Good UX is based on evidence. Qualitative evidence, such as user testing and field research, can only get you so far. To get the full picture of how users are engaging with your website or app, you'll need to use quantitative evidence in the form of analytics. This book will show you, step by step, how you can use website and app analytics data to inform design choices and definitively improve user experience. Offering practical guidelines, with plenty of detailed examples, this book covers: why you need to gather analytics data for your UX projects getting set up with analytics tools analyzing data how to find problems in your analytics using analytics to aid user research, measure and report on outcomes By the end of this book, you'll have a strong understanding of the important role analytics plays in the UX process. It will inspire you to take an "analytics first" approach to your UX projects.

An essential guide to healthcare data problems, sources, and solutions Strategies in Biomedical Data Science provides medical professionals with much-needed guidance toward managing the increasing deluge of healthcare data. Beginning with a look at our current top-down methodologies, this book demonstrates the ways in which both technological development and more effective use of current resources can better serve both patient and payer. The discussion explores the aggregation of disparate data sources, current analytics and toolsets, the growing necessity of smart bioinformatics, and more as data science and biomedical science grow increasingly intertwined. You'll dig into the unknown challenges that come along with every advance, and explore the ways in which healthcare data management and technology will inform medicine, politics, and research in the not-so-distant future. Real-world use cases and clear examples are featured throughout, and coverage of data sources, problems, and potential mitigations provides necessary insight for forward-looking healthcare professionals. Big Data has been a topic of discussion for some time, with much attention focused on problems and management issues surrounding truly staggering amounts of data. This book offers a lifeline through the tsunami of healthcare data, to help the medical community turn their data management problem into a solution. Consider the data challenges personalized medicine entails Explore the available advanced analytic resources and tools Learn how bioinformatics as a service is quickly becoming reality Examine the future of IOT and the deluge of personal device data The sheer amount of healthcare data being generated will only increase as both biomedical research and clinical practice trend toward individualized, patient-specific care. Strategies in Biomedical Data Science provides expert insight into the kind of robust data management that is becoming increasingly critical as healthcare evolves.

For one- and two-semester courses in introductory business statistics. Understand Business. Understand Data. The 3rd Edition of Statistics for Business: Decision Making and Analysis emphasizes an application-based approach, in which readers learn how to work with data to make decisions. In this contemporary presentation of business statistics, readers learn how to approach business decisions through a 4M Analytics decision making strategy—motivation, method, mechanics and message—to better understand how a business context motivates the statistical process and how the results inform a course of action. Each chapter includes hints on using Excel, Minitab Express, and JMP for calculations, pointing the reader in the right direction to get started with analysis of data. Also available with MyLab Statistics MyLab™ Statistics from Pearson is the world’s leading online resource for teaching and learning statistics; it integrates interactive homework, assessment, and media in a flexible, easy-to-use format. MyLab Statistics is a course management system that helps individual students succeed. It provides engaging experiences that personalize, stimulate, and measure learning for each student. Tools are embedded to make it easy to integrate statistical software into the course. Note: You are purchasing a standalone product; MyLab™does not come packaged with this content. Students, if interested in purchasing this title with MyLab, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information. If you would like to purchase both the physical text and MyLab, search for: 0134763734 / 9780134763736 Statistics for Business: Decision Making and Analysis, Student Value Edition Plus MyLab Statistics with Pearson eText - Access Card Package, 3/e Package consists of: 0134497260 / 9780134497266 Statistics for Business: Decision Making and Analysis, Student Value Edition 0134748646 / 9780134748641 MyLab Statistics for Business Stats with Pearson eText - Standalone Access Card - for Statistics for Business: Decision Making and Analysis

