Making Sense of Data Making Sense of Data covers a series of technical topics that address how to analyze data. The book is divided into five parts: Introduction; Tables, Graphs, and Statistics; Grouping; Predictions; and Conclusions. Full description
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This book is written in plain language and is useful to you who wants to learn about the business of gathering and analyzing data and accomplishing the objectives you set beforehand. Its most important message I think is that you define the problem so that the data gathered will be tailored to solving it. (Biz India, 22 December 2012)
" a well–written book on data analysis and data mining that provides an excellent foundation " (CHOICE, May 2007)
"This is a must–read book for learning practical statistics and data analysis" (Computing Reviews.com, May 22, 2007)
" the book should be accessible to all its intended readers." (MAA Reviews, December 28, 2006)
"This is a must–read book for learning practical statistics and data analysis" (Computing Reviews.com, May 22, 2007)
" the book should be accessible to all its intended readers." (MAA Reviews, December 28, 2006)
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:
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.
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