Bøker av Galit Shmueli

MACHINE LEARNING FOR BUSINESS ANALYTICSMachine 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 R 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-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques.This is the second R edition of Machine Learning for Business Analytics. This edition also includes:* A new co-author, Peter Gedeck, who brings over 20 years of experience in machine learning using R* An expanded chapter focused on discussion of deep learning techniques* A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning* A new chapter on responsible data science* Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students* A full chapter devoted to relevant case studies with more than a dozen cases demonstrating applications for the machine learning techniques* End-of-chapter exercises that help readers gauge and expand their comprehension and competency of the material presented* A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutionsThis textbook is an ideal resource for upper-level undergraduate and graduate level courses in data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.

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 importance of making informative decisions.Practical Risk Analysis for Project Planning is a hands-on introduction to integrating numerical data and domain knowledge into popular spreadsheet software such as Microsoft Excel or Google Spreadsheets, to arrive at informed project-planning decisions. The focus of the book is on formalizing domain expertise into numerical data, providing tools for assessing potential project performance, and evaluating performance under realistic uncertainty. The book introduces basic principles for assessing potential project performance and risk under different scenarios, by addressing uncertainty that arises at different levels. It describes measures of expected performance and risk, presents approaches such as scenario building and Monte Carlo simulation for addressing uncertainty, and introduces methods for comparing competing projects and reducing risk via project portfolios.A companion website to the book is available at www.RiskAnalysisBook.comNo special software is required except Excel or another spreadsheet software. While the book assumes no knowledge of statistics, operations research, or management science, it does rely on basic familiarity with Excel. Chapter exercises and examples of real projects are aimed at hands-on learning.

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 batch based on a sample. They are therefore widely used by manufacturers, suppliers, contractors and subcontractors, and service providers in a wide range of industries.The book introduces readers to the most popular sampling plans, including Military Standards and civilian ISO and ANSI/ASQC/BS standards. It covers the design, choice and performance evaluation of different types of plans, including single- and double-stage plans, rectifying and non-rectifying plans, plans for pass/fail and continuous measurements, continuous sampling plans, and more.Practical Acceptance Sampling is suitable for courses on quality control and for quality practitioners with basic knowledge of statistics. It offers clear explanations, examples, end-of-chapter problems, and illustrations of state-of-the-art online resources. Methods are illustrated using Microsoft Excel, online calculators, and SQCOnline.com. However, any statistical software can be used with the book.A companion website to the book is available at www.SamplingBook.com