An Introduction to Data Analytics: Its Types and Its Applications

An Introduction to Data Analytics: Its Types and Its Applications

A. Sheik Abdullah, S. Selvakumar, A. M. Abirami
Copyright: © 2017 |Pages: 14
DOI: 10.4018/978-1-5225-2031-3.ch001
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Abstract

Data analytics mainly deals with the science of examining and investigating raw data to derive useful patterns and inference. Data analytics has been deployed in many of the industries to make decisions at proper levels. It focuses upon the assumption and evaluation of the method with the intention of deriving a conclusion at various levels. Various types of data analytical techniques such as predictive analytics, prescriptive analytics, descriptive analytics, text analytics, and social media analytics are used by industrial organizations, educational institutions and by government associations. This context mainly focuses towards the illustration of contextual examples for various types of analytical techniques and its applications.
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Analytics Process Model

The Mechanism of analytics has been used variantly with machine learning, data science and knowledge discovery. The process model initially starts with the data source which is in raw form of representation. The data needed for analysis has to be selected with accordance to the problem need for data interpretation. The identified data may contain various missing fields, irrelevant data items. This has to be resolved and cleaned. Then the data has to be transformed accordingly to the necessary format for evaluation and this can be made by the data standardization techniques such as min-max normalization, Z-score normalization and normalization by decimal scaling. As an outcome the final evaluated pattern provides the visualized data representation of the data which can be fed up for evaluation and interpretation. The workflow of the process model is depicted in Figure 1.

Figure 1.

Analytics process model

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Types Of Analytics

Predictive Analytics

Predictive analytics mainly deals with the mechanism of predicting or observing the target value of measure. The value of measure signifies the performance of the analytical model which is being developed. There by the nature of the developed model can be ascertained with the measured value. Hence the term predictive analytics if often said to be supervised learning because the target variable will be known in prior with accordance to the definition of the tuple of record (T. Hastie, R. Tibshirani, & Friedman, 2001). There are various sorts of algorithms used in predicting the nature of a data or a real world problem, such as:

  • 1.

    Linear regression

  • 2.

    Logistic regression

  • 3.

    Support vector machines

  • 4.

    Neural network

  • 5.

    Decision trees

  • 6.

    Ensemble methods such as boosting and bagging

Let us discuss about one of the techniques in predictive analytics such as linear Regression.

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