Mastering Text Mining with R is your go-to guide for learning how to process and analyze textual data using R. Throughout the book, you'll gain the skills necessary to perform data extraction and natural language processing, equipping you with practical applications tailored to real-world scenarios. What this Book will help me do Learn to access and manipulate textual data from various sources using R. Understand text processing techniques and employ them with tools like OpenNLP. Explore methods for text categorization, reduction, and summarization with hands-on exercises. Perform text classification tasks such as sentiment analysis and entity recognition. Build custom applications using text mining techniques and frameworks. Author(s) Ashish Kumar is a seasoned data scientist and software developer with years of experience in text analytics and the R programming language. He has a knack for explaining complex topics in an accessible and practical manner, ideal for learners embracing their text mining journey. Who is it for? This book is for anyone keen on mastering text mining with R. If you're an R programmer, data analyst, or data scientist looking to delve into text analytics, you'll find it ideal. Some familiarity with basic programming and statistics will enhance your experience, but all concepts are introduced clearly and effectively.

Learn the fundamental aspects of the business statistics, data mining, and machine learning techniques required to understand the huge amount of data generated by your organization. This book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. Further, Practical Business Analytics using R helps you understand specific issues faced by organizations and how the solutions to these issues can be facilitated by business analytics. This book will discuss and explore the following through examples and case studies: An introduction to R: data management and R functions The architecture, framework, and life cycle of a business analytics project Descriptive analytics using R: descriptive statistics and data cleaning Data mining: classification, association rules, and clustering Predictive analytics: simple regression, multiple regression, and logistic regression This book includes case studies on important business analytic techniques, such as classification, association, clustering, and regression. The R language is the statistical tool used to demonstrate the concepts throughout the book. What You Will Learn • Write R programs to handle data • Build analytical models and draw useful inferences from them • Discover the basic concepts of data mining and machine learning • Carry out predictive modeling • Define a business issue as an analytical problem Who This Book Is For Beginners who want to understand and learn the fundamentals of analytics using R. Students, managers, executives, strategy and planning professionals, software professionals, and BI/DW professionals.

Leverage the power of visualization in business intelligence and data science to make quicker and better decisions. Use statistics and data mining to make compelling and interactive dashboards. This book will help those familiar with Tableau software chart their journey to being a visualization expert. Pro Tableau demonstrates the power of visual analytics and teaches you how to: Connect to various data sources such as spreadsheets, text files, relational databases (Microsoft SQL Server, MySQL, etc.), non-relational databases (NoSQL such as MongoDB, Cassandra), R data files, etc. Write your own custom SQL, etc. Perform statistical analysis in Tableau using R Use a multitude of charts (pie, bar, stacked bar, line, scatter plots, dual axis, histograms, heat maps, tree maps, highlight tables, box and whisker, etc.) What you'll learn Connect to various data sources such as relational databases (Microsoft SQL Server, MySQL), non-relational databases (NoSQL such as MongoDB, Cassandra), write your own custom SQL, join and blend data sources, etc. Leverage table calculations (moving average, year over year growth, LOD (Level of Detail), etc. Integrate Tableau with R Tell a compelling story with data by creating highly interactive dashboards Who this book is for All levels of IT professionals, from executives responsible for determining IT strategies to systems administrators, to data analysts, to decision makers responsible for driving strategic initiatives, etc. The book will help those familiar with Tableau software chart their journey to a visualization expert.

If you've ever wondered how to bridge the gap between mathematics, programming, and actionable data insights, 'Principles of Data Science' is the guide for you. This book explores the full data science pipeline, providing you with tools and knowledge to transform raw data into impactful decisions. With practical lessons and hands-on tutorials, you'll master the essential skills of a data scientist. What this Book will help me do Understand and apply the five core steps of the data science process. Gain insight into data cleaning, visualization, and effective communication of results. Learn and implement foundational machine learning models using Python or R. Bridge gaps between mathematics, statistics, and programming to solve data-driven problems. Evaluate machine learning models using key metrics for better predictive capabilities. Author(s) The author, a seasoned data scientist with years of professional experience in analytics and software development, brings a rich perspective to the topic. Combining a strong foundation in mathematics with expertise in Python and R, they have worked on diverse real-world data projects. Their teaching philosophy emphasizes clarity and practical application, ensuring you not only gain knowledge but also know how to apply it effectively. Who is it for? This book is intended for individuals with a basic understanding of algebra and some programming experience in Python or R. It is perfect for programmers who wish to dive into the world of data science or for those with math skills looking to apply them practically. If you seek to turn raw data into valuable insights and predictions, this book is tailored for you.

