Read e-book online Making sense of data I : a practical guide to exploratory PDF
By Glenn J. Myatt
Praise for the First Edition
“...a well-written publication on facts research and knowledge mining that gives a superb foundation...”
“This is a must-read publication for studying functional records and knowledge analysis...”
A confirmed go-to advisor for facts research, Making feel of information I: a pragmatic consultant to Exploratory facts research and information Mining, moment Edition specializes in uncomplicated facts research techniques which are essential to make well timed and exact judgements in a various diversity of initiatives. in response to the authors’ useful adventure in imposing information research and knowledge mining, the hot version offers transparent factors that advisor readers from virtually each box of study.
In order to facilitate the wanted steps while dealing with an information research or facts mining venture, a step by step procedure aids pros in conscientiously reading information and imposing effects, resulting in the improvement of smarter company judgements. The instruments to summarize and interpret facts with the intention to grasp info research are built-in all through, and the Second Edition additionally features:
- Updated routines for either guide and computer-aided implementation with accompanying labored examples
- New appendices with assurance at the freely to be had Traceis™ software program, together with tutorials utilizing information from various disciplines resembling the social sciences, engineering, and finance
- New topical insurance on a number of linear regression and logistic regression to supply a number generic and obvious approaches
- Additional real-world examples of knowledge education to set up a pragmatic heritage for making judgements from data
Making experience of information I: a realistic advisor to Exploratory facts research and knowledge Mining, moment Edition is a superb reference for researchers and pros who have to in achieving potent determination making from facts. The Second Edition can also be an incredible textbook for undergraduate and graduate-level classes in info research and information mining and is acceptable for cross-disciplinary classes discovered inside laptop technology and engineering departments
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Additional info for Making sense of data I : a practical guide to exploratory data analysis and data mining
1 Overview While the central location is a single value that characterizes an individual variable’s data values, it provides no insight into the variation of the data or, in other words, how the different values are distributed around this DISTRIBUTION OF THE DATA 25 location. The frequency distribution, which is based on a simple count of how many times a value occurs, is often a starting point for the analysis of variation. Understanding the frequency distribution is the focus of the following section and can be performed using simple data visualizations and calculated metrics.
For example, the value of the first observation for the first variable is x11 , the value for the second observation’s first variable is x21 , and so on. Throughout the book we will explore different mathematical operations that make use of this generalized form of a data table. The most common way of looking at data is through a spreadsheet, where the raw data is displayed as rows of observations and columns of variables. This type of visualization is helpful in reviewing the raw data; however, the table can be overwhelming when it contains more than a handful of observations or variables.
In situations where data has been collected over time, changes related to the passing of time may no longer be relevant to the analysis. For example, when looking at a variable Cost of production for which the data has been collected over many years, the rise in costs attributable to inflation may need to be considered for the analysis. When data is combined from multiple sources, an observation is more likely to have been recorded more than once. Duplicate entries should be removed. 3 REMOVING OBSERVATIONS AND VARIABLES After an initial categorization of the variables, it may be possible to remove variables from consideration.
Making sense of data I : a practical guide to exploratory data analysis and data mining by Glenn J. Myatt