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Machine Learning for Business Analytics
Machine Learning for Business Analytics Machine learning—also known as data mining or data analytics—is a fundamental part of data science. It is used by organizations in a wide variety of arenas to turn raw data into actionable information. Machine Learning for Business Analytics: Concepts, Techniques and Applications in RapidMiner provides a comprehensive introduction and an overview of this methodology. This best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation and network analytics. Along with hands-on exercises and real-li...
Data Mining for Business Analytics
An applied approach to data mining and predictive analytics with clear exposition, hands-on exercises, and real-life case studies. Readers will work with all of the standard data mining methods using the Microsoft® Office Excel® add-in XLMiner® to develop predictive models and learn how to obtain business value from Big Data. Featuring updated topical coverage on text mining, social network analysis, collaborative filtering, ensemble methods, uplift modeling and more, the Third Edition also includes: Real-world examples to build a theoretical and practical understanding of key data mining methods End-of-chapter exercises that help readers better understand the presented material Data-rich...
Practical Risk Analysis for Project Planning
Projects are investments of resources for achieving a particular objective or set of objectives. Resources include time, money, manpower, and sometimes lives. Objectives include financial gain, social and health benefits, national goals, educational and scientific achievements, and reduction of suffering, among many others. Projects are undertaken by large and small organizations, by governments, non-profit organizations, private businesses, and by individuals. Determining whether to execute a project, or which project to execute among a set of possibilities is often a challenge with high stakes. Assessing the potential outcomes of a project can therefore be detrimental, leading to the impor...
Getting Started with Business Analytics
Assuming no prior knowledge or technical skills, Getting Started with Business Analytics: Insightful Decision-Making explores the contents, capabilities, and applications of business analytics. It bridges the worlds of business and statistics and describes business analytics from a non-commercial standpoint. The authors demystify the main concepts and terminologies and give many examples of real-world applications. The first part of the book introduces business data and recent technologies that have promoted fact-based decision-making. The authors look at how business intelligence differs from business analytics. They also discuss the main components of a business analytics application and t...
Information Quality
Provides an important framework for data analysts in assessing the quality of data and its potential to provide meaningful insights through analysis Analytics and statistical analysis have become pervasive topics, mainly due to the growing availability of data and analytic tools. Technology, however, fails to deliver insights with added value if the quality of the information it generates is not assured. Information Quality (InfoQ) is a tool developed by the authors to assess the potential of a dataset to achieve a goal of interest, using data analysis. Whether the information quality of a dataset is sufficient is of practical importance at many stages of the data analytics journey, from the...
Statistical Methods in e-Commerce Research
This groundbreaking book introduces the application of statistical methodologies to e-Commerce data With the expanding presence of technology in today's economic market, the use of the Internet for buying, selling, and investing is growing more popular and public in nature. Statistical Methods in e-Commerce Research is the first book of its kind to focus on the statistical models and methods that are essential in order to analyze information from electronic-commerce (e-Commerce) transactions, identify the challenges that arise with new e-Commerce data structures, and discover new knowledge about consumer activity. This collection gathers over thirty researchers and practitioners from the fie...
Practical Acceptance Sampling
New to the second edition: A section on Acceptance-on-Zero plans, additional screenshots from the newly-designed SQCOnline.com with several new calculators, and improved book design for enhanced readability. Practical Acceptance Sampling is a hands-on introduction to the inspection of products and services for quality assurance using statistically-based sampling plans. In today’s era of global supply chains, the path from raw materials to final product often takes place over multiple companies and across multiple continents. Acceptance sampling is key in the 21st century environment. Acceptance sampling plans provide criteria and decision rules for determining whether to accept or reject a...
Practical Time Series Forecasting with R
Practical Time Series Forecasting with R: A Hands-On Guide, Second Edition provides an applied approach to time-series forecasting. Forecasting is an essential component of predictive analytics. The book introduces popular forecasting methods and approaches used in a variety of business applications. The book offers clear explanations, practical examples, and end-of-chapter exercises and cases. Readers will learn to use forecasting methods using the free open-source R software to develop effective forecasting solutions that extract business value from time-series data. Featuring improved organization and new material, the Second Edition also includes: - Popular forecasting methods including ...
Modeling Online Auctions
Explore cutting-edge statistical methodologies for collecting, analyzing, and modeling online auction data Online auctions are an increasingly important marketplace, as the new mechanisms and formats underlying these auctions have enabled the capturing and recording of large amounts of bidding data that are used to make important business decisions. As a result, new statistical ideas and innovation are needed to understand bidders, sellers, and prices. Combining methodologies from the fields of statistics, data mining, information systems, and economics, Modeling Online Auctions introduces a new approach to identifying obstacles and asking new questions using online auction data. The authors...
Practical Statistics for Data Scientists
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why ...
