Data Mining vs Data Science: Difference and Comparison

Analyzing the data is a difficult task with the rise in the digital world. For that, people go for professionals like data mining and data science people.

They will help scrap these data using programming languages, analyze them, and then provide a better solution.

They use problem-solving, mathematical skills, and concepts to arrive at this solution.

Key Takeaways

  1. Data mining focuses on extracting patterns from large data sets, while data science covers the entire data processing pipeline.
  2. Data science involves interdisciplinary skills, whereas data mining primarily requires statistical and computational knowledge.
  3. Data science applications range from decision-making to predictive analysis, while data mining supports pattern recognition and anomaly detection.

Data Mining vs Data Science

Data Mining is the process of analyzing large amounts of data to extract valuable insights and is used in various applications. Data Science is a broader field that encompasses data mining and other related disciplines such as statistics, machine learning, and computer science.

Data Mining vs Data Science

Organizations use data mining to solve large business problems by extracting specific data from a huge set of given databases.

It is used in various applications such as in the healthcare sector, manufacturing engineering, financial banking, fraud detection, education, lie detection, and market basket analysis.

Having a basic understanding of databases and related programming languages will be useful in data mining. 

Data science is a field where people will perform advanced data analysis. There are many high-paying jobs available for data scientists to do because of the digital world that we live in.

The two main languages that are mainly involved in learning data science are R and Python. People need a strong grip on these two languages and good problem-solving skills to succeed in this job. 

Comparison Table

Parameters of ComparisonData MiningData Science
DefinitionIt is a field that involves dealing with large amounts of dataIt is a technique used for extracting important information from a huge amount of data
PurposeScientific purposeBusiness purpose
Data typeStructured, semi-structured, and unstructured dataStructured data
GoalIt helps to make data more stable  It is used to make data-centric products for an organization
Another nameData archaeologyData-driven science

What is Data Mining?

With the help of this method, you can increase revenue costs, improve customer relationships, and can reduce risks. In data mining, you must clean the raw data and find the patterns.

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The next process is creating models. Once you have created the models, you should test those models. You need to know about machine learning, statistics, and database systems for this.

There are many types of data mining available such as pictorial data mining, social media mining, audio mining, text mining, web mining, and video mining. Data mining can also be done using Excel.

For this, you need to know about both Excel and SQL databases. Many big software companies do data mining. Among them, Sisense stands in the first position. With the help of data mining, organizations can enable knowledge-based data easily.

It is one of the cost-effective processes when you compare it with other statistical data applications. It is one of the quick processes where you can analyze a large amount of data within a short period.

The downside of data mining is some organizations will sell user data to some other organizations for money. Data analytics software needs very advanced training to work. You cannot simply work with normal software. 

data mining

What is Data Science?

Data science is the form of cleansing and manipulating the data for performing advanced data analysis. It is a field of study where it involves programming skills, mathematical and statistical knowledge.

It will generate good insight. Based on that, analysts will turn the business into a better way. Data scientists find which questions need answering.

Based on that, they will have to find the relevant data. For this, they need to have business analytical skills and the ability to clean and present the data.

Many business organizations use data scientists for analyzing and managing a large amount of data. It is a field where you can get insight into both structured and unstructured data.

They need to use different scientific methods and algorithms to solve the data. It is one of the good careers when it comes to studying purposes.

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The major topics that are involved in data science are statistics, business intelligence, mathematics, algorithms, coding, data structures, and machine learning.

Because of the evolution of IoT, which is nothing but the Internet of Things, there will be a great demand for data scientists in the future. Millions of jobs will arise for data scientists.

To do a data science course, you need to have a bachelor’s degree in the related field. It would be good if you pursued a master’s degree rather than self-learning, as many people are struggling to find jobs after self-learning. 

data science

Main Differences Between Data Mining and Data Science

  1. Data mining is an area where people will deal with large amounts of data. On the other hand, data science involves extracting information from a huge amount of data.
  2. The main purpose of data mining is scientific. On the other hand, the main purpose of data science is business.
  3. The data types involved in data mining are structured, semi-structured, and unstructured. On the other hand, the data type involved in data science is structured.
  4. The goal of data mining is to make the data more stable. On the other hand, data science aims to make the data-centric towards an organization.
  5. Data mining is also called data archaeology. On the other hand, data science is also called data-driven science. 
Difference Between Data Mining and Data Science

Last Updated : 18 June, 2023

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8 thoughts on “Data Mining vs Data Science: Difference and Comparison”

  1. I find it ironic that data analytics software needs advanced training when its purpose is to simplify data tasks.

  2. Data science and data mining are both very interesting fields but require vast knowledge and skills to excel in. I’m curious to know more about the advantages and disadvantages of each.

  3. It seems the study of data science is a very promising field, considering the number of jobs that will be available as the world becomes more digital.

  4. Data mining appears to have certain ethical concerns that need to be addressed with respect to user data and privacy.

  5. The article provides an in-depth understanding of the key differences between data mining and data science. It’s crucial for those aiming to venture into these fields.


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