商业数据科学
本书特色
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对于认真拥抱大数据机遇的人而言，这是一本必读书。
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内容简介
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这是一本博大精深但又不太技术的指南，向你介绍数据科学的基本原则，并带领你全程浏览从所搜集数据中抽取有用知识和商业价值所必需的“数据分析思维”。通过学习数据科学原则，你将领略当今用到的诸多数据挖掘技巧。更重要的是，这些原则支撑着通过数据挖掘技巧解决商业问题所需的手段和策略。
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目录
Preface1.Introduction： Data-Analytic ThinkingThe Ubiquity of Data OpportunitiesExample： Hurricane FrancesExample： Predicting Customer ChurnData Science， Engineering， and Data-Driven Decision MakingData Processing and “Big Data”From Big Data 1.0 to Big Data 2.0Data and Data Science Capability as a Strategic AssetData-Analytic ThinkingThis BookData Mining and Data Science， RevisitedChemistry Is Not About Test Tubes： Data Science Versus the Work of the Data ScientistSummary2.Business Problems and Data Science SolutionsFrom Business Problems to Data Mining TasksSupervised Versus Unsupervised MethodsData Mining and Its ResultsThe Data Mining ProcessBusiness UnderstandingData UnderstandingData PreparationModelingEvaluationDeploymentImplications for Managing the Data Science TeamOther Analytics Techniques and TechnologiesStatisticsDatabase QueryingData WarehousingRegression AnalysisMachine Learning and Data MiningAnswering Business Questions with These TechniquesSummary3.Introduction to Predictive Modeling： From Correlation to Supervised Segmentation.Models， Induction， and PredictionSupervised SegmentationSelecting Informative AttributesExample： Attribute Selection with Information GainSupervised Segmentation with Tree-Structured ModelsVisualizing SegmentationsTrees as Sets of RulesProbability EstimationExample： Addressing the Churn Problem with Tree InductionSummary4.Fitting a Model to DataClassification via Mathematical FunctionsLinear Discriminant FunctionsOptimizing an Objective FunctionAn Example of Mining a Linear Discriminant from DataLinear Discriminant Functions for Scoring and Ranking InstancesSupport Vector Machines， BrieflyRegression via Mathematical FunctionsClass Probability Estimation and Logistic “Regression”Logistic Regression： Some Technical DetailsExample： Logistic Regression versus Tree InductionNonlinear Functions， Support Vector Machines， and Neural Networks5.Overfitting and Its Avoidance6.Similarity， Neighbors， and Clusters7.Decision AnalyticThinking h What Is a Good Model?8.Visualizing Model Performance9.Evidence and Probabilities10.Representing and Mining Text11.Decision Analytic Thinking Ih Toward Analytical Engineering12.Other Data Science Tasks and Techniques13.Data Science and Business Strategy14.ConclusionA.Proposal ReviewGuideB.Another Sample ProposalGlossaryBibliographyIndex
封面
书名:商业数据科学
作者:Foster Provost，Tom F
页数:21,386页
定价:¥98.0
出版社:东南大学出版社
出版日期:2018-02-01
ISBN:9787564175283
PDF电子书大小:155MB 高清扫描完整版