to address such problems. Practical and validated solutions to hard biomedical and engineering problems can be developed by the applications of neural networks, support vector machines, reservoir computing, evolutionary optimization, biosignal processing, pattern recognition methods and other techniques to address complex problems of the real world.

This book includes discussions related to solutions of such tasks as: probabilistic description of the investment function; recovering the income function from GDP estimates; development of models for the economic cycles; selecting the time interval of pseudo-stationarity of cycles; estimating characteristics/parameters of cycle models; analysis of accuracy of model factors. All of the above constitute the general principles of a theory explaining the phenomenon of economic cycles and provide mathematical tools for their quantitative description. The introduced theory is applicable to macroeconomic analyses as well as econometric estimations of economic cycles.

The subject of Discrete Signals and Systems is broad and deserves a single book devoted to it. The objective of this textbook is to present all the required material that an undergraduate student will need to master this subject matter and the use of MATLAB.

Become a self-sufficient citizen developer by learning the tools within the Microsoft Power Platform and how they can be used together to drive change and multiply your productivity. Learn about PowerApps for building applications, Power Automate for automating business processes across those applications, and Power BI for analyzing results and communicating business intelligence through compelling visuals. By understanding the purpose and capabilities of these tools, you will be able to enhance your organization’s visibility into key areas and make informed business decisions in a timely matter. This book is divided into four parts and begins in Part I by showing you how to build applications through PowerApps. You will learn about screens and controls, application sharing and administration, and how to make your applications accessible from mobile devices such as phones and tablets. Part II is about creating workflows using Power Automate that implement business logic across your applications. Part III brings in dashboards and data analysis, showing you how to connect to a data source, cleanse the data from that source, and drive decision making through interactive reports and storytelling. Part IV brings together all the pieces by showing the integrations that are possible when all three tools are combined into a single solution. What You Will Learn Understand the need for the citizen developer in today’s business environment Organize and plan the building of line-of-business applications with PowerApps solutions Replace wasteful paper processes with automated applications built in PowerApps Automate workflows across processes with Power Automate Communicate analytical results through visualizations and storytelling Integrate PowerApps, Power Automate, and Power BI into solutions that multiply productivity Who This Book Is For Power users and analysts with strong Excel skills who need a more comprehensive set of tools that can better help them accomplish their vision on projects, those familiar with one of the Power Platform tools who wish to learn how all three can fit together, and those who are seen as as “rogue IT” problem solvers who get things done when others have tried but failed

Find your dream job with this handy and informative reference guide, packed with accessible advice on over 300 positions, including details on entry routes, qualifications, salary expectations and useful contacts.

Create good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data. Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results. Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed. This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected. What You Will Learn Be aware of the principles of creating and collecting data Know the basic data types and representations Select data types, anticipating analysis goals Understand dataset structures and practices for analyzing and sharing Be guided by examples and use cases (good and bad) Use cleaning tools and methods to create good data Who This Book Is For Researchers who design studies and collect data and subsequently conduct and report the results of their analyses can use the best practices in this book to produce better descriptions and interpretations of their work. In addition, data analysts who explore and explain data of other researchers will be able to create better datasets.

Do you want to create data analysis reports without writing a line of code? This book introduces SAS Studio, a free data science web browser-based product for educational and non-commercial purposes. The power of SAS Studio comes from its visual point-and-click user interface that generates SAS code. It is easier to learn SAS Studio than to learn R and Python to accomplish data cleaning, statistics, and visualization tasks. The book includes a case study about analyzing the data required for predicting the results of presidential elections in the state of Maine for 2016 and 2020. In addition to the presidential elections, the book provides real-life examples including analyzing stocks, oil and gold prices, crime, marketing, and healthcare. You will see data science in action and how easy it is to perform complicated tasks and visualizations in SAS Studio.You will learn, step-by-step, how to do visualizations, including maps. In most cases, you will not need a line of code as you work with the SAS Studio graphical user interface. The book includes explanations of the code that SAS Studio generates automatically. You will learn how to edit this code to perform more complicated advanced tasks. The book introduces you to multiple SAS products such as SAS Viya, SAS Analytics, and SAS Visual Statistics. What You Will Learn Become familiar with SAS Studio IDE Understand essential visualizations Know the fundamental statistical analysis required in most data science and analytics reports Clean the most common data set problems Use linear progression for data prediction Write programs in SAS Get introduced to SAS-Viya, which is more potent than SAS studio Who This Book Is For A general audience of people who are new to data science, students, and data analysts and scientists who are experiencedbut new to SAS. No programming or in-depth statistics knowledge is needed.

