Data Mining Vs Statistics

Data Mining Vs Statistics

Last updated on 10th Oct 2020, Artciles, Blog

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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 in the two and make you understand where exactly each one is used in real world industry applications.

Criteria Data Mining Statistics
Used forExplorationConfirmation
Data attributeData that is not cleanClean 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.

Data Mining Tutorial for Beginners :

Data mining is the domain that is involved with making predictions with heightened accuracy. Statistics is about analyzing, interpreting and presenting the numerical facts and data in order to derive valuable insights out of it. Data mining actually grew out of the 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 result. As 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 on data that is cleansed. Statistics is more about confirmation and applying the 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 the 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 the process that can work with both numeric and non-numeric data but statistics can work only on the 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.

Key Differences between Data Mining and Statistics

  • Data mining is the beginning of data science and it covers the entire process of data analysis whereas statistics is the base and core partition of data mining algorithm.
  • Data Mining is an exploratory analysis process in which we explore and gather the data first and builds a model on the data to detect the pattern and make theories on them to predict the future outcome or to resolve the issues. Whereas statistic is the confirmative process in which first theories are made and then validation is applied on that theory to test the datasets.
  • As day by day data size is increasing data format is also changing mostly received data is unstructured data which may contain numeric or non-numeric data and both types of data used for data mining but statistics only numeric type of data is used for the probabilistically and mathematical calculation and prediction.
  • Data mining is an inductive process and uses an algorithm like a decision tree, clustering algorithm to derive data partition and generate hypotheses from data whereas statistics is the deductive process i.e. it does not involve any predictions it is used to derive knowledge and verify hypotheses.
  • Data mining is not much concerned about collection or gathering of data as it is exploratory data analysis also data mining is mostly software and computational process for discovering patterns on large datasets whereas statistics is more about the collection of data as to get confirmation on the predicted data we need to gather data analyze it to answer questions. Collected data can be Quantitative, Qualitative, Primary or secondary data.
  • Data cleaning in the data mining is the first step as it helps to understand and correct the quality of data to get accurate final analysis. In data cleaning, a user has the ability to clean inaccurate or incomplete data. Without proper data quality, your final analysis will suffer in accuracy or you could potentially arrive at the wrong conclusion. Whereas in Statistics after collection of data from various sources data cleansing is done and on this cleaned data statistical methods are applied for the confirmative analysis.
  • Data mining is a process of digging deep in the previously available unknown but actionable information from large databases for using it to make some crucial decisions. A set of methods are used to find patterns and relationships within the available data. It is a confluence of various processes including statistics, machine learning, database management, artificial intelligence (AI) and data pattern recognition etc. whereas Statistics is an important component of data mining that offers effective analytics techniques and tools for dealing with a large amount of data for benefiting businesses. It is a science of data learning that covers everything from collecting to using data effectively.
  • Data Mining is essentially applied commercial applications like financial data analysis, retail industry, telecommunication, biology and other scientific detection. Whereas Statistics is used in every data sample to draw out a set of new information. It describes about the character of the data to be analyzed and explore the relation of the data. It uses predictive analytics to run scenarios that help to decide about the future actions. On the other hand, statistics gives breathing into a lifeless data.
  • Some of the popular evolving trends in Data mining are application exploration, visual data mining, biological data mining, web mining, software mining, distributed data mining, real data mining and lots more. And Statistics help to identify new patterns in the available unstructured data.

Data Mining vs Statistics Comparision Table

The differences between Data Mining vs Statistics are explained in the points presented below:

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Data Mining Statistics
Explore and gather data first, builds model to detect patterns and make theories.It provides theories to test using statistical.
Data used is Numeric or Non numeric.Data used is Numeric.
Inductive Process (Generation of new theory from data)Deductive Process (Does not involve making any predictions)
Data collection is less important.Data collection is more important.
Data Cleaning is done in data mining.Clean data is used to apply statistical method.
Needs less user interaction to validate model hence, easy to automate.Needs user interaction to validate model hence, difficult to automate.
Suitable for large data setsSuitable for smaller data sets
It’s an algorithm which learns from data without using any programming rule.Formalization of relationship in data in the form of mathematical equation
Use heuristics think (rules used to form judgments and make decisions)Does not have scope for heuristic think.
Classification, Clustering, Neural network, Association, Estimation, Sequence based analysis, VisualizationDescriptive Statistical, Inferential Statistical
Financial Data Analysis, Retail Industry, Telecommunication Industry, Biological Data Analysis, Certain Scientific Applications etc.Demography, Actuarial Science, Operation research, Biostatistics, Quality Control etc.


To conclude in any organization due to the emergence of big data with big volume and different velocity data plays an important role and predict outcomes data mining and statistics is an integral part. Data mining will always use statistical thinking to draw output hence, both Data Mining and Statistics will grow inevitably in the near future. And it is using statistics on large data user/organization need to use data mining thinking and approaches.

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