Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Each chapter starts with conceptual background information about the techniques, includes multiple examples using R to achieve results, and concludes with a case study. Written by Matt and Joshua F. Wiley, Advanced R Statistical Programming and Data Models shows you how to conduct data analysis using the popular R language. You’ll delve into the preconditions or hypothesis for various statistical tests and techniques and work through concrete examples using R for a variety of these next-level analytics. This is a must-have guide and reference on using and programming with the R language. What You’ll Learn Conduct advanced analyses in R including: generalized linear models, generalized additive models, mixedeffects models, machine learning, and parallel processing Carry out regression modeling using R data visualization, linear and advanced regression, additive models, survival / time to event analysis Handle machine learning using R including parallel processing, dimension reduction, and feature selection and classification Address missing data using multiple imputation in R Work on factor analysis, generalized linear mixed models, and modeling intraindividual variability Who This Book Is For Working professionals, researchers, or students who are familiar with R and basic statistical techniques such as linear regression and who want to learn how to use R to perform more advanced analytics. Particularly, researchers and data analysts in the social sciences may benefit from these techniques. Additionally, analysts who need parallel processing to speed up analytics are givenproven code to reduce time to result(s).

Discover the ultimate guide to Tableau 2019.x that offers over 115 practical recipes to tackle business intelligence and data analysis challenges. This book takes you from the basics to advanced techniques, empowering you to create insightful dashboards, leverage powerful analytics, and seamlessly integrate with modern cloud data platforms. What this Book will help me do Master both basic and advanced functionalities of Tableau Desktop to effectively analyze and visualize data. Understand how to create impactful dashboards and compelling data stories for drive decision-making. Deploy advanced analytical tools including R-based forecasting and statistical techniques with Tableau. Set up and utilize Tableau Server in multi-node environments on Linux and Windows. Utilize Tableau Prep to efficiently clean, shape, and transform data for seamless integration into Tableau workflows. Author(s) The authors of the Tableau 2019.x Cookbook are recognized industry professionals with rich expertise in business intelligence, data analytics, and Tableau's ecosystem. Dmitry Anoshin and his co-authors bring hands-on experience from various industries to provide actionable insights. They focus on delivering practical solutions through structured learning paths. Who is it for? This book is tailored for data analysts, BI developers, and professionals equipped with some knowledge of Tableau wanting to enhance their skills. If you're aiming to solve complex analytics challenges or want to fully utilize the capabilities of Tableau products, this book offers the guidance and knowledge you need.

Dive into the intricacies of data science with 'Principles of Data Science'. This book takes you on a journey to explore, analyze, and transform data into actionable insights using mathematical models, Python programming, and machine learning concepts. With a clear and engaging style, you will progress from understanding theoretical foundations to implementing advanced techniques in real-world scenarios. What this Book will help me do Master the five critical steps in a practical data science workflow. Clean and prepare raw datasets for accurate machine learning models. Understand and apply statistical models and mathematical principles for data analysis. Build and evaluate predictive models using Python and effective metrics. Create impactful visualizations that clearly convey data insights. Author(s) Sinan Ozdemir is an expert in data science, with a background in developing and teaching advanced courses in machine learning and predictive analytics. With co-authors None Kakade and None Tibaldeschi, they bring years of hands-on experience in data science to this comprehensive guide. Their approach simplifies complex concepts, making them accessible without sacrificing depth, to empower readers to make data-driven decisions confidently. Who is it for? This book is ideal for aspiring data scientists seeking a practical introduction to the field. It's perfect for those with basic math skills looking to apply them to data science or experienced programmers who want to explore the mathematical foundation of data science. A basic understanding of Python programming will be invaluable, but the book builds up core concepts step-by-step, making it accessible to both beginners and experienced professionals.

