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商业数据科学(影印版)(英文版)

  • 定价: ¥98
  • ISBN:9787564175283
  • 开 本:16开 平装
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  • 折扣:
  • 出版社:东南大学
  • 页数:386页
  • 作者:(美)福斯特·普罗...
  • 立即节省:
  • 2018-02-01 第1版
  • 2018-02-01 第1次印刷
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导语

  

内容提要

  

    福斯特·普罗沃斯特、汤姆·福赛特著的《商业数据科学(影印版)(英文版)》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。

目录

Preface
1.Introduction: Data-Analytic Thinking
  The Ubiquity of Data Opportunities
  Example: Hurricane Frances
  Example: Predicting Customer Churn
  Data Science, Engineering, and Data-Driven Decision Making
  Data Processing and "Big Data"
  From Big Data 1.0 to Big Data 2.0
  Data and Data Science Capability as a Strategic Asset
  Data-Analytic Thinking
  This Book
  Data Mining and Data Science, Revisited
  Chemistry Is Not About Test Tubes: Data Science Versus the Work of the Data Scientist
  Summary
2.Business Problems and Data Science Solutions
  From Business Problems to Data Mining Tasks
  Supervised Versus Unsupervised Methods
  Data Mining and Its Results
  The Data Mining Process
    Business Understanding
    Data Understanding
    Data Preparation
    Modeling
    Evaluation
    Deployment
  Implications for Managing the Data Science Team
  Other Analytics Techniques and Technologies
    Statistics
    Database Querying
    Data Warehousing
    Regression Analysis
    Machine Learning and Data Mining
    Answering Business Questions with These Techniques
  Summary
3.Introduction to Predictive Modeling: From Correlation to Supervised Segmentation.
  Models, Induction, and Prediction
  Supervised Segmentation
    Selecting Informative Attributes
    Example: Attribute Selection with Information Gain
    Supervised Segmentation with Tree-Structured Models
  Visualizing Segmentations
  Trees as Sets of Rules
  Probability Estimation
  Example: Addressing the Churn Problem with Tree Induction
  Summary
4.Fitting a Model to Data
  Classification via Mathematical Functions
    Linear Discriminant Functions
    Optimizing an Objective Function
    An Example of Mining a Linear Discriminant from Data
    Linear Discriminant Functions for Scoring and Ranking Instances
    Support Vector Machines, Briefly
  Regression via Mathematical Functions
  Class Probability Estimation and Logistic "Regression"
    Logistic Regression: Some Technical Details
  Example: Logistic Regression versus Tree Induction
  Nonlinear Functions, Support Vector Machines, and Neural Networks
5.Overfitting and Its Avoidance
6.Similarity, Neighbors, and Clusters
7.Decision AnalyticThinking h What Is a Good Model?
8.Visualizing Model Performance
9.Evidence and Probabilities
10.Representing and Mining Text
11.Decision Analytic Thinking Ih Toward Analytical Engineering
12.Other Data Science Tasks and Techniques
13.Data Science and Business Strategy
14.Conclusion
A.Proposal ReviewGuide
B.Another Sample Proposal
Glossary
Bibliography
Index