Presents information to create a trade-off analysis framework for use in government and commercial acquisition environments This book presents a decision management process based on decision theory and cost analysis best practices aligned with the ISO/IEC 15288, the Systems Engineering Handbook, and the Systems Engineering Body of Knowledge. It provides a sound trade-off analysis framework to generate the tradespace and evaluate value and risk to support system decision-making throughout the life cycle. Trade-off analysis and risk analysis techniques are examined. The authors present an integrated value trade-off and risk analysis framework based on decision theory. These trade-off analysis concepts are illustrated in the different life cycle stages using multiple examples from defense and commercial domains. Provides techniques to identify and structure stakeholder objectives and creative, doable alternatives Presents the advantages and disadvantages of tradespace creation and exploration techniques for trade-off analysis of concepts, architectures, design, operations, and retirement Covers the sources of uncertainty in the system life cycle and examines how to identify, assess, and model uncertainty using probability Illustrates how to perform a trade-off analysis using the INCOSE Decision Management Process using both deterministic and probabilistic techniques Trade-off Analytics: Creating and Exploring the System Tradespace is written for upper undergraduate students and graduate students studying systems design, systems engineering, industrial engineering and engineering management. This book also serves as a resource for practicing systems designers, systems engineers, project managers, and engineering managers. is a Research Professor in the Department of Industrial Engineering at the University of Arkansas. He is also a senior principal with Innovative Decisions, Inc., a decision and risk analysis firm and has served as Chairman of the Board. Dr. Parnell has published more than 100 papers and book chapters and was lead editor of Gregory S. Parnell, PhD, Decision Making for Systems Engineering and Management, Wiley Series in Systems Engineering (2nd Ed, Wiley 2011) and lead author of the Handbook of Decision Analysis (Wiley 2013). He is a fellow of INFORMS, the INCOSE, MORS, and the Society for Decision Professionals.

Mastering Tableau is your comprehensive guide to becoming highly skilled in Tableau, focusing on advanced data visualization and practical applications. You will learn how to create complex dashboards, integrate R, and make the most of Tableau's features to deliver compelling insights. By the end of the book, you'll be ready to tackle real-world business intelligence challenges. What this Book will help me do Master advanced Tableau calculations such as row-level and aggregate-level calculations. Create engaging and efficient dashboards for professional data presentations. Integrate R functionalities with Tableau for predictive and advanced analytics. Design and implement custom geographic visualizations, including polygon maps. Optimize performance and best practices in Tableau for innovative BI solutions. Author(s) Jen Stirrup and None Baldwin are experienced data analysts and Tableau experts with years of practical experience in consulting and teaching. Jen has contributed significantly to the Tableau community through workshops and talks. Together, they provide structured guidance that helps readers master Tableau while emphasizing hands-on learning. Who is it for? This book is for business analysts aiming to enhance their data visualization skills using Tableau. Whether you are an intermediate Tableau user looking to tackle advanced techniques or someone wanting to streamline your BI workflows, this book focuses on practical problem-solving. It equips you to use Tableau effectively to create impactful visualizations and insights.

