Difference between Data mining and Data Science? (2024)

Difference between Data mining and Data Science? (1)

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Data mining and data science are the two most important concepts in information technology. Data mining is a process of determining useful information, trends, and patterns from large databases, so that these parameters can be used to solve several business problems. On the other hand, data science is the process of obtaining important insights from the unstructured and structured data by using different analysis tools. Basically, data science is one of the modern emerging fields of computer science and information technology for the study of largescale data analysis.

Read this article to learn more about Data Mining and Data Science and how they are different from each other.

What is Data Mining?

Data mining is a process of extracting useful information, patterns, and trends from raw data. It uses sophisticated numerical algorithms to split the data and compute the probability of future events. There are several types of services in data mining processes, including text mining, web mining, audio, and video mining, pictorial data mining, and social network data mining.

Data mining is done through simple or advanced software. Data mining is known as Knowledge Discovery in Data (KDD). Data mining can include the use of several types of software packages including analytics tools. It can be automated, or it can be largely labor-intensive, where individual workers send specific queries for information to an archive or database.

What is Data Science?

Data Science is an emerging area of computer science that targets information. It is an interdisciplinary area that uses a blend of devices, algorithms, and machine principles to extract usable data from both structured and unstructured records.

Data science is not only statistics or machine learning, it also uses data analysis and modeling to learn the complex world of data. Data scientists are the one responsible for this job and they can collect data from multiple sources, organize and analyze the data, and then connect the findings in a way that efficiently affects business decisions. The objective is to extract useful insights from information.

Difference between Data Mining and Data Science

The following are the important differences between data mining and data science −

S.No.

Data Mining

Data Science

1.

Data mining is a phase of extracting useful data, patterns, and trends from large databases.

Data science defines the process of obtaining valuable insights from structured and unstructured records by using several tools and methods.

2.

The main objective of data mining is to discover properties of existing information that were previously unknown and to find statistical rules or patterns from those data to solve complex computing problems.

The main objective of data science is to use certain specialized computational methods to find meaningful and useful data within a dataset to create important decisions.

3.

In Data mining, the identified trends and patterns are used by organizations to formulate operations, marketing, and financial strategies to fuel business growth.

Data science is scientific research that paves the way for a project program- or portfolio-centric analysis.

4.

Data Mining centers on discovering records from several sources and transforming the data into a useful tool. It can be used across industries.

Data Science makes data-focused products for organizations and drives decisions through the aid of records. It can be used across industries.

5.

Data mining involves the process of data analysis to obtain information.

Data science focuses on the science of the data.

6.

The objective of data mining is to make existing data more valuable.

The objective of the data science is to increase the dominance of the data product.

7.

This is a technique of extracting information and patterns.

Data science is a wider field of studying about data.

8.

Data mining mainly used in business applications.

Data science is mainly used in scientific applications.

9.

Data mining is a part of knowledge discovery in database processes.

Data science is a field of study in different engineering disciplines like cloud computing.

10.

Data mining generally deals with structured data.

Data science can deal with any type of data whether structured, unstructured, or semi-structured.

Conclusion

The most significant difference that you should note here is that data mining is a technique used for business purposes, whereas data science is a field of study of data which is mainly used for scientific purposes.

Kiran Kumar Panigrahi

Updated on: 21-Feb-2023

1K+ Views

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Difference between Data mining and Data Science? (2024)

FAQs

Difference between Data mining and Data Science? ›

Data mining is a process of extracting useful information, patterns, and trends from huge databases. Data science refers to the process of obtaining valuable insights from structured and unstructured data by using various tools and methods. Data mining is a technique. Data science is a field.

What is the difference between data scientist and data miner? ›

The purpose of data science is to create data-centric projects. Data scientists explore, sort, and analyze data that helps businesses in decision-making. On the other hand, the objective of data mining is to find existing data properties that were not known previously.

Do data scientists mine data? ›

In businesses, data scientists typically mine data for information that can be used to predict customer behavior, identify new revenue opportunities, detect fraudulent transactions and meet other business needs.

