# Difference Between Data Mining and Statistics

Analyzing previous and present data is all about predicting future issues. Many organizations use data mining and statistics to make data-driven decisions which are the primary part of data science. Both terms data mining and statistics are a bit confusing since it sounds similar, but it is different. Statistics form the major part of data mining, which includes the overall procedure of data analysis. In this article, we will discuss what data mining is, statistics, and the difference between data mining and statistics.

## What is Data Mining?

Data mining is a process of extracting useful information, pattern, and trends from huge data sets and utilizes them to make a data-driven decision. Data mining comprises various processes, such as web mining, text mining, and social media mining. Data mining can be done through simple or complex software. Data mining is known as Knowledge Discovery in Data (KDD).

## Process of Data Mining

The data mining process is divided into five categories

Information Gathering:

Identify information from huge data sets and update it to decentralized data warehouses.

Store and Manage Data:

This step stores data in distributed storage, in-house servers, or the cloud (e.g., Azure).

Modeling:

Modeling involves the business team; subject matter experts will access the data, apply sampling and transmission in data, and remove all the irrelevant and incomplete data.

Deployment Models:

In this stage, a deployment plan is made that helps to manage the data mining model.

Visualize Data:

In this stage, data presentation in different formats takes place so that the end-users can easily understand. For example, graphs, charts, models, decision tree format, etc.

## What are Statistics?

Statistics refers to the analysis and presentation of numeric data, which is the major part of all data mining algorithm. It provides tools and analytics techniques 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.

## Difference between data mining and Statistics

Data Mining Statistics
Data mining is a process of extracting useful information, pattern, and trends from huge data sets and utilizes them to make a data-driven decision. Statistics refers to the analysis and presentation of numeric data, and it is the major part of all data mining algorithm.
The data used in data mining is numeric or non-numeric. The data used in the statistic is numeric only.
In data mining, data collection is not more important. In statistics, data collection is more important.
The types of data mining are clustering, classification, association, neural network, sequence-based analysis, visualization, etc. The types of statistics are descriptive statistical and Inferential statistical.
It is suitable for huge data sets. It is suitable for smaller data set.
Data mining is an inductive process. It means the generation of new theory from data. Statistics is the deductive process. It does not indulge in making any predictions.
Data cleaning is a part of data mining. In statistics, clean data is used to implement the statistical method.
It requires less user interaction to validate the model, so it is easy to automate. It requires user interaction to validate the model, so it is complex automate.
Data mining applications include financial Data Analysis, Retail Industry, Telecommunication Industry, Biological Data Analysis, Certain Scientific Applications, etc. The application of statistics includes biostatistics, quality control, demography, operational research, etc.