Making sense of data了解数据：探索数据分析与数据挖掘实用指南 pdf kindle caj 下载 azw3 umd 电子版 夸克云

　　A practical, step-by-step approach to *** sense out of data

Making Sense of Data educates readers on the steps and issues that need to be c***idered in order to successfully complete a data ***ysis or data mining project. The author provides clear explanati*** that guide the reader to make timely and accurate decisi*** from data in almost every field of study. A step-by-step approach aids professionals in carefully ***yzing data and implementing results, leading to the development of smarter business decisi***. With a comprehensive collection of methods from both data ***ysis and data mining disciplines, this book successfully describes the issues that need to be c***idered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision *** from data.

Readers are given a solid foundation in the procedures associated with complex data ***ysis or data mining projects and are provided with concrete discussi*** of the most universal tasks and technical soluti*** related to the ***ysis of data, including:

* Problem definiti***

* Data preparation

* Data visualization

* Data mining

* Statistics

* Grouping methods

* Predictive modeling

* Deployment issues and applicati***

Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data ***ysis or data mining project.

From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relati***hips in the data, to *** predicti*** from the data, Making Sense of Data addresses the many issues that need to be c***idered as well as the steps that need to be taken to master data ***ysis and mining.

Preface

1 Introduction

　1.1 Overview

　1.2 Problem definition

　1.3 Data preparation

　1.4 Implementation of the ***ysis

　1.5 Deployment of the results

　1.6 Book outline

　1.7 Summary

　1.8 Further reading

2 Definition

　2.1 Overview

　2.2 Objectives

　2.3 Deliverables

　2.4 Roles and resp***ibilities

　2.5 Project plan

　2.6 Case study

　　2.6.1 Overview

　　2.6.2 Problem

　　2.6.3 Deliverables

　　2.*** Roles and resp***ibilities

　　2.6.5 Current situation

　　2.6.6 Timetable and budget

　　2.6.7 Cost/benefit ***ysis

　2.7 Summary

　2.8 Further reading

3 Preparation

　3.1 Overview

　3.2 Data sources

　3.3 Data understanding

　　3.3.1 Data tables

　　3.3.2 Continuous and discrete variables

　　3.3.3 Scales of measurement

　　3.3.4 Roles in ***ysis

　　3.3.5 Frequency distribution

　3.4 Data preparation

　　3.4.1 Overview

　　3.4.2 Cleaning the data

　　3.4.3 Removing variables

　　3.4.4 Data transformati***

　　3.4.5 Segmentation

　3.5 Summary

3.6 Exercises

　3.7 Further reading

4 Tables and graphs

　4.1 Introduction

　4.2 Tables

　　4.2.1 Data tables

　　4.2.2 Contingency tables

　　4.2.3 Summary tables

　4.3 Graphs

　　4.3.1 Overview

　　4.3.2 Frequency polygrams and histograms

　　4.3.3 Scatterplots

　　4.3.4 Box plots

　　4.3.5 Multiple graphs

　4.4 Summary

　4.5 Exercises

　4.6 Further reading

5 Statistics

　5.1 Overview

　5.2 Descriptive statistics

　　5.2.1 Overview

　　5.2.2 Central tendency

　　5.2.3 Variation

　　5.2.4 Shape

　　5.2.5 Example

　5.3 Inferential statistics

　　5.3.1 Overview

　　5.3.2 Confidence intervals

　　5.3.3 Hypothesis tests

　　5.3.4 Chi-square

　　5.3.5 One-way ***ysis of variance

　5.4 Comparative statistics

　　5.4.1 Overview

　　5.4.2 Visualizing relati***hips

　　5.4.3 Correlation coefficient (r)

　　5.4.4 Correlation ***ysis for more than two variables

　5.5 Summary

　5.6 Exercises

　5.7 Further reading

6 Grouping

　6.1 Introduction

　　6.1.1 Overview

　　6.1.2 Grouping by values or ranges

　　6.1.3 Similarity measures

　　6.1.4 Grouping approaches

　6.2 Clustering

　　6.2.1 Overview

　　6.2.2 Hierarchical agglomerative clustering

　　6.2.3 K-means clustering

　6.3 Associative rules

　　6.3.1 Overview