What are the similarities between data science and data mining? ›

Both face similar challenges and opportunities in dealing with big data, such as scalability, complexity, and diversity. Data mining and data science both involve extracting valuable insights from data. They share common goals of uncovering patterns, trends, and knowledge to inform decision-making.

What is the difference between data mining and data discovery? ›

KDD is the overall process of extracting knowledge from data, while Data Mining is a step inside the KDD process, which deals with identifying patterns in data. And Data Mining is only the application of a specific algorithm based on the overall goal of the KDD process.

What is the highest salary of data mining? ›

Data Mining salary in India ranges between ₹ 1.0 Lakhs to ₹ 5.9 Lakhs with an average annual salary of ₹ 2.0 Lakhs. Salary estimates are based on 55 latest salaries received from Data Minings.

Do you need a degree for data mining? ›

The first step to becoming a data miner is to obtain a relevant undergraduate degree, typically in computer science, data science, statistics or math. You'll want to find an entry-level data-related position, such as data analyst, that can equip you with foundational knowledge and experience.

Are data scientists happy with their job? ›

But to put this into perspective, CareerExplorer also compared 'Data Scientist' to the satisfaction and happiness levels of people in similar careers – where most averaged 3.3 or 3.4 starts out of 5. Suffice it to say, data scientists are pretty happy with their careers, especially those who love what they do!

Can anybody be a data scientist? ›

Can anyone become a data scientist, or do I need a specific background? While a background in mathematics, statistics, or computer science is beneficial, anyone committed to learning and developing the necessary skills can become a data scientist.

What exactly does a data scientist do? ›

Data scientists examine which questions need answering and where to find the related data. They have business acumen and analytical skills as well as the ability to mine, clean, and present data. Businesses use data scientists to source, manage, and analyze large amounts of unstructured data.

Which is best data science or data mining? ›

If you want to discover hidden patterns or associations in data, data mining is a better fit. Tools and techniques: Consider the tools and techniques that are most relevant to your specific project. Data scientists use a broader set of tools, while data mining may involve specialized algorithms.

What are the tools used in data mining? ›

Data Mining tools are software programs that help in framing and executing data mining techniques to create data models and test them as well. It is usually a framework like R studio or Tableau with a suite of programs to help build and test a data model.

What is the key difference between data mining and big data? ›

Big Data refers to the collection of humongous datasets, such as the datasets within excel sheets, that are too large for easy handling. On the other hand, data mining refers to the analysis of large data chunks for extracting relevant and useful information.

What is an example of data mining? ›

Data Mining Examples

Retailers often use data mining techniques to analyze customer purchase history and identify patterns or associations. For example, market basket analysis can reveal that customers who buy diapers are also likely to purchase baby food, leading to cross-selling opportunities.

What are the two major types of data mining? ›

Data mining can be broadly categorized into two main types — predictive data mining and descriptive data mining. Each type serves distinct business needs and offers unique insights.

What are the major issues of data mining? ›

Some of the Data mining challenges are given as under:
  • Security and Social Challenges.
  • Noisy and Incomplete Data.
  • Distributed Data.
  • Complex Data.
  • Performance.
  • Scalability and Efficiency of the Algorithms.
  • Improvement of Mining Algorithms.
  • Incorporation of Background Knowledge.

What is the role of data scientist in data mining? ›

They are responsible for mining valuable information from various sources and transforming it into actionable insights that can drive business growth. In today's data-driven world, organizations rely on data scientists to uncover patterns, identify trends, and develop innovative solutions to complex business problems.

What does a data miner do? ›

Data miners analyze data from different perspectives and summarize it into useful information - information that can be used to increase revenue, cut costs, or both.

What is the role of data mining in data science? ›

Data mining is the process of searching and analyzing a large batch of raw data in order to identify patterns and extract useful information. Companies use data mining software to learn more about their customers. It can help them to develop more effective marketing strategies, increase sales, and decrease costs.

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