What is Data Mining?

What is Data Mining?


Data Mining is a key skill in Data Science. It is the process of categorizing data sets and arranging them into patterns based on abnormalities or trends. Organizations across the world use various methods of data mining to collect insights for decision-making. Without data mining skills, a data science professional cannot separate critical data from a disorganized data source.

Suppose you are working as an analyst, and your manager asks you very specific questions like “what is the current sales trend?”, “what are my customers buying?”, “is my customer buying a mobile? which is the most likely product he may buy along with it?”, “how much do we need to produce to meet the market demand?” how would you answer them? 

To answer all the questions above, one needs to start with mining data. It also helps machine learning engineers in processing the data. Keep reading to learn all about data mining.


What is Data Mining?

Data mining is extracting insights, identifying patterns, discovering, and harvesting information using data analysis and statistics. It is similar to oil, gold, and iron ore mining, which requires a lot of physical and labor work and various chemical processes. Similarly, data mining is where you extract, clean, transform and process the data to answer business questions. This explains the phrase "knowledge mining using data.”

The Stages in Data Mining

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As we are clear on what data mining is, let's understand how data mining is done. 

Data mining has multiple hierarchies and stages. Like mining ores in various steps, data mining constitutes various techniques that cannot be shuffled. So, let us understand the different parts involved in data mining in detail.

Just as different ores have different mining processes, various data mining questions have several techniques to mine. Data mining approaches a similar way of collecting the data, processing the data, transforming the data, and finally, analyzing and representing it. 

The following are the stages in the process of data mining:

  • Data Acquisition: In this process, data is collected from various sources such as Google Analytics, cookies data, and external or internal data. Some data is available in API, some in a data lake, fuel in a warehouse, etc. 
  • Data Preparation: Sometimes, data might be highly disorganized and would need more cleaning. These kinds of data might sometimes require data transformation as well. Some of them might be available in wide format, others in long format.  
  • Data Modelling: In this section, The actual data is divided into training and testing data sets, on top of which statistical models are used to perform the mining. For example, If we are planning for a Twitter sentiment analysis. The sentiment analysis model is built using natural language programming. You can read more about data modeling in the article - what is data modeling?
  • Model Deployment: In model deployment, the model will be deployed as an application or a tool meant to take the decision-making process. 


Advantages of Data Mining

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Here are the major benefits of data mining:

    • Decision-making: Good data mining helps the business make better decisions. For example: if you are a film producer and have released a new film and to understand the film's response, you use data mining to understand what the people are saying about the film. Did they like it or dislike it, and so on.
    • Trend insights: Understanding customer trends are significant. If you overproduce a product and if there is no adequate demand, people may not buy your product, and all the perishable products may exceed the shelf date of the product. For example, Understanding how many of my customers will eat ice cream this summer is very critical.    
    • Why it happened: Data mining helps us understand why my customers are churning out or which customers are defaulting. In addition, data mining can help companies to take preemptive offers or steps to avoid possible losses.  
    • Low-cost solution: If you are a data analyst and planning to deploy a model without performing proper data cleaning and processing, these are data mining steps. The deployed models will be very slow in computation and may also have low accuracy. 
    • Works well with Legacy Systems: If you procrastinate using data mining in big data or modern data platforms. I want to inform you that data mining is a bit old and compatible with new and legacy systems.
Data mining is an essential skill for an analyst because it is a process of transforming data, making precise decisions, and answering critical business questions.

Types of Data Mining Techniques

Here are the various data mining techniques that a data analyst has to be familiar with. 

  • Regression: Regression analysis is one of the top solutions to find the relationship between a dependent variable and an independent variable. What will be the salary of a candidate with X years of experience? 
  • Classification:  Classification analysis is another type of statistical analysis that deals with categorical variables as target variables. A few examples are; whether it will rain today? Will India win the match? And so on.
  • Forecasting: Forecasting is usually applied to time series data. Where do you want to know; What will happen to the trend and seasonality?
  • Clustering: Based on the similarities and dissimilarities between the variables. The variables are grouped into small segments. This process is called clustering. 
  • Association rules mining: It is usually used to understand the relationship between purchased and other products. 

You may also want to read Demonstrate Your Machine Learning Skills With These Projects.


Applications of Data Mining

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By using data mining, we can understand the pattern inside data. Let me help with a few examples of essential data mining. 

  • Data Mining in Retail Domain: In Retail Analytics, organizations use data mining to understand the relationship between your purchase and the products you buy. Data mining can realize the likelihood of a product purchase based on the historical purchase. Then the algorithm will recommend the products to be bought along with the items. 
  • Data Mining in Supply Chain: In the supply chain, data mining is used to understand the trend of sales and demand. So that they can plan their supply chain,  inventory management, and production, it also helps to handle the bullwhip effect.  
  • Data Mining in Digital Marketing: In digital marketing, understanding who is creating the art and targeting potential clients is essential. It helps us to understand which ad campaign is effective and at what time, place, and mode we have advertised.

Data Mining Tools

There are multiple tools which are available for data mining. Here are some of them:

  • Python: Python is an open-source language and one of the most popular software tools used for data science, data visualization, software development, etc. It has a vast user base. There are well-built libraries that are meant for a specific purpose. These are the popular Python libraries that are famous for data mining:
    1. Numpy: NumPy is the Library that deals with numerical operations and helps you perform operations on an array.
    2. Pandas: Pandas help you to import tabular data and perform fundamental transformations such as group by filtering, merging and joining, etc.
    3. Sklearn: This package helps us perform all the machine learning and statistical techniques essential for data analysis. It contains models related to regression, classifications, forecasting, clustering, etc. 
    4. Matplotlib: This package helps us to visualize the data. Using matplotlib, we can create static, dynamic, animated, and continuous visualizations. 
    5. Seaborn: It provides a high-level interface for drawing attractive and informative statistical visuals. 
  • R: R is a statistical analysis tool using with which you can build, test, and analyze statistical models related to data mining. It has some dedicated libraries for the same, which are as follows: 
    1. Dplyr: This library is dedicated for data manipulation, filtering, group by etc. Which are meant to handle most of the functions available in SQL.
    2. Ggplot: It is a visualization tool which is used to create static and dynamic plots. It is also comparable with ShinyR.
    3. Others: Unlike Python where you have a dedicated library to perform all the data mining activities. Here in R we have multiple libraries like tm for text mining, e1071 for SVM and so on.
  • Knime: It is a tool with a decent GUI and requires coding skills. It helps you create pipelines, perform cleaning, inspect, create, evaluate and deploy models.

Data mining is an essential skill for an analyst because it is a process of transforming data and letting the end user make precise decisions and answer critical business questions.

Data Mining vs. Data Analytics

Both data mining and data analytics require similar skill sets and tools. Data mining is a crucial skill in creating a model in data analysis. Data mining emphasizes on analysis and decision-making, which is just two f the subsets of analytics. Data Analytics uses a wide range of hypothesis testing to validate significant and insignificant variables and provide more emphasis on model accuracy. On the other hand, data mining looks into the business value. In data mining, many emphases are provided on data visualization and choosing the right visualization to help a business user in decision-making.  

If you are looking to build a career in the in-demand field of Data Science, data mining is surely a key skill you have to acquire. Whether you want to upgrade your Data Science skill set or start from scratch, join OdinSchool's Data Science Course. Get in touch with one of our career counsellors today!


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Please look at all the blogs written by a Data Science Subject Matter expert, Sangamesh KS.

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