Explore and understand data with the powerful data visualization techniques of Tableau, and then communicate insights in powerful ways Key Features Apply best practices in data visualization and chart types exploration Explore the latest version of Tableau Desktop with hands-on examples Understand the fundamentals of Tableau storytelling Book Description Graphical presentation of data enables us to easily understand complex data sets. Tableau 10 Complete Reference provides easy-to-follow recipes with several use cases and real-world business scenarios to get you up and running with Tableau 10. This Learning Path begins with the history of data visualization and its importance in today's businesses. You'll also be introduced to Tableau - how to connect, clean, and analyze data in this visual analytics software. Then, you'll learn how to apply what you've learned by creating some simple calculations in Tableau and using Table Calculations to help drive greater analysis from your data. Next, you'll explore different advanced chart types in Tableau. These chart types require you to have some understanding of the Tableau interface and understand basic calculations. You'll study in detail all dashboard techniques and best practices. A number of recipes specifically for geospatial visualization, analytics, and data preparation are also covered. Last but not least, you'll learn about the power of storytelling through the creation of interactive dashboards in Tableau. Through this Learning Path, you will gain confidence and competence to analyze and communicate data and insights more efficiently and effectively by creating compelling interactive charts, dashboards, and stories in Tableau. This Learning Path includes content from the following Packt products: Learning Tableau 10 - Second Edition by Joshua N. Milligan Getting Started with Tableau 2018.x by Tristan Guillevin What you will learn Build effective visualizations, dashboards, and story points Build basic to more advanced charts with step-by-step recipes Become familiar row-level, aggregate, and table calculations Dig deep into data with clustering and distribution models Prepare and transform data for analysis Leverage Tableau's mapping capabilities to visualize data Use data storytelling techniques to aid decision making strategy Who this book is for Tableau 10 Complete Reference is designed for anyone who wants to understand their data better and represent it in an effective manner. It is also used for BI professionals and data analysts who want to do better at their jobs. Downloading the example code for this book You can download the example code files for all Packt books you have purchased from your account at http://www.PacktPub.com. If you purchased this book elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you.

Design, develop, and master efficient Power BI solutions for impactful business insights Key Features Get to grips with the fundamentals of Microsoft Power BI Combine data from multiple sources, create visuals, and publish reports across platforms Understand Power BI concepts with real-world use cases Book Description Microsoft Power BI Complete Reference Guide gets you started with business intelligence by showing you how to install the Power BI toolset, design effective data models, and build basic dashboards and visualizations that make your data come to life. In this Learning Path, you will learn to create powerful interactive reports by visualizing your data and learn visualization styles, tips and tricks to bring your data to life. You will be able to administer your organization's Power BI environment to create and share dashboards. You will also be able to streamline deployment by implementing security and regular data refreshes. Next, you will delve deeper into the nuances of Power BI and handling projects. You will get acquainted with planning a Power BI project, development, and distribution of content, and deployment. You will learn to connect and extract data from various sources to create robust datasets, reports, and dashboards. Additionally, you will learn how to format reports and apply custom visuals, animation and analytics to further refine your data. By the end of this Learning Path, you will learn to implement the various Power BI tools such as on-premises gateway together along with staging and securely distributing content via apps. This Learning Path includes content from the following Packt products: Microsoft Power BI Quick Start Guide by Devin Knight et al. Mastering Microsoft Power BI by Brett Powell What you will learn Connect to data sources using both import and DirectQuery options Leverage built-in and custom visuals to design effective reports Administer a Power BI cloud tenant for your organization Deploy your Power BI Desktop files into the Power BI Report Server Build efficient data retrieval and transformation processes Who this book is for Microsoft Power BI Complete Reference Guide is for those who want to learn and use the Power BI features to extract maximum information and make intelligent decisions that boost their business. If you have a basic understanding of BI concepts and want to learn how to apply them using Microsoft Power BI, then Learning Path is for you. It consists of real-world examples on Power BI and goes deep into the technical issues, covers additional protocols, and much more.

Dive into "Hands-On Data Science with R" and embark on a journey to master the R language for practical data science applications. This comprehensive guide walks through data manipulation, visualization, and advanced analytics, preparing you to tackle real-world data challenges with confidence. What this Book will help me do Understand how to utilize popular R packages effectively for data science tasks. Learn techniques for cleaning, preprocessing, and exploring datasets. Gain insights into implementing machine learning models in R for predictive analytics. Master the use of advanced visualization tools to extract and communicate insights. Develop expertise in integrating R with big data platforms like Hadoop and Spark. Author(s) This book was written by experts in data science and R including Doug Ortiz and his co-authors. They bring years of industry experience and a desire to teach, presenting complex topics in an approachable manner. Who is it for? Designed for data analysts, statisticians, or programmers with basic R knowledge looking to dive into machine learning and predictive analytics. If you're aiming to enhance your skill set or gain confidence in tackling real-world data problems, this book is an excellent choice.

