Businesses & industries have started to realize that there’s a huge value hiding in the massive amount of data they capture nearly every day. They regularly try to employ various new techniques to realize that value. Among all the popular data-related terms, probably the most popular ones are ‘Data science’ and ‘Data mining. Many people use these terms interchangeably; they come with significant differences. Sometimes it might be confusing to some people to distinguish between Data Science and Data Mining, so reading this article, will clarify your concepts about Data Science and Data Mining.
What Is Data Science?
Data science is a domain in which information & knowledge are extracted from the data using scientific methods, algorithms, and processes. It can be explained as a combination of various mathematical tools, algorithms, statistics, and machine learning (ML) techniques. These techniques are thus used to find the hidden patterns and valuable insights from the data, which helps decision-making. Data science deals with both structured as well as raw data. It is related to both big data & data mining. Data science involves studying the historical trends and thus using its conclusions to redefine present trends and predict future trends.
Application of Data Science
Application of data science is nearly in all industries today. However, the following industries lead the usage of Data science:
1. Manufacturing: Manufacturing uses Data Science for various purposes. DS is primarily used in manufacturing to improve efficiency, reduce risk, and increase profit. In addition, Data Science may be utilized to boost productivity, streamline operations, and forecast trends.
2. E-commerce: E-commerce and retail are among the most critical sectors that need extensive information evaluation. By monitoring customer activities and using information analytics effectively, e-commerce businesses can forecast sales, earnings, and losses and influence users into buying products.
3. Healthcare: Daily data, digital medical records, accounting, clinical services, information via health trackers, and other sources generate massive amounts of data. This creates a significant possibility for healthcare practitioners to improve client care by using meaningful insights from past patient data. DS is the driving force behind it.
4. Banking: Banks are among the first to use IT knowledge to improve procedures and safety. They utilize technologies to understand their clients better, keep them, and attract new ones. By knowing their activity habits, data analysis assists banking services in engaging with consumers more effectively.
5. Telecommunication: Each organization in the telecