This text is an unbound, binder-ready edition. Business Statistics: For Contemporary Decision Making, 8th Edition continues the tradition of presenting and explaining the wonders of business statistics through the use of clear, complete, student-friendly pedagogy. Ken Black's text equips readers with the quantitative decision-making skills and analysis techniques you need to make smart decisions based on real-world data.

This comprehensive guide introduces you to the functionalities of MicroStrategy for business intelligence, empowering you to build dashboards, reports, and visualizations using hands-on, practical recipes with clear examples. You'll learn how to use MicroStrategy for the entire BI lifecycle, making data actionable and insights accessible. What this Book will help me do Install and configure the MicroStrategy platform, including setting up a fully operational BI environment. Create interactive dashboards and web reports to visualize and analyze data effectively. Learn to use MicroStrategy on mobile devices, enabling access to data-driven insights anywhere. Discover advanced analytics techniques using Visual Insight and MicroStrategy Cloud Express. Master practical skills with real-life examples to implement robust BI solutions. Author(s) Davide Moraschi, an experienced professional in business intelligence and data analytics, brings his expertise to guiding readers through the MicroStrategy platform. He has years of experience implementing and developing BI solutions in diverse industries, offering practical perspectives. Davide's approachable teaching style and clear examples make technical concepts accessible and engaging. Who is it for? This book is tailored for BI developers and data analysts who want to deepen their expertise in MicroStrategy. It's also suitable for IT professionals and business users aiming to leverage MicroStrategy for data insights. Some existing knowledge of BI concepts, such as dimensional modeling, will enrich your learning experience. You need no prior experience with MicroStrategy to benefit from this book.

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know. In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share new algorithms, methods, and models by presenting case studies and the code they use. If you’re familiar with linear algebra, probability, and statistics, and have programming experience, this book is an ideal introduction to data science. Topics include: Statistical inference, exploratory data analysis, and the data science process Algorithms Spam filters, Naive Bayes, and data wrangling Logistic regression Financial modeling Recommendation engines and causality Data visualization Social networks and data journalism Data engineering, MapReduce, Pregel, and Hadoop Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course.

"IBM SPSS Modeler Cookbook" is your practical guide to mastering data mining with IBM SPSS Modeler. This comprehensive book takes you beyond the basics, offering expert insights, time-saving techniques, and powerful workflows to grow your skills and elevate your analytical abilities. You will learn to apply the CRISP-DM methodology, efficiently prepare and explore data, build advanced models, and confidently incorporate analytical results into your business decisions. What this Book will help me do Effectively apply the CRISP-DM standard process to organize your data mining projects. Leverage efficient techniques for data extraction, transformation, and preparation. Develop and evaluate predictive models for practical applications in your organization. Enhance your models by utilizing advanced features and expert tips. Automate and streamline your data mining process with scripting for ultimate control. Author(s) Keith McCormick and None Abbott are seasoned data mining professionals with deep expertise in IBM SPSS Modeler and predictive analytics. Together, they have extensive experience in consulting, training, and applying advanced analytical techniques across industries. Through their approachable and insightful writing style, they share practical knowledge and expert workflows to empower readers. Who is it for? This book is designed for individuals who have basic experience with IBM SPSS Modeler and aspire to deepen their expertise. Whether you are a data analyst looking to advance your analytical capabilities or a professional aiming to integrate data-driven solutions into your organization, this book provides the knowledge and practical guidance you need to take the next step in your data mining journey.

This book is a comprehensive guide designed for those new to Pentaho Data Integration. With a focus on practical application and step-by-step learning, this book covers everything from installation to complex data manipulation. By following along, you will acquire the skills you need to efficiently manage and transform data using Pentaho. What this Book will help me do Understand how to install and set up Pentaho Data Integration for professional data manipulation. Master data transformation tasks such as cleaning, sorting, and integrating different data sources. Learn to configure and operate databases within the Pentaho environment, including CRUD operations. Gain hands-on experience with data warehousing concepts and using Pentaho to populate data warehouses. Develop workflows and schedules for automated data processes using Pentaho's advanced tools. Author(s) Carina Roldán is an experienced data professional with extensive expertise in the field of ETL and data integration. Her teaching style is clear, approachable, and heavily reliant on practical examples. She focuses on enabling learners to build real-world skills in a supportive and engaging manner, making complex topics accessible to everyone. Who is it for? This book is perfect for developers, database administrators, and IT professionals looking to venture into ETL tools or seeking a deeper understanding of Pentaho Data Integration. Beginners without prior exposure to Pentaho Data Integration will find it an excellent entry point, while those with some experience will benefit from its in-depth insights. It is also valuable for data warehouse designers and architects aiming to streamline their workflows.

This book serves as a thorough introduction to using the Greenplum platform for big data analytics. It explores key concepts for processing, analyzing, and deriving insights from big data using Greenplum, covering aspects from data integration to advanced analytics techniques like programming with R and MADlib. What this Book will help me do Understand the architecture and core components of the Greenplum platform. Learn how to design and execute data science projects using Greenplum. Master loading, processing, and querying big data in Greenplum efficiently. Explore programming with R and integrating it with Greenplum for analytics. Gain skills in high-availability configurations, backups, and recovery within Greenplum. Author(s) Sunila Gollapudi is a seasoned expert in the field of big data analytics and has multiple years of experience working with platforms like Greenplum. Her real-world problem-solving expertise shapes her practical and approachable writing style, making this book not only educational but enjoyable to read. Who is it for? This book is ideal for data scientists or analysts aiming to explore the capabilities of big data platforms like Greenplum. It suits readers with basic knowledge of data warehousing, programming, and analytics tools who want to deepen their expertise and effectively harness Greenplum for analytics.

You've just received a new survey of study results, and you need to quickly create custom graphical views of the data. Or, you've completed your analysis, and you need graphs to present the results to your audience, in the style that they prefer. Now, you can create custom graphs quickly and easily with Getting Started with the Graph Template Language in SAS, without having to understand all of the Graph Template Language (GTL) features first.

This book will get you started building graphs immediately and will guide you toward a better understanding of the GTL, one step at a time. It shows you the most common approaches to a variety of graphs along with information that you can use to build more complex graphs from there. Sanjay Matange offers expert tips, examples, and techniques, with a goal of providing you with a solid foundation in using the GTL so that you can progress to more sophisticated, adaptable graphs as you need them.

Ultimately, Getting Started with the Graph Template Language in SAS allows you to bypass the learning curve. It teaches you how to quickly create custom, aesthetically pleasing graphs that present your data with maximum clarity and minimum clutter.

This book is part of the SAS Press program.

Mining big data requires a deep investment in people and time. How can you be sure you’re building the right models? With this hands-on book, you’ll learn a flexible toolset and methodology for building effective analytics applications with Hadoop. Using lightweight tools such as Python, Apache Pig, and the D3.js library, your team will create an agile environment for exploring data, starting with an example application to mine your own email inboxes. You’ll learn an iterative approach that enables you to quickly change the kind of analysis you’re doing, depending on what the data is telling you. All example code in this book is available as working Heroku apps. Create analytics applications by using the agile big data development methodology Build value from your data in a series of agile sprints, using the data-value stack Gain insight by using several data structures to extract multiple features from a single dataset Visualize data with charts, and expose different aspects through interactive reports Use historical data to predict the future, and translate predictions into action Get feedback from users after each sprint to keep your project on track

KNIME Essentials is a comprehensive guide to mastering KNIME, an open-source data analytics platform. Through this book, you'll discover how to process, visualize, and report on data effectively. Whether you're new to KNIME or data analytics in general, this resource is designed to equip you with the skills needed to handle data challenges confidently. What this Book will help me do Understand how to install and set up KNIME for data analysis tasks. Learn to create workflows to efficiently process data. Explore methods for importing and pre-processing data from various sources. Master techniques for visualizing and analyzing processed data. Generate professional-grade reports based on your data visualizations. Author(s) Gábor Bakos, the author of KNIME Essentials, leverages his expertise in data analytics and software tools to provide readers with a practical guide to mastering KNIME. With years of experience in working with analytics platforms, he crafts content that is accessible and focused on delivering real-world results. His user-focused approach helps readers quickly grasp complex concepts. Who is it for? This book is ideal for data analysts and professionals seeking to enhance their data processing skills with KNIME. No prior knowledge of KNIME is expected, but a foundational understanding of data analytics concepts would be beneficial. If you're looking to produce insightful analytics and reports efficiently, this guide is tailored for you.

A major tool for quality control and management, statistical process control (SPC) monitors sequential processes, such as production lines and Internet traffic, to ensure that they work stably and satisfactorily. Along with covering traditional methods, Introduction to Statistical Process Control describes many recent SPC methods that improve upon the more established techniques. The author—a leading researcher on SPC—shows how these methods can handle new applications. After exploring the role of SPC and other statistical methods in quality control and management, the book covers basic statistical concepts and methods useful in SPC. It then systematically describes traditional SPC charts, including the Shewhart, CUSUM, and EWMA charts, as well as recent control charts based on change-point detection and fundamental multivariate SPC charts under the normality assumption. The text also introduces novel univariate and multivariate control charts for cases when the normality assumption is invalid and discusses control charts for profile monitoring. All computations in the examples are solved using R, with R functions and datasets available for download on the author’s website. Offering a systematic description of both traditional and newer SPC methods, this book is ideal as a primary textbook for a one-semester course in disciplines concerned with process quality control, such as statistics, industrial and systems engineering, and management sciences. It can also be used as a supplemental textbook for courses on quality improvement and system management. In addition, the book provides researchers with many useful, recent research results on SPC and gives quality control practitioners helpful guidelines on implementing up-to-date SPC techniques.

Learn the concepts and methods for creating economic and business value with digital analytics, mobile analytics, web analytics, and market research and social media data. In , pioneering expert Judah Phillips introduces the concepts, terms, and methods that comprise the science and art of digital analysis for web, site, social, video, and other types of quantitative and qualitative data. Business readers—from new practitioners to experienced executives—who want to understand how digital analytics can be used to reduce costs and increase profitable revenue throughout the business should read this book. Phillips delivers a comprehensive review of the core concepts, vocabulary, and frameworks, including analytical methods and tools that can help you successfully integrate analytical processes, technology, and people into all aspects of business operations. This unbiased and product-independent primer draws from the author's extensive experience doing and managing analytics in this field. Digital Analytics Primer

Improve patient outcomes, lower costs, reduce fraud—all with healthcare analytics Healthcare Analytics for Quality and Performance Improvement walks your healthcare organization from relying on generic reports and dashboards to developing powerful analytic applications that drive effective decision-making throughout your organization. Renowned healthcare analytics leader Trevor Strome reveals in this groundbreaking volume the true potential of analytics to harness the vast amounts of data being generated in order to improve the decision-making ability of healthcare managers and improvement teams. Examines how technology has impacted healthcare delivery Discusses the challenge facing healthcare organizations: to leverage advances in both clinical and information technology to improve quality and performance while containing costs Explores the tools and techniques to analyze and extract value from healthcare data Demonstrates how the clinical, business, and technology components of healthcare organizations (HCOs) must work together to leverage analytics Other industries are already taking advantage of big data. Healthcare Analytics for Quality and Performance Improvement helps the healthcare industry make the most of the precious data already at its fingertips for long-overdue quality and performance improvement.

When ricocheting a solution that involves analytics, the mainframe might not be the first platform that comes to mind. However, the IBM® System z® group has developed some innovative solutions that include the well-respected mainframe benefits. This book describes a workshop that demonstrates the use of real-time advanced analytics for enhancing core banking decisions using a loan origination example. The workshop is a live hands-on experience of the entire process from analytics modeling to deployment of real-time scoring services for use on IBM z/OS®. In this IBM Redbooks® publication, we include a facilitator guide chapter as well as a participant guide chapter. The facilitator guide includes information about the preparation, such as the needed material, resources, and steps to set up and run this workshop. The participant guide shows step-by-step the tasks for a successful learning experience. The goal of the first hands-on exercise is to learn how to use IBM SPSS® Modeler for Analytics modeling. This provides the basis for the next exercise "Configuring risk assessment in SPSS Decision Management". In the third exercise, the participant experiences how real-time scoring can be implemented on a System z. This publication is written for consultants, IT architects, and IT administrators who want to become familiar with SPSS and analytics solutions on the System z.

Partial Least Squares (PLS) is a flexible statistical modeling technique that applies to data of any shape. It models relationships between inputs and outputs even when there are more predictors than observations. Using JMP statistical discovery software from SAS, Discovering Partial Least Squares with JMP explores PLS and positions it within the more general context of multivariate analysis.

Ian Cox and Marie Gaudard use a “learning through doing” style. This approach, coupled with the interactivity that JMP itself provides, allows you to actively engage with the content. Four complete case studies are presented, accompanied by data tables that are available for download. The detailed “how to” steps, together with the interpretation of the results, help to make this book unique.

Discovering Partial Least Squares with JMP is of interest to professionals engaged in continuing development, as well as to students and instructors in a formal academic setting. The content aligns well with topics covered in introductory courses on: psychometrics, customer relationship management, market research, consumer research, environmental studies, and chemometrics. The book can also function as a supplement to courses in multivariate statistics and to courses on statistical methods in biology, ecology, chemistry, and genomics.