Learn the basics of Data Science through an easy to understand conceptual framework and immediately practice using RapidMiner platform. Whether you are brand new to data science or working on your tenth project, this book will show you how to analyze data, uncover hidden patterns and relationships to aid important decisions and predictions. Data Science has become an essential tool to extract value from data for any organization that collects, stores and processes data as part of its operations. This book is ideal for business users, data analysts, business analysts, engineers, and analytics professionals and for anyone who works with data. You’ll be able to: Gain the necessary knowledge of different data science techniques to extract value from data. Master the concepts and inner workings of 30 commonly used powerful data science algorithms. Implement step-by-step data science process using using RapidMiner, an open source GUI based data science platform Data Science techniques covered: Exploratory data analysis, Visualization, Decision trees, Rule induction, k-nearest neighbors, Naïve Bayesian classifiers, Artificial neural networks, Deep learning, Support vector machines, Ensemble models, Random forests, Regression, Recommendation engines, Association analysis, K-Means and Density based clustering, Self organizing maps, Text mining, Time series forecasting, Anomaly detection, Feature selection and more... Contains fully updated content on data science, including tactics on how to mine business data for information Presents simple explanations for over twenty powerful data science techniques Enables the practical use of data science algorithms without the need for programming Demonstrates processes with practical use cases Introduces each algorithm or technique and explains the workings of a data science algorithm in plain language Describes the commonly used setup options for the open source tool RapidMiner

Leverage health data into insight! Applied Health Analytics and Informatics Using SAS describes health anamatics, a result of the intersection of data analytics and health informatics. Healthcare systems generate nearly a third of the world’s data, and analytics can help to eliminate medical errors, reduce readmissions, provide evidence-based care, demonstrate quality outcomes, and add cost-efficient care. This comprehensive textbook includes data analytics and health informatics concepts, along with applied experiential learning exercises and case studies using SAS Enterprise MinerTM within the healthcare industry setting. Topics covered include: Sampling and modeling health data – both structured and unstructured Exploring health data quality Developing health administration and health data assessment procedures Identifying future health trends Analyzing high-performance health data mining models Applied Health Analytics and Informatics Using SAS is intended for professionals, lifelong learners, senior-level undergraduates, graduate-level students in professional development courses, health informatics courses, health analytics courses, and specialized industry track courses. This textbook is accessible to a wide variety of backgrounds and specialty areas, including administrators, clinicians, and executives. This book is part of the SAS Press program.

Standardizes the definition and framework of analytics ABOK stands for Analytics Body of Knowledge. Based on the authors’ definition of analytics—which is “a process by which a team of people helps an organization make better decisions (the objective) through the analysis of data (the activity)”— this book from Institute for Operations Research and the Management Sciences (INFORMS) represents the perspectives of some of the most respected experts on analytics. The INFORMS ABOK documents the core concepts and skills with which an analytics professional should be familiar; establishes a dynamic resource that will be used by practitioners to increase their understanding of analytics; and, presents instructors with a framework for developing academic courses and programs in analytics. The INFORMS ABOK offers in-depth insight from peer-reviewed chapters that provide readers with a better understanding of the dynamic field of analytics. Chapters cover: Introduction to Analytics; Getting Started with Analytics; The Analytics Team; The Data; Solution Methodology; Model Building; Machine Learning; Deployment and Life Cycle Management; and The Blossoming Analytics Talent Pool: An Overview of the Analytics Ecosystem. Across industries and academia, readers with various backgrounds in analytics – from novices who are interested in learning more about the basics of analytics to experienced professionals who want a different perspective on some aspect of analytics – will benefit from reading about and implementing the concepts and methods covered by the INFORMS ABOK.

  This book looks at strategies to help companies become more intelligent, connected, and agile. It discusses how companies can define and measure high-impact outcomes and use effectively analytics technology to achieve them. It also looks at the technology needed to implement the analytics necessary to achieve high-impact outcomes—from both analytics tool and technical infrastructure perspective. Also discussed are ancillary, but critical, topics such as data security and governance that may not traditionally be a part of analytics discussions but are essential in helping companies maintain a secure environment for their analytics and access the quality data they need to gain critical insights and drive better decision-making.