A non-technical guide to leveraging retail analytics for personal and competitive advantage Style & Statistics is a real-world guide to analytics in retail. Written specifically for the non-IT crowd, this book explains analytics in an approachable, understandable way, and provides examples of direct application to retail merchandise management, marketing, and operations. The discussion covers current industry trends and emerging-standard processes, and illustrates how analytics is providing new solutions to perennial retail problems. You'll learn how to leverage the benefits of analytics to boost your personal career, and how to interpret data in a way that's useful to the average end business user or shopper. Key concepts are detailed in easy-to-understand language, and numerous examples highlight the growing importance of understanding analytics in the retail environment. The power of analytics has become apparent across industries, but it's left an especially indelible mark on retail. It's a complex topic, but you don't need to be a data scientist to take advantage of the opportunities it brings. This book shows you what you need to know, and how to put analytics to work with retail-specific applications. Learn how analytics can help you be better at your job Dig deeper into the customer's needs, wants, and dreams Streamline merchandise management, pricing, marketing, and more Find solutions for inefficiencies and inaccuracies As the retail customer evolves, so must the retail industry. The retail landscape not only includes in-store but also website, mobile site, mobile apps, and social media . With more and more competition emerging on all sides, retailers need to use every tool at their disposal to create value and gain a competitive advantage. Analytics offers a number of ways to make your company stand out, whether it's through improved operations, customer experience, or any of the other myriad factors that build a great place to shop. Style & Statistics provides an analytics primer with a practical bent, specifically for the retail industry.

Data is a key part of analyzing your designs and the way your users use your designs. Analytics can seem intimidating if you are not familiar with them, but the basics are pretty simple once you know what the numbers and graphs mean. What you’ll learn&8212;and how you can apply it You will learn basic tips about how to interpret a graph of user behavior to find the problems in your designs (so you can fix them!), and what the fundamental numbers mean. You will also start to have an intuition about how to compare those numbers to understand the “health” of your site/app and see insights that no one else can see. This lesson is for you because You can start using the information from these lessons today, and you will feel more comfortable learning more about user data and analytics after reading them. Prerequisites: No experience with data is necessary General familiarity with the idea of designing digital things is helpful Materials or downloads needed: None This Lesson in taken from by Joel Marsh. UX for Beginners

The intensified used of data based on analytical models to control digitalized operational business processes in an intelligent way is a game changer that continuously disrupts more and more markets. This book exemplifies this development and shows the latest tools and advances in this field Business Analytics for Managers offers real-world guidance for organizations looking to leverage their data into a competitive advantage. This new second edition covers the advances that have revolutionized the field since the first edition's release; big data and real-time digitalized decision making have become major components of any analytics strategy, and new technologies are allowing businesses to gain even more insight from the ever-increasing influx of data. New terms, theories, and technologies are explained and discussed in terms of practical benefit, and the emphasis on forward thinking over historical data describes how analytics can drive better business planning. Coverage includes data warehousing, big data, social media, security, cloud technologies, and future trends, with expert insight on the practical aspects of the current state of the field. Analytics helps businesses move forward. Extensive use of statistical and quantitative analysis alongside explanatory and predictive modeling facilitates fact-based decision making, and evolving technologies continue to streamline every step of the process. This book provides an essential update, and describes how today's tools make business analytics more valuable than ever. Learn how Hadoop can upgrade your data processing and storage Discover the many uses for social media data in analysis and communication Get up to speed on the latest in cloud technologies, data security, and more Prepare for emerging technologies and the future of business analytics Most businesses are caught in a massive, non-stop stream of data. It can become one of your most valuable assets, or a never-ending flood of missed opportunity. Technology moves fast, and keeping up with the cutting edge is crucial for wringing even more value from your data— Business Analytics for Managers brings you up to date, and shows you what analytics can do for you now.

Real-world tips for creating business value Details on modeling, data clustering, and more Enterprise use cases to help you get started Learn to predict the future! Business today relies on effectively using data to predict trends and sales. Predictive analytics is the tool that can make it happen, and this book eliminates the tricks and shows you how to use it. You'll learn to prepare and process your data, create goals, build a predictive model, get your organization's stakeholders on board, and more. Inside... How to start a project Identifying data types Modeling tips Working with algorithms How data clustering works How data classification works How deep learning works Advice on presentations Step-by-step predictive modeling

Resolve data-heavy retail marketing questions through key analytic steps, with expert guidance in a direct and conceptual style.

Use aggregate data to answer high-level business questions!