While the book is helpful and instructive to those who are using JMP, a knowledge of JMP is not required, and little or no prior statistical knowledge is necessary. By working through the introductory chapters and the case studies, you gain a deeper understanding of PLS and learn how to use JMP to perform PLS analyses in real-world situations.

This book motivates current and potential users of JMP to extend their analytical repertoire by embracing PLS. Dynamically interacting with JMP, you will develop confidence as you explore underlying concepts and work through the examples. The authors provide background and guidance to support and empower you on this journey.

This book is part of the SAS Press program.

"Data is here, it's growing, and it's powerful." Author Cathy O'Neil argues that the right approach to data is skeptical, not cynical––it understands that, while powerful, data science tools often fail. Data is nuanced, and "a really excellent skeptic puts the term 'science' into 'data science.'" The big data revolution shouldn't be dismissed as hype, but current data science tools and models shouldn't be hailed as the end-all-be-all, either.

If you know how to program with Python, and know a little about probability, you’re ready to tackle Bayesian statistics. This book shows you how to use Python code instead of math to help you learn Bayesian fundamentals. Once you get the math out of the way, you’ll be able to apply these techniques to real-world problems.

Learn the secrets to using analytics to grow your business Analytics continues to trend as one of the hottest topics in the business community today. With ever-growing amounts of business data and evolving performance management/business intelligence architectures, how well your business does analyzing its data will differentiate you from your competition. Killer Analytics explores how you can use the muscle of analytics to measure new business elements. Author Mark Brown introduces 20 new metrics that can drive competitive advantage for your business, including social networks, sustainability, culture, innovation, employee satisfaction, and other key business elements. Shows organizations how to use analytics to measure key elements of business performance not traditionally measured Introduces 20 new metrics that drive competitive advantage Reveals how to measure social networking, sustainability, innovation, culture, and more Aside from the science and process of analytics, businesses need to think outside the box in terms of what they are measuring and how new analytical tools can be used to measure business elements such as innovation or sustainability. Opening the doors to a powerful new way of measuring your business, Killer Analytics saves you a small fortune on consultants with dynamic, forward-thinking advice for making the most of every component of your business.

Learn how to perform data analysis with the R language and software environment, even if you have little or no programming experience. With the tutorials in this hands-on guide, youâ??ll learn how to use the essential R tools you need to know to analyze data, including data types and programming concepts. The second half of Learning R shows you real data analysis in action by covering everything from importing data to publishing your results. Each chapter in the book includes a quiz on what youâ??ve learned, and concludes with exercises, most of which involve writing R code. Write a simple R program, and discover what the language can do Use data types such as vectors, arrays, lists, data frames, and strings Execute code conditionally or repeatedly with branches and loops Apply R add-on packages, and package your own work for others Learn how to clean data you import from a variety of sources Understand data through visualization and summary statistics Use statistical models to pass quantitative judgments about data and make predictions Learn what to do when things go wrong while writing data analysis code

This IBM® Redbooks® publication explains how IBM Cognos® Business Intelligence (BI) administrators, authors, modelers, and power users can use the dynamic query layer effectively. It provides guidance for determining which technology within the dynamic query layer can best satisfy your business requirements. Administrators can learn how to tune the query service effectively and preferred practices for managing their business intelligence content. This book includes information about metadata modeling of relational data sources with IBM Cognos Framework Manager. It includes considerations that can help you author high-performing applications that satisfy analytical requirements of users. This book provides guidance for troubleshooting issues related to the dynamic query layer of Cognos BI.

Using OpenRefine provides a comprehensive guide to managing and cleaning large datasets efficiently. By following a practical, recipe-based approach, this book ensures readers can quickly master OpenRefine's features to enhance their data handling skills. Whether dealing with transformations, entity recognition, or dataset linking, you'll gain the tools to make your data work for you. What this Book will help me do Import and structure various formats of data for seamless processing. Apply both basic and advanced transformations to optimize data quality. Utilize regular expressions for sophisticated filtering and partitioning. Perform named-entity extraction and advanced reconciliation tasks. Master the General Refine Expression Language for powerful data operations. Author(s) The author is an experienced data analyst and educator, specializing in data preparation and transformation for real-world applications. Their approach combines a thorough technical understanding with an accessible teaching style, ensuring that complex concepts are easy to grasp. Who is it for? This book is crafted for anyone working with large datasets, from novices learning to handle and clean data to experienced practitioners seeking advanced techniques. If you aim to improve your data management skills or deliver quality insights from messy data, this book is for you.