[下载] Making Sense of Data I

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收藏 2009-10-13

坛子里已经有 II 了，我来传个 I
Description
A practical, step-by-step approach to making sense out of data

Making Sense of Data educates readers on the steps and issues that needto be considered in order to successfully complete a data analysis ordata mining project. The author provides clear explanations that guidethe reader to make timely and accurate decisions from data in almostevery field of study. A step-by-step approach aids professionals incarefully analyzing data and implementing results, leading to thedevelopment of smarter business decisions. With a comprehensivecollection of methods from both data analysis and data miningdisciplines, this book successfully describes the issues that need tobe considered, the steps that need to be taken, and appropriatelytreats technical topics to accomplish effective decision making fromdata.

Readers are given a solid foundation in the procedures associated withcomplex data analysis or data mining projects and are provided withconcrete discussions of the most universal tasks and technicalsolutions related to the analysis of data, including:
* Problem definitions
* Data preparation
* Data visualization
* Data mining
* Statistics
* Grouping methods
* Predictive modeling
* Deployment issues and applications

Throughout the book, the author examines why these multiple approachesare needed and how these methods will solve different problems.Processes, along with methods, are carefully and meticulously outlinedfor use in any data analysis or data mining project.

From summarizing and interpreting data, to identifying non-trivialfacts, patterns, and relationships in the data, to making predictionsfrom the data, Making Sense of Data addresses the many issues that needto be considered as well as the steps that need to be taken to masterdata analysis and mining.

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vrooadk

2009-10-13 02:16:27

Preface.

1. Introduction.

1.1 Overview.

1.2 Problem definition.

1.3 Data preparation.

1.4 Implementation of the analysis.

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 responsibilities.

2.5 Project plan.

2.6 Case study.

2.6.1 Overview.

2.6.2 Problem.

2.6.3 Deliverables.

2.6.4 Roles and responsibilities.

2.6.5 Current situation.

2.6.6 Timetable and budget.

2.6.7 Cost/benefit analysis.

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 analysis.

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 transformations.

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 analysis of variance.

5.4 Comparative statistics.

5.4.1 Overview.

5.4.2 Visualizing relationships.

5.4.3 Correlation coefficient (r).

5.4.4 Correlation analysis 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 combinations.

6.3.3 Extracting rules from groups.

6.3.4 Example.

6.4 Decision trees.

6.4.1 Overview.

6.4.2 Tree generation.

6.4.3 Splitting criteria.

6.4.4 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 calculations.

7.5.4 Neural network predictions.

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. Conclusions.

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 analysis.

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.