This guide shows how to combine data science with social science to gain unprecedented insight into customer behavior, so you can change it. Joanne Rodrigues-Craig bridges the gap between predictive data science and statistical techniques that reveal why important things happen -- why customers buy more, or why they immediately leave your site -- so you can get more behaviors you want and less you don’t. Drawing on extensive enterprise experience and deep knowledge of demographics and sociology, Rodrigues-Craig shows how to create better theories and metrics, so you can accelerate the process of gaining insight, altering behavior, and earning business value. You’ll learn how to: Develop complex, testable theories for understanding individual and social behavior in web products Think like a social scientist and contextualize individual behavior in today’s social environments Build more effective metrics and KPIs for any web product or system Conduct more informative and actionable A/B tests Explore causal effects, reflecting a deeper understanding of the differences between correlation and causation Alter user behavior in a complex web product Understand how relevant human behaviors develop, and the prerequisites for changing them Choose the right statistical techniques for common tasks such as multistate and uplift modeling Use advanced statistical techniques to model multidimensional systems Do all of this in R (with sample code available in a separate code manual)

Metabase Up and Running is your go-to guide for mastering Metabase, the open-source business intelligence tool. You'll progress from the basics of installation and setup to connecting data sources and creating insightful visualizations and dashboards. By the end, you'll be confident in implementing Metabase in your organization for impactful decision-making. What this Book will help me do Understand how to securely deploy and configure Metabase on Amazon Web Services. Master the creation of dashboards, reports, and visual visualizations using Metabase's tools. Gain expertise in user and permissions management within Metabase. Learn to use Metabase's SQL console for advanced database interactions. Acquire skills to embed Metabase within applications and automate reports via email or Slack. Author(s) None Abraham, an experienced tool specialist, is passionate about teaching others how to leverage data tools effectively. With a background in business analytics, Abraham has guided companies of all sizes. Their approachable writing style ensures a learning journey that is both informative and engaging. Who is it for? This book is ideal for business analysts and data professionals looking to amplify their business intelligence capabilities using Metabase. Readers should have some understanding of data analytics principles. Whether you're starting in analytics or seeking advanced automation, this book offers valuable guidance to meet your goals.

O ffers a clear view of the utility and place for survey data within the broader Big Data ecosystem This book presents a collection of snapshots from two sides of the Big Data perspective. It assembles an array of tangible tools, methods, and approaches that illustrate how Big Data sources and methods are being used in the survey and social sciences to improve official statistics and estimates for human populations. It also provides examples of how survey data are being used to evaluate and improve the quality of insights derived from Big Data. Big Data Meets Survey Science: A Collection of Innovative Methods shows how survey data and Big Data are used together for the benefit of one or more sources of data, with numerous chapters providing consistent illustrations and examples of survey data enriching the evaluation of Big Data sources. Examples of how machine learning, data mining, and other data science techniques are inserted into virtually every stage of the survey lifecycle are presented. Topics covered include: Total Error Frameworks for Found Data; Performance and Sensitivities of Home Detection on Mobile Phone Data; Assessing Community Wellbeing Using Google Street View and Satellite Imagery; Using Surveys to Build and Assess RBS Religious Flag; and more. Presents groundbreaking survey methods being utilized today in the field of Big Data Explores how machine learning methods can be applied to the design, collection, and analysis of social science data Filled with examples and illustrations that show how survey data benefits Big Data evaluation Covers methods and applications used in combining Big Data with survey statistics Examines regulations as well as ethical and privacy issues Big Data Meets Survey Science: A Collection of Innovative Methods is an excellent book for both the survey and social science communities as they learn to capitalize on this new revolution. It will also appeal to the broader data and computer science communities looking for new areas of application for emerging methods and data sources.