　　6.3.2 Grouping by value combinati***

　　6.3.3 Extracting rules from groups

　　6.3.4 Example

*** Decision trees

　　***.1 Overview

　　***.2 Tree generation

　　***.3 Splitting criteria

　　***.4 Example

　6.5 Summary

　6.6 Exercises

　6.7 Further reading

7 Prediction

　7.1 Introduction

　　7.1.1 Overview

　　7.1.2 Classification

　　7.1.3 Regression

　　7.1.4 Building a prediction model

　　7.1.5 Applying a prediction model

　7.2 Simple regression models

　　7.2.1 Overview

　　7.2.2 Simple linear regression

　　7.2.3 Simple nonlinear regression

　7.3 K-nearest neighbors

7.3.1 Overview

　　7.3.2 Learning

　　7.3.3 Prediction

7.4 Classification and regression trees

　　7.4.1 Overview

　　7.4.2 Predicting using decision trees

　　7.4.3 Example

　7.5 Neural networks

　　7.5.1 Overview

　　7.5.2 Neural network layers

　　7.5.3 Node calculati***

　　7.5.4 Neural network predicti***

　　7.5.5 Learning process

　　7.5.6 Backpropagation

　　7.5.7 Using neural networks

　　7.5.8 Example

7.6 Other methods

　　7.7 Summary

　　7.8 Exercises

　　7.9 Further reading

8 Deployment

　8.1 Overview

　8.2 Deliverables

　8.3 Activities

　8.4 Deployment scenarios

　8.5 Summary

　8.6 Further reading

9 Conclusi***

　9.1 Summary of process

　9.2 Example

　　9.2.1 Problem overview

　　9.2.2 Problem definition

　　9.2.3 Data preparation

　　9.2.4 Implementation of the ***ysis

　　9.2.5 Deployment of the results

　9.3 Advanced data mining

　　9.3.1 Overview

　　9.3.2 Text data mining

　　9.3.3 Time series data mining

　　9.3.4 Sequence data mining

　9.4 Further reading

Appendix A Statistical tables

　A.1 Normal distribution

　A.2 Student’s t-distribution

　A.3 Chi-square distribution

　A.4 F-distribution

Appendix B Answers to exercises

Glossary

Bibliography

Index

GLENN J. MYATT, PhD, is cofounder of Leadscope, Inc., a data mining company providing soluti*** to the pharmaceutical and chemical industry. He has also acted as a part-time lecturer in chemoinformatics at The Ohio State University and has held a series o

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书籍介绍

A practical, step-by-step approach to *** sense out of data

Making Sense of Data educates readers on the steps and issues that need to be c***idered in order to successfully complete a data ***ysis or data mining project. The author provides clear explanati*** that guide the reader to make timely and accurate decisi*** from data in almost every field of study. A step-by-step approach aids professionals in carefully ***yzing data and implementing results, leading to the development of smarter business decisi***. With a comprehensive collection of methods from both data ***ysis and data mining disciplines, this book successfully describes the issues that need to be c***idered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision *** from data.

Readers are given a solid foundation in the procedures associated with complex data ***ysis or data mining projects and are provided with concrete discussi*** of the most universal tasks and technical soluti*** related to the ***ysis of data, including:

* Problem definiti***

* Data preparation

* Data visualization

* Data mining

* Statistics

* Grouping methods

* Predictive modeling

* Deployment issues and applicati***

Throughout the book, the author examines why these multiple approaches are needed and how these methods will solve different problems. Processes, along with methods, are carefully and meticulously outlined for use in any data ***ysis or data mining project.

From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relati***hips in the data, to *** predicti*** from the data, Making Sense of Data addresses the many issues that need to be c***idered as well as the steps that need to be taken to master data ***ysis and mining.