Use data analytics to drive innovation and value throughout your network infrastructure Network and IT professionals capture immense amounts of data from their networks. Buried in this data are multiple opportunities to solve and avoid problems, strengthen security, and improve network performance. To achieve these goals, IT networking experts need a solid understanding of data science, and data scientists need a firm grasp of modern networking concepts. Data Analytics for IT Networks fills these knowledge gaps, allowing both groups to drive unprecedented value from telemetry, event analytics, network infrastructure metadata, and other network data sources. Drawing on his pioneering experience applying data science to large-scale Cisco networks, John Garrett introduces the specific data science methodologies and algorithms network and IT professionals need, and helps data scientists understand contemporary network technologies, applications, and data sources. After establishing this shared understanding, Garrett shows how to uncover innovative use cases that integrate data science algorithms with network data. He concludes with several hands-on, Python-based case studies reflecting Cisco Customer Experience (CX) engineers’ supporting its largest customers. These are designed to serve as templates for developing custom solutions ranging from advanced troubleshooting to service assurance. Understand the data analytics landscape and its opportunities in Networking See how elements of an analytics solution come together in the practical use cases Explore and access network data sources, and choose the right data for your problem Innovate more successfully by understanding mental models and cognitive biases Walk through common analytics use cases from many industries, and adapt them to your environment Uncover new data science use cases for optimizing large networks Master proven algorithms, models, and methodologies for solving network problems Adapt use cases built with traditional statistical methods Use data science to improve network infrastructure analysisAnalyze control and data planes with greater sophistication Fully leverage your existing Cisco tools to collect, analyze, and visualize data

How can analytics scholars and healthcare professionals access the most exciting and important healthcare topics and tools for the 21st century? Editors Tinglong Dai and Sridhar Tayur, aided by a team of internationally acclaimed experts, have curated this timely volume to help newcomers and seasoned researchers alike to rapidly comprehend a diverse set of thrusts and tools in this rapidly growing cross-disciplinary field. The Handbook covers a wide range of macro-, meso- and micro-level thrusts—such as market design, competing interests, global health, personalized medicine, residential care and concierge medicine, among others—and structures what has been a highly fragmented research area into a coherent scientific discipline. The handbook also provides an easy-to-comprehend introduction to five essential research tools—Markov decision process, game theory and information economics, queueing games, econometric methods, and data science—by illustrating their uses and applicability on examples from diverse healthcare settings, thus connecting tools with thrusts. The primary audience of the Handbook includes analytics scholars interested in healthcare and healthcare practitioners interested in analytics. This Handbook: Instills analytics scholars with a way of thinking that incorporates behavioral, incentive, and policy considerations in various healthcare settings. This change in perspective—a shift in gaze away from narrow, local and one-off operational improvement efforts that do not replicate, scale or remain sustainable—can lead to new knowledge and innovative solutions that healthcare has been seeking so desperately. Facilitates collaboration between healthcare experts and analytics scholar to frame and tackle their pressing concerns through appropriate modern mathematical tools designed for this very purpose. The handbook is designed to be accessible to the independent reader, and it may be used in a variety of settings, from a short lecture series on specific topics to a semester-long course.

Enjoy reading interviews with more than two dozen data professionals to see a picture of what it’s like to work in the industry managing and analyzing data, helping you to know what it takes to move from your current expertise into one of the fastest growing areas of technology today. Data is the hottest word of the century, and data professionals are in high demand. You may already be a data professional such as a database administrator or business intelligence analyst. Or you may be one of the many people who want to work as a data professional, and are curious how to get there. Either way, this collection helps you understand how data professionals work, what makes them successful, and what they do to keep up. You’ll find interviews in this book with database administrators, database programmers, data architects, business intelligence professionals, and analytics professionals. Interviewees work across industry sectors ranging from healthcare and banking tofinance and transportation and beyond. Each chapter illuminates a successful professional at the top of their game, who shares what helped them get to the top, and what skills and attitudes combine to make them successful in their respective fields. Interviewees in the book include: Mindy Curnutt, Julie Smith, Kenneth Fisher, Andy Leonard, Jes Borland, Kevin Feasel, Ginger Grant, Vicky Harp, Kendra Little, Jason Brimhall, Tim Costello, Andy Mallon, Steph Locke, Jonathan Stewart, Joseph Sack, John Q. Martin, John Morehouse, Kathi Kellenberger, Argenis Fernandez, Kirsten Benzel, Tracy Boggiano, Dave Walden, Matt Gordon, Jimmy May, Drew Furgiuele, Marlon Ribunal, and Joseph Fleming. All of them have been successful in their careers, and share their perspectives on working and succeeding in the field as data and database professionals. What You'll Learn Stand out as an outstanding professional in your area of data work by developing the right set of skills and attitudes that lead to success Avoid common mistakes and pitfalls, and recover from operational failures and bad technology decisions Understand current trends and best practices, and stay out in front as the field evolves Break into working with data through database administration, business intelligence, or any of the other career paths represented in this book Manage stress and develop a healthy work-life balance no matter which career path you decide upon Choose a suitable path for yourself from among the different career paths in working with data Who This Book Is For Database administrators and developers, database and business intelligence architects, consultants, and analytic professionals, as well as those intent on moving into one of those career paths. Aspiring data professionals and those in related technical fields who want to make a move toward managing or analyzing data on a full-time basis will find the book useful. Existing data professionals who want to be outstanding and successful at what they do will also appreciate the book's advice and guidance.

