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While the analysis of educational data is not itself a new practice, recent advances in educational technology, including the increase in computing power and the ability to log fine-grained data about students' use of a computer-based learning environment, have led to an increased interest in developing techniques for analyzing the large amounts of data generated in educational settings.
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data. In contrast, data mining uses machine learning and statistical models to uncover clandestine or hidden patterns in a large ...
A common practice in data mining is to classify, to look at the attributes of an object or situation and make a guess at what category the observed item belongs to.As new evidence is examined (typically by feeding a training set to a learning algorithm), these guesses are refined and improved.
Metabolomics is a very data heavy subject, and often involves sifting through massive amounts of irrelevant data before finding any conclusions. Data mining has allowed this relatively new field of medical research to grow considerably within the last decade, and will likely be the method of which new research is found within the subject. [28]
Data science is a field that uses scientific and computing tools to extract information and insights from data, driven by the increasing volume and availability of data. [46] Data mining , big data , statistics, machine learning and deep learning are all interwoven with data science.