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Data Analysis and Data Mining

As more and more people are becoming connected over the internet and are also becoming more and more dependent to technology, the level of data that happens to be developed over time has been on a gradual rise since the internet and tech boom in 2002*. This sudden increase in data has resulted in various forms of data being developed. One of these forms is known as data analysis where as the other happens to be data mining. These two concepts can closely be assumed to relate, however, this is never the case since these two concepts happen to be rather different in a large number of ways.

For instance, data analysis happens to deal with the extraction, cleaning, transformation, remodeling, visual representation and grouping of data into information that can be consumed and used in making decision or coming up with a particular conclusion. Thus, this does point out that data analysis is mainly used for the main purpose of making sense of complex data that is yet to be processed into understandable information (Zaki & Meira, 2014). Data analysis also happens to be used in testing hypothesis, this results in placing data analysis in being used in statistics, mathematics as well as computer science based structures as a means of deriving a set of findings or conclusions from collected data. 

On the other hand, data mining does happen to follows some principles of data analysis and can be grouped under data mining, however, data mining happens to be the extensive exploration and evaluation of large amounts of data with the purpose of retrieving or identifying a hidden pattern in a large data set. Data mining is also used in developing machine learning and AI system models that happen to be used in conducting effective data analysis. data mining is also known as information discovery since it ends up looking at various sets of data and attaining a fixed finding from the data set presented (Zaki & Meira, 2014). Data mining is also used in a large structure of data that contains immense complexities since it does not require visualization as a result of its initial purpose of making data usable, that is, turning raw data into valuable information.

References

Zaki, M. J., & Meira, W. (2014). Data mining and analysis: fundamental concepts and 

algorithms. Cambridge University Press.

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