For self-service data preparation, Tableau Prep is relatively easy to use—as long as you know how to clean and organize your datasets. Carl Allchin, from The Information Lab in London, gets you up to speed on Tableau Prep through a series of practical lessons that include methods for preparing, cleaning, automating, organizing, and outputting your datasets. Based on Allchin’s popular blog, Preppin’ Data, this practical guide takes you step-by-step through Tableau Prep’s fundamentals. Self-service data preparation reduces the time it takes to complete data projects and improves the quality of your analyses. Discover how Tableau Prep helps you access your data and turn it into valuable information. Know what to look for when you prepare data Learn which Tableau Prep functions to use when working with data fields Analyze the shape and profile of your dataset Output data for analysis and learn how Tableau Prep automates your workflow Learn how to clean your dataset using Tableau Prep functions Explore ways to use Tableau Prep techniques in real-world scenarios Make your data available to others by managing and documenting the output

This comprehensive guide dives into the intricacies of designing enterprise-grade solutions using Microsoft Power Platform. You'll learn how to leverage the tools in the suite, such as Power Apps, Power Automate, and Power BI, alongside Microsoft 365 and Azure services. By exploring practical examples and methodologies, you will be equipped to handle complex requirements and business challenges. What this Book will help me do Master the integration of Microsoft 365 and Azure components to enhance Power Platform applications. Learn advanced security implementations to safeguard enterprise architecture solutions. Gain proficiency in data migration strategies for robust and seamless data handling across platforms. Understand application lifecycle management and best practices for enterprise-grade Power Platform solutions. Extend and customize Power Platform tools for tailored business applications. Author(s) Robert Rybaric is an experienced enterprise architect specializing in Microsoft technologies. With years of experience crafting complex architecture solutions for dynamic business environments, Robert brings insights that simplify and clarify the challenges of enterprise architecture. His hands-on experience and practical approach make him a trusted voice in the IT solutions space. Who is it for? This book is ideal for enterprise architects and IT decision-makers seeking to use Microsoft Power Platform to address complex business needs. It caters to professionals with a foundational understanding of the platform, aiming to enhance their ability to implement scalable, efficient solutions in competitive business environments.

Before the onset of COVID-19, the healthcare community was already moving to meet the challenges of a growing global population. By collecting record amounts of clinical data electronically and making significant progress on neural network-based AI approaches, the industry now has the potential to build powerful predictive analytics systems. The focus will accelerate the shift from a one-size-fits-all approach to individualized medicine. But several questions remain. What are the plausible outcomes for the world of predictive analytics in both the short and long term? What does the care pathway look like if everything is predicted? And with patient populations and healthcare needs increasing exponentially, how can the industry deliver care in a sustainable and cost-effective way? This comprehensive report, written by Jaquie Finn and Dr. Gavin Troughton with Cambridge Consultants, explores the possibilities. You’ll learn: How predictive analytics plays a part across all stages of the care pathway The foundational enablers for predictive analytics How healthcare economics figure into the equation Predictive analytics and today’s healthcare system The future of predictive analytics in healthcare

Discover how Microsoft Power Automate can transform business processes by enabling you to automate workflows with minimal coding. This book introduces the core concepts and practical applications of workflow automation using Power Automate, making it an essential guide for enhancing productivity and efficiency in digital processes. What this Book will help me do Gain foundational knowledge of Microsoft Power Automate and how its components work together. Build automation flows that integrate with popular Microsoft 365 and third-party applications. Create efficient workflows like automated email processes and file management systems. Learn to enhance workflows with features like approvals, conditions, and triggers. Understand the basics of robotic process automation and artificial intelligence applications. Author(s) Aaron Guilmette leverages his extensive experience with Microsoft 365 to provide practical, user-friendly guides to the platform's tools. His expertise in workflow optimization ensures that readers can efficiently implement the concepts discussed. Aaron's teaching style emphasizes clarity and accessibility, making complex topics understandable for readers of all levels. Who is it for? This book is for technologists, system administrators, and power users eager to learn Microsoft Power Automate from scratch or enhance their workflow capabilities. It is ideal for those familiar with Microsoft 365 looking to streamline business processes. The book aims to help readers achieve automation goals, from simple workflows to more complex solutions.