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Difference Between Data Mining and Data Analysis

Data Mining and Data Analysis are the major steps in any project based on data-driven decisions, and it is required to be done with efficiency to ensure the success of the projects. Nowadays, data analysis and strategy development play a vital role in collecting important information from the available data sets.

First, all the data is kept in the data warehouse, and then it is used for the business intelligence requirements. There are various concepts and views regarding data mining and data analysis, but you can say that both terms are subsets of business intelligence. Data mining and Data analysis are similar, so finding the difference between them is a little bit difficult. Before starting the differentiation between data mining and data analysis, let's understand the two terms separately. Read the given article to know the basic difference between data mining and data analysis.

What is Data Mining?

Data mining refers to a process that is used to turn raw data into meaningful data. Data mining is based on research, so many organizations follow the data mining process to transform data into useful information. It helps the organizations build more innovative strategies, increase sales, generate revenue, and grow a business by cost reduction.

Data Mining Techniques

Below are the data mining techniques

Classification analysis

Classification analysis is used to classify distinct data in a different class. It is used to restore significant information related to data and metadata.

Association Rule Learning

Association rule learning refers to the process that enables to identify relations between distinct variables in a large set of data.

Outlier detection

Outlier detection refers to the data observation in a database that does not match an expected pattern.

Clustering Analysis

The term 'cluster' is the collection of data objects which are similar within the same cluster.

Regression Analysis

Regression analysis is the process of analyzing and identifying the relationship among the different variables.

Data Analysis

Data analysis is a method that can be used to investigate, analyze, and demonstrate data to find useful information. There are several types of that, but usually, people think about the quantitative data first. For example, the data comes after surveying, census data.

Let's understand the concept of data analysis with the help of a day-to-day example. Suppose there is a retail shop like Big Bazar. You can say, some products in Big Bazar are always getting expired before they are sold. It means it is a financial loss for the company. So how to minizine the loss?

Let's have a look at the available data.

The products can be categorized into various categories like food products, beverages, cloth sections, etc. They can further categorize these products and eventually from a tree.

The retail shop manager has the list of products sold on each day, peak hours of the store, products sold during the different hour's zones, number of customers on each day, and a lot of other related information. Now, based on all the information, they can figure out which products sell at what time of the day. You can say they also split it into seasons means what products sell during which season. So that, they can also find which products have very less sell.

Methods of Data Analysis

There are two methods of data analysis: qualitative and quantitative.

Qualitative research

Primarily, it describes the product characteristics. It does not utilize any number. It emphasizes the quality of the product.

Quantitative research

It is the inverse of qualitative research because its primary focus is numbers. Quantitative research is all about quantity.

Difference between Data mining and Data Analysis

Data Mining vs Data Analysis
Data Mining Data Analysis
Data mining is a process of extracting useful information, patterns, and trends from raw data. Data analysis is a method that can be used to investigate, analyze, and demonstrate data to find useful information.
The data mining output gives the data pattern. The data analysis output is a verified hypothesis or insights based on the data.
It includes the intersection of databases, machine learning, and statistics. It requires expertise in computer science, mathematics, statistics, AI.
It is also called KDD. It is of various types - text analytics, predictive analysis, data mining, etc.
It is responsible for extracting useful patterns and trends in data. It is responsible for developing models, testing, and proposing hypotheses using analytical methods.
The best example of a data mining application is in the E-commerce sector, where websites display options of those who purchased and viewed the specific product. The best example of data analysis is the study of the census.

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