Data miners, data scientists, analytic managers, and analysts who work in all industries will find the insights in Randy Collica's Strategic Analytics and SAS: Using Aggregate Data to Drive Organizational Initiatives invaluable in their work. This book shows you how to use your existing data at aggregate levels to answer high-level business questions. Written in a detailed, step-by-step format, the multi-industry use cases begin with a high-level question that a C-level executive might ask. Collica then progresses through the steps to perform the analysis, including many tables and screenshots to guide you along the way. He then ends each use case with the solution to the high-level question. Topics covered include logistic analysis, models developed from surveys, survival analysis, confidence intervals, text mining and analysis, visual analytics, hypothesis tests, and size and magnitude of analytic effects. Connect the dots between detailed data on your customers and the high-level business goals of your organization with Strategic Analytics and SAS!

User experience monitoring is essential for enhancing the usability and performance of your mobile native app. How are your customers using your product? Which features do they prefer? How can you spot trouble before it adversely affects your product? This O’Reilly report provides an overview of several metrics you can apply, based on different use-cases. Author Wolfgang Beer explains the typical instrumentation and publishing process of mobile apps, and takes you through different instrumentation approaches. With screenshots from popular tools such as Google Analytics, Ruxit, Fabric, and Flurry Analytics, this report helps you choose the metrics that will help you improve your product’s performance. Monitor performance to understand your app’s stability and usability Measure app user engagement by identifying active and new users, and determining median session length Determine your app’s current retention and churn rates Gather business intelligence by defining users according to personas and lifetime value Oversee the service and infrastructure dependencies of your app in real time Visually track user behavior with heat maps and navigational paths Add automated or manual instrumentation before you publish your app

How to lead the change Analytics are driving big changes, not only in what marketing departments do but in how they are organized, staffed, led, and run. Leaders are grappling with issues that range from building an analytically driven marketing organization and determining the kinds of structure and talent that are needed to leading interactions with IT, finance, and sales and creating a unified view of the customer. The Analytical Marketer provides critical insight into the changing marketing organization—digital, agile, and analytical—and the tools for reinventing it. Written by the head of global marketing for SAS, The Analytical Marketer is based on the author’s firsthand experience of transforming a marketing organization from “art” to “art and science.” Challenged and inspired by their company’s own analytics products, the SAS marketing team was forced to rethink itself in order to take advantage of the new capabilities that those tools offer the modern marketer. Key marketers and managers at SAS tell their stories alongside the author’s candid lessons learned as she led the marketing organization’s transformation. With additional examples from other leading companies, this book is a practical guide and set of best practices for creating a new marketing culture that thrives on and adds value through data and analytics.

A complete, start-to-finish guide to Google Analytics instrumentation and reporting Google Analytics Breakthrough is a much-needed comprehensive resource for the world's most widely adopted analytics tool. Designed to provide a complete, best-practices foundation in measurement strategy, implementation, reporting, and optimization, this book systematically demystifies the broad range of Google Analytics features and configurations. Throughout the end-to-end learning experience, you'll sharpen your core competencies, discover hidden functionality, learn to avoid common pitfalls, and develop next-generation tracking and analysis strategies so you can understand what is helping or hindering your digital performance and begin driving more success. Google Analytics Breakthrough offers practical instruction and expert perspectives on the full range of implementation and reporting skills: Learn how to campaign-tag inbound links to uncover the email, social, PPC, and banner/remarketing traffic hiding as other traffic sources and to confidently measure the ROI of each marketing channel Add event tracking to capture the many important user interactions that Google Analytics does not record by default, such as video plays, PDF downloads, scrolling, and AJAX updates Master Google Tag Manager for greater flexibility and process control in implementation Set up goals and Enhanced Ecommerce tracking to measure performance against organizational KPIs and configure conversion funnels to isolate drop-off Create audience segments that map to your audience constituencies, amplify trends, and help identify optimization opportunities Populate custom dimensions that reflect your organization, your content, and your visitors so Google Analytics can speak your language Gain a more complete view of customer behavior with mobile app and cross-device tracking Incorporate related tools and techniques: third-party data visualization, CRM integration for long-term value and lead qualification, marketing automation, phone conversion tracking, usability, and A/B testing Improve data storytelling and foster analytics adoption in the enterprise As many as 10-25 million organizations have installed Google Analytics, including an estimated 67 percent of Fortune 500 companies, but deficiencies plague most implementations, and inadequate reporting practices continue to hinder meaningful analysis. By following the strategies and techniques in Google Analytics Breakthrough, you can address the gaps in your own still set, transcend the common limitations, and begin using Google Analytics for real competitive advantage. Critical contributions from industry luminaries such as Brian Clifton, Tim Ash, Bryan and Jeffrey Eisenberg, and Jim Sterne – and a foreword by Avinash Kaushik – enhance the learning experience and empower you to drive consistent, real-world improvement through analytics.