Using Power Query, you can import, reshape, and cleanse any data from a simple interface, so you can mine that data for all of its hidden insights. Power Query is embedded in Excel, Power BI, and other Microsoft products, and leading Power Query expert Gil Raviv will help you make the most of it. Discover how to eliminate time-consuming manual data preparation, solve common problems, avoid pitfalls, and more. Then, walk through several complete analytics challenges, and integrate all your skills in a realistic chapter-length final project. By the time you're finished, you'll be ready to wrangle any data–and transform it into actionable knowledge. Prepare and analyze your data the easy way, with Power Query · Quickly prepare data for analysis with Power Query in Excel (also known as Get & Transform) and in Power BI · Solve common data preparation problems with a few mouse clicks and simple formula edits · Combine data from multiple sources, multiple queries, and mismatched tables · Master basic and advanced techniques for unpivoting tables · Customize transformations and build flexible data mashups with the M formula language · Address collaboration challenges with Power Query · Gain crucial insights into text feeds · Streamline complex social network analytics so you can do it yourself For all information workers, analysts, and any Excel user who wants to solve their own business intelligence problems.

Explore the latest Python tools and techniques to help you tackle the world of data acquisition and analysis. You'll review scientific computing with NumPy, visualization with matplotlib, and machine learning with scikit-learn. This revision is fully updated with new content on social media data analysis, image analysis with OpenCV, and deep learning libraries. Each chapter includes multiple examples demonstrating how to work with each library. At its heart lies the coverage of pandas, for high-performance, easy-to-use data structures and tools for data manipulation Author Fabio Nelli expertly demonstrates using Python for data processing, management, and information retrieval. Later chapters apply what you've learned to handwriting recognition and extending graphical capabilities with the JavaScript D3 library. Whether you are dealing with sales data, investment data, medical data, web page usage, or other data sets, Python Data Analytics, Second Edition is an invaluable reference with its examples of storing, accessing, and analyzing data. What You'll Learn Understand the core concepts of data analysis and the Python ecosystem Go in depth with pandas for reading, writing, and processing data Use tools and techniques for data visualization and image analysis Examine popular deep learning libraries Keras, Theano,TensorFlow, and PyTorch Who This Book Is For Experienced Python developers who need to learn about Pythonic tools for data analysis

Transform your R programming into interactive web applications with "Web Application Development with R Using Shiny." This book takes you step-by-step through creating dynamic user interfaces and web solutions with the R Shiny package, empowering you to build impactful tools that showcase your data. What this Book will help me do Create interactive web applications using R Shiny. Apply JavaScript for added functionality and customization in Shiny apps. Effortlessly deploy Shiny apps online for accessibility. Understand Shiny UI functions to design effective user interfaces. Leverage data visualization techniques for insightful analytics in apps. Author(s) Chris Beeley and Shitalkumar R. Sukhdeve bring their profound expertise in R programming and Shiny development to this book. Chris is an experienced data scientist passionate about interactive data solutions, while Shitalkumar, with a strong computing background, shares his hands-on insights. Their collaborative and tutorial approach ensures learners grasp each concept smoothly. Who is it for? This book is ideal for R programmers eager to transition from static data evaluation to engaging, interactive web applications. It caters to professionals and enthusiasts seeking practical, hands-on coding guidance. Readers should have foundational R programming knowledge, ensuring a smooth transition into Shiny concepts.

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.

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.

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.

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.

It’s estimated that 80 percent of an organization’s data contains location attributes, but many don’t understand how to unlock the potential of this data for their organizations to make better decisions. You have just been handed the keys by finding this book. Readers will unlock these methods by learning about location analytics as well as taking a deep dive into the Planned Grocery® platform created in part by the author. The Planned Grocery® location analytics platform has been mentioned in the Wall Street Journal (twice), Forbes, Bloomberg, and Business Insider. A sampling of clients of Planned Grocery® include: Philips Edison and Company, Just Fresh, Slate Retail REIT, Wegmans, and Whole Foods. The practical information in this book is designed to prepare you to recognize and take advantage of situations where you and your organization can become more successful using location analytics. This will be accomplished by taking you through an explanation of the fundamentals of location analytics, by looking at various case studies, by learning how to identify and analyze spatial data sets, and by learning about the companies that are doing interesting work in this space.