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Making sense of data. A practical guide to exploratory data analysis and data mining. (English) Zbl 1163.68012

Wiley-Interscience. Hoboken, NJ: John Wiley & Sons (ISBN 0-470-07471-X/pbk; 978-0-470-10102-5/ebook). xii, 280 p. (2007).
Almost every field of study generates a large amount of data. The volume of data that is generated leads to information overload and the ability to make sense of all this data is becoming an increasingly important, though hard, task. It requires an understanding of exploratory data analysis and data mining as well as an appreciation of the subject matter, business processes, software deployment, project management methods, change management issues, and so on. It is the purpose of this book to describe a practical approach for making sense out of data. More specifically, it guides readers on the steps and issues that need to be considered in order to successfully complete a data analysis or data mining project by providing clear explanations that help in making timely and accurate decisions from data in almost every field of study. A step-by-step approach aids professionals in carefully analysing 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, the book successfully describes the issues that need to be considered and the steps that need to be taken, and appropriately treats technical topics to accomplish effective decision making from data.
Within 9 chapters, the book covers a series of topics relating to the process of making sense of data, including problem definition, data preparation, visualisation and mining, statistics, grouping methods, predictive modelling and deployment issues and applications. It is focused on practical approaches and contains information on 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 summarising and interpreting data, to identifying non-trivial facts, patterns, and relationships in the data, to making predictions from the data, the book 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.
Each chapter of the book includes a further-reading section that highlights additional books and online resources that provide background and other information. Moreover, at the end of selected chapters there are sets of exercises designed to help in understanding the respective chapter’s material. The book is, finally, accompanied by a website that contains additional resources including software, data sets, and tutorials to help understanding how to implement the topics covered in the book.

MSC:

68P01 General topics in the theory of data
68T05 Learning and adaptive systems in artificial intelligence
68-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to computer science
62-01 Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics
62-07 Data analysis (statistics) (MSC2010)
62H30 Classification and discrimination; cluster analysis (statistical aspects)

Citations:

Zbl 1166.68011

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