Targeted analytics to address the unique opportunities in hospitality and gaming The Analytic Hospitality Executive helps decision makers understand big data and how it can drive value in the industry. Written by a leading business analytics expert who specializes in hospitality and travel, this book draws a direct link between big data and hospitality, and shows you how to incorporate analytics into your strategic management initiative. You'll learn which data types are critical, how to identify productive data sources, and how to integrate analytics into multiple business processes to create an overall analytic culture that turns information into insight. The discussion includes the tools and tips that help make it happen, and points you toward the specific places in your business that could benefit from advanced analytics. The hospitality and gaming industry has unique needs and opportunities, and this book's targeted guidance provides a roadmap to big data benefits. Like most industries, the hospitality and gaming industry is experiencing a rapid increase in data volume, variety, and velocity. This book shows you how to corral this growing current, and channel it into productive avenues that drive better business. Understand big data and analytics Incorporate analytics into existing business processes Identify the most valuable data sources Create a strategic analytic culture that drives value Although the industry is just beginning to recognize the value of big data, it's important to get up to speed quickly or risk losing out on benefits that could drive business to greater heights. The Analytic Hospitality Executive provides a targeted game plan from an expert on the inside, so you can start making your data work for you.

Learn all you need to know about seven key innovations disrupting business analytics today. These innovations—the open source business model, cloud analytics, the Hadoop ecosystem, Spark and in-memory analytics, streaming analytics, Deep Learning, and self-service analytics—are radically changing how businesses use data for competitive advantage. Taken together, they are disrupting the business analytics value chain, creating new opportunities. Enterprises who seize the opportunity will thrive and prosper, while others struggle and decline: disrupt or be disrupted. Disruptive Business Analytics provides strategies to profit from disruption. It shows you how to organize for insight, build and provision an open source stack, how to practice lean data warehousing, and how to assimilate disruptive innovations into an organization. Through a short history of business analytics and a detailed survey of products and services, analytics authority Thomas W. Dinsmore provides a practical explanation of the most compelling innovations available today. What You'll Learn Discover how the open source business model works and how to make it work for you See how cloud computing completely changes the economics of analytics Harness the power of Hadoop and its ecosystem Find out why Apache Spark is everywhere Discover the potential of streaming and real-time analytics Learn what Deep Learning can do and why it matters See how self-service analytics can change the way organizations do business Who This Book Is For Corporate actors at all levels of responsibility for analytics: analysts, CIOs, CTOs, strategic decision makers, managers, systems architects, technical marketers, product developers, IT personnel, and consultants.

Data Analysis Plans: A Blueprint for Success Using SAS gets you started on building an effective data analysis plan with a solid foundation for planning and managing your analytics projects. Data analysis plans are critical to the success of analytics projects and can improve the workflow of your project when implemented effectively. This book provides step-by-step instructions on writing, implementing, and updating your data analysis plan. It emphasizes the concept of an analysis plan as a working document that you update throughout the life of a project.

This book will help you manage the following tasks:

control client expectations

limit and refine the scope of the analysis

enable clear communication and understanding among team members

organize and develop your final report

SAS users of any level of experience will benefit from this book, but beginners will find it extremely useful as they build foundational knowledge for performing data analysis and hypotheses testing. Subject areas include medical research, public health research, social studies, educational testing and evaluation, and environmental studies.

