SRAM Full Form - Static Random Access Memory

Difference Between Data Mining and Data Analysis

Last Updated : 11 Jan, 2024

1. Data Analysis :
Data Analysis involves extraction, cleaning, transformation, modeling and visualization of data with an objective to extract important and helpful information which can be additional helpful in deriving conclusions and make choices.

The main purpose of data analysis is to search out some important information in raw data so the derived knowledge is often used to create vital choices.

2. Data Mining :
Data mining could be called as a subset of Data Analysis. It is the exploration and analysis of huge knowledge to find important patterns and rules.

Data mining could also be a systematic and successive method of identifying and discovering hidden patterns and data throughout a big dataset. Moreover, it is used to build machine learning models that are further used in artificial intelligence.

Data-Mining-Vs-Data-Analysis

Below is a table of differences between Data Mining and Data Analysis :

Based on	Data Mining	Data Analysis
Definition	It is the process of extracting important pattern from large datasets.	It is the process of analysing and organizing raw data in order to determine useful informations and decisions
Function	It is used in discovering hidden patterns in raw data sets .	In this all operations are involved in examining data sets to fine conclusions.
Data set	In this data set are generally large and structured.	Dataset can be large, medium or small, Also structured, semi structured, unstructured.
Models	Often require mathematical and statistical models	Analytical and business intelligence models
Visualization	It generally does not require visualization	Surely requires Data visualization.
Goal	Prime goal is to make data usable.	It is used to make data driven decisions.
Required Knowledge	It involves the intersection of machine learning, statistics, and databases.	It requires the knowledge of computer science, statistics, mathematics, subject knowledge Al/Machine Learning.
Also known as	It is also known as Knowledge discovery in databases.	Data analysis can be divided into descriptive statistics, exploratory data analysis, and confirmatory data analysis.
Output	It shows the data tends and patterns.	The output is verified or discarded hypothesis

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