How different is data mining from statistics?

Analyzing data is estimated as a tool to mine the valuable data from a huge volume available and data mining as well as statistics are fields that work with this goal in mind. Now, it is also said that statistics can be a core aspect for data mining. Other activities involved in data mining include data analysis, as well.

Now, statistics can be helpful for businesses to determine the pattern that also help to weed out the differences between some random facts from significant or important findings. It also helps businesses to predict the outcome from analysis and reports etc. Hence, you can conclude that data mining and statistics can both be adopted as data analysis techniques and can help better in arriving at more informed decisions.

In the meanwhile, let’s take a look to see how similar or different they are from each other.

Define data mining

When you mine out relevant information from the existing huge database of earlier unknown yet comprehensible and actionable data, the process can be called as data mining. This data can be used to make informed business decisions, guiding a business to success. At the same time, it is also considered as a technique used to determine the earlier unknown associations and patterns within data.

Hence, data mining can be considered as an assembly of fields such as artificial intelligence, database management, statistics, machine learning, data visualization and pattern recognition etc. Using data mining process, it is now possible to arrive at predictive information based on the data collected from the data bases. It has also been established as the application of statistical methods, in a way.

Define statistics

Meantime, when you consider statistics, it is a significant component of data mining, which offers the tools and techniques for analyzing a huge volume of data. It can also be said to be the science of learning, based on data. It also consists of extracting, organizing and analyzing data. Though, considered as an aspect of data mining with the same goal, it is however seen that not every statistician can cater to the demands for a data analyst. 

Similarities and differences between data mining and statistics

Even if both concepts, in a way means to learn from data or to discover, determine structures in them etc., with the aim to convert data to information, their entire approach to the process is entirely different. This marks the difference in the concepts, despite the overlapping techniques.

Using its techniques to identify the valid and relevant properties for data, statistics, however is considered as a method to measure data. On the other hand,  when you consider data mining it helps with building models that can help determine the patterns and associations in data, especially from within huge volume of databases.

Given below are some of the most used methods of data mining as well as types of statistics in data analysis: 

Widely accepted data mining methods

The techniques of data mining may vary based on the kind of data that you try to decipher. Let’s have a look at these techniques:

·         Classification

·         Clustering

·         Neural networks

·         Association

·         Sequence based analysis

·         Estimation

·         Visualization

Methods of statistical analysis

At the same time, the types of statistical analysis methods includes descriptive and inferential. Descriptive statistics is chiefly used to organize or summarize the data. Same ways, using these summaries to arrive at informed conclusions is known as inferential statistics.

Conclusion: Thus, from here you can conclude that though both statistics and data mining can be used as tools to arrive at informed decisions, both use different techniques of approach, both to be used in its separate context.

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