The Data and Analytics Playbook: Proven Methods for Governed Data and Analytic Quality explores the way in which data continues to dominate budgets, along with the varying efforts made across a variety of business enablement projects, including applications, web and mobile computing, big data analytics, and traditional data integration. The book teaches readers how to use proven methods and accelerators to break through data obstacles to provide faster, higher quality delivery of mission critical programs. Drawing upon years of practical experience, and using numerous examples and an easy to understand playbook, Lowell Fryman, Gregory Lampshire, and Dan Meers discuss a simple, proven approach to the execution of multiple data oriented activities. In addition, they present a clear set of methods to provide reliable governance, controls, risk, and exposure management for enterprise data and the programs that rely upon it. In addition, they discuss a cost-effective approach to providing sustainable governance and quality outcomes that enhance project delivery, while also ensuring ongoing controls. Example activities, templates, outputs, resources, and roles are explored, along with different organizational models in common use today and the ways they can be mapped to leverage playbook data governance throughout the organization. Provides a mature and proven playbook approach (methodology) to enabling data governance that supports agile implementation Features specific examples of current industry challenges in enterprise risk management, including anti-money laundering and fraud prevention Describes business benefit measures and funding approaches using exposure based cost models that augment risk models for cost avoidance analysis and accelerated delivery approaches using data integration sprints for application, integration, and information delivery success

Filled with helpful examples and real-life projects of SAS users, A Recipe for Success Using SAS University Edition is an easy guide on how to start applying the analytical power of SAS to real-world scenarios. This book shows you: how to start using analytics how to use SAS to accomplish a project goal how to effectively apply SAS to your community or school how users like you implemented SAS to solve their analytical problems A beginner’s guide on how to create and complete your first analytics project using SAS University Edition, this book is broken down into easy-to-read chapters that also include quick takeaway tips. It introduces you to the vocabulary and structure of the SAS language, shows you how to plan and execute a successful project, introduces you to basic statistics, and it walks you through case studies to inspire and motivate you to complete your own projects. Following a recipe for success using this book, harness the power of SAS to plan and complete your first analytics project!

Unlock the potential of big data analytics by mastering R programming with this comprehensive guide. This book takes you step-by-step through real-world scenarios where R's capabilities shine, providing you with practical skills to handle, process, and analyze large and complex datasets effectively. What this Book will help me do Understand the latest big data processing methods and how R can enhance their application. Set up and use big data platforms such as Hadoop and Spark in conjunction with R. Utilize R for practical big data problems, such as analyzing consumption and behavioral datasets. Integrate R with SQL and NoSQL databases to maximize its versatility in data management. Discover advanced machine learning implementations using R and Spark MLlib for predictive analytics. Author(s) None Walkowiak is an experienced data analyst and R programming expert with a passion for data engineering and machine learning. With a deep knowledge of big data platforms and extensive teaching experience, they bring a clear and approachable writing style to help learners excel. Who is it for? Ideal for data analysts, scientists, and engineers with fundamental data analysis knowledge looking to enhance their big data capabilities using R. If you aim to adapt R for large-scale data management and analysis workflows, this book is your ideal companion to bridge the gap.

Data-driven techniques have improved decision-making processes for people in industries such as finance and real estate. Yet, despite promising solutions that data analytics and artificial intelligence/machine learning (ML) tools can bring to healthcare, the industry remains largely unconvinced. In this O’Reilly report, you’ll explore the potential of—and impediments to—widespread adoption of AI and ML in the medical field. You’ll also learn how extensive government regulation and resistance from the medical community have so far stymied full-scale acceptance of sophisticated data analytics in healthcare. Through interviews with several professionals working at the intersection of medicine and data science, author Mike Barlow examines five areas where the application of AI/ML strategies can spur a beneficial revolution in healthcare: Identifying risks and interventions for healthcare management of entire populations Closing gaps in care by designing plans for individual patients Supporting customized self-care treatment plans and monitoring patient health in real time Optimizing healthcare processes through data analysis to improve care and reduce costs Helping doctors and patients choose proper medications, dosages, and promising surgical options