In today’s world of huge amounts of data being generated at breakneck speeds, there are a lot of terms that come up during the course of discussion in corporate boardrooms on a daily basis. Two of the very common terms that are being increasingly used are “Data Mining” and “Statistics”. This blog will help you understand each of these terms, bring out the difference between the two, and make you understand where exactly each one is used in real-world industry applications.
| Criteria | Data Mining | Statistics |
| Methodology | Inductive | Deductive |
| Variables | Large | Small |
| Used for | Exploration | Confirmation |
| Data attribute | Data that is not clean | Clean data |
Data mining and statistics have a lot of overlap but then they have a lot of distinct features as well. The process of data mining includes parsing through huge volumes of data and coming up with hidden patterns, relationships, and such other aspects that can prove to have huge implications for businesses.
Statistics is more about finding the various patterns in data using tried and tested mathematical models, formulae, and other aspects. Data mining is more about using various trial-and-error methods in the hope of finding something more useful.
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Data mining is the domain that is involved with making predictions with heightened accuracy. Statistics is about analyzing, interpreting, and presenting numerical facts and data in order to derive valuable insights from it. Data mining actually grew out of database technology and it has now become a multi-disciplinary field that encompasses a lot of the subjects in machine learning, statistics, and other processes to extract hidden information and patterns from raw data and convert it into nuggets of information.
The process of data mining is through the use of clustering, classification, regression, and other aspects. When it comes to data mining some of the most important concepts include the process of data cleansing, data inspection, data preparation, and more.
Today more and more data mining techniques use the process of artificial intelligence in order to gain an upper edge when compared to the traditional means of data mining. At the end both data mining and statistics try to do the same thing which is to find some mapping between the input and the output in this world. Statistics uses the method of stochastic approach in order to model the world. Once there is a proper model then you can extract more samples from the model.
The field of Data Mining gives little importance to the process of how you come to get some results. The main goal of the data mining process is to come up with enough inferences or results that can justify a certain decision in the real world.
Data mining is more about digging data, discovering patterns, and coming up with theories to get to inferences. But the methods of statistical analysis can be applied only to data that is cleansed. Statistics is more about confirmation and applying various theories. The size of data is large in data mining whereas for statistics it works on small data sets. Data mining is more about an exploratory approach wherein the data is dug out first, the patterns are discovered or hidden patterns and then the theories are made. Whereas statistics is the domain of providing the theory first and then testing it using various statistical tools. Data mining uses a lot of heuristic thinking whereas the methods of statistics do not use a lot of heuristic thinking.
Data mining is a process that can work with both numeric and non-numeric data but statistics can work only on numeric data. Estimation, classification, neural networks, clustering, association, and visualization are used in data mining. Descriptive analytics and inferential analytics are the most important statistical methods used.
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