Making Sense of Data
A Practical Guide to Exploratory Data Analysis and Data Mining
(Sprache: Englisch)
Making Sense of Data covers a series of technical topics that address how to analyze data.
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Making Sense of Data covers a series of technical topics that address how to analyze data.
Klappentext zu „Making Sense of Data “
A practical, step-by-step approach to making sense out of dataMaking Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data.Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and areprovided with concrete discussions of the most universaltasks and technical solutions related to the analysis of data, including: Problem definitions Data preparation Data visualization Data mining Statistics Grouping methods Predictive modeling Deployment issues and applicationsThroughout 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 analysis or data mining project.From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.
A practical, step-by-step approach to making sense out of data
Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data.
Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions 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 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 analysis or data mining project.
From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.
Making Sense of Data educates readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project. The author provides clear explanations that guide the reader to make timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analyzing data and implementing results, leading to the development of smarter business decisions. With a comprehensive collection of methods from both data analysis and data mining disciplines, this book successfully describes the issues that need to be considered, the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data.
Readers are given a solid foundation in the procedures associated with complex data analysis or data mining projects and are provided with concrete discussions of the most universal tasks and technical solutions 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 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 analysis or data mining project.
From summarizing and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, Making Sense of Data addresses the many issues that need to be considered as well as the steps that need to be taken to master data analysis and mining.
Inhaltsverzeichnis zu „Making Sense of Data “
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.2.7 Summary.2.8 Further reading.3. Preparation.3.1 Overview.3.2 Data sources.3.3 Data understanding.3.4 Data preparation.3.5 Summary.3.6 Exercises.3.7 Further reading.4. Tables and graphs.4.1 Introduction.4.2 Tables.4.4 Summary.4.5 Exercises.4.6 Further reading.5. Statistics.5.1 Overview.5.2 Descriptive statistics.5.3 Inferential statistics.5.4 Comparative statistics.5.5 Summary.5.6 Exercises.5.7 Further reading.6. Grouping.6.1 Introduction.6.2 Clustering.6.3 Associative rules.6.4 Decision trees.6.5 Summary.6.6 Exercises.6.7 Further reading.7. Prediction.7.1 Introduction.7.2 Simple regression models.7.3 K-nearest neighbors.7.4 Classification and regression trees.7.5 Neural networks.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.9.3 Advanced data mining.9.4 Further reading.Appendix A Statistical tables.A.1 Normal distribution.A.2 Student?(tm)s t-distribution.A.3 Chi-square distribution.A.4 F-distribution.Appendix B Answers to exercises.Glossary.Bibliography.Index.
Autoren-Porträt von Glenn J. Myatt
GLENN J. MYATT, PhD, is cofounder of Leadscope, Inc., a data mining company providing solutions 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 of industrial and academic research positions. Dr. Myatt is the author of numerous journal articles.
Bibliographische Angaben
- Autor: Glenn J. Myatt
- 2006, 280 Seiten, Maße: 16,1 x 23,4 cm, Kartoniert (TB), Englisch
- Verlag: Wiley & Sons
- ISBN-10: 047007471X
- ISBN-13: 9780470074718
Sprache:
Englisch
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"...the book should be accessible to all its intended readers." (MAA Reviews, December 28, 2007)
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