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Difference between Data Mining and Statistics?
Data mining is the technique of exploration and analysis via the automatic or semiautomatic method of massive quantities of facts as a way to discover significant patterns and policies. It is the process of selection, exploration, and modeling of large quantities of data to discover regularities or relations that are at first unknown to obtain clear and useful results for the owner of the database.
Data mining is the procedure of exploration and analysis by automatic or semiautomatic means of huge quantities of data to find meaningful patterns and rules. It is not limited to the use of computer algorithms or statistical techniques. It is a process of business intelligence that can be used together with information technology to support company decisions.
Data Mining is similar to Data Science. It is carried out by a person, in a specific situation, on a particular data set, with an objective. This process includes various types of services such as text mining, web mining, audio and video mining, pictorial data mining, and social media mining. It is done through software that is simple or highly specific.
By outsourcing data mining, all the work can be completed quicker with low operation costs. Specialized firms can also use new technologies to set data that is impossible to place manually. There are tonnes of information available on various platforms, but very little knowledge is accessible.
Statistics refers to the analysis and presentation of numeric data, which is the major part of all data mining algorithm. It supports tools and analytics methods to deal with a huge amount of data. Statistics incorporates planning, designing, gathering information, analyzing, and reporting research findings. Due to these statistics is not only limited to mathematics, but a business analyst also uses statistics to solve business problems.
Inferential statistics is used for a sample to estimate the values of a population’s parameters. It can carry out hypothesis tests to see if two datasets are similar or disparate. It is used to conduct linear- or multiple-regression analysis to explain causation.
Hypothesis testing can numerically compare two datasets. For instance, it can feel(hypothesize) that this sales volume is similar, or better than that of the main competitor. It can use hypothesis testing to mathematically confirm or reject this assumption. Correlation analysis is a simple tool to isolate the variables of interest from several random variables, often observed in huge datasets, to see which business variables significantly affect the desired business outcome.
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