MACHINE LEARNING FOR BUSINESS ANALYTICS: CONCEPTS, TECHNIQUES, AND APPLICATIONS WITH ANALYTIC SOLVER DATA MINING (4版)
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MACHINE LEARNING FOR BUSINESS ANALYTICS
Machine learning―also known as data mining or predictive 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 with Analytic Solver® Data Mining provides a comprehensive introduction and an overview of this methodology. The fourth edition of this best-selling textbook covers both statistical and machine learning algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, time series forecasting 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 fourth edition of Machine Learning for Business Analytics also includes:
An expanded chapter on deep learning
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 solutions
This 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.
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Introduction to Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets (7版)
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Introduction to Management Science and Business Analytics: A Modeling and Case Studies Approach with Spreadsheets
作 / 譯 者 ： Frederick S. Hillier,Mark S. Hillier,Kari Schmedders,Molly Stephens
I S B N - 13 ： 9781265040055
I S B N - 10 ： 1265040052
類 別： 管理科學
版 次： 7 版
年 份： 2023
規 格： 742 頁
出 版 商： McGraw Hill Education
目錄
PART I: THE ESSENCE OF MANAGEMENT SCIENCE AND BUSINESS ANALYTICS
Ch 1 Introduction
Ch 2 Overview of the Analysis Process
PART II: MODELS FOR PREDICTIVE ANALYTICS
Ch 3 Classification and Prediction Models for Predictive Analytics
CH 4 Predictive Analytics Based on Traditional Forecasting Methods
PART III: USING LINEAR PROGRAMMING TO PERFORM PRESCRIPTIVE ANALYTICS
Ch 5 Linear Programming: Basic Concepts
Ch 6 Linear Programming: Formulation and Applications
Ch 7 The Art of Modeling with Spreadsheets
Ch 8 What-If Analysis for Linear Programming
Ch 9 Network Optimization Problems
PART IV: USING INTEGER OR NONLIEAR PROGRAMMING TO PERFORM PRESCRIPTIVE ANALYTICS
Ch10 Integer Programming
Ch11 Nonlinear Programming
PART V: TRADITIONAL UNCERTAINTY MODELS FOR PERFORMING PREDICTIVE OR PRESCRIPTIVE ANALYTICS
Ch12 Decision Analysis
Ch13 Queueing Models
Ch14 Computer Simulation: Basic Concepts
Ch15 Computer Simulation with Analytic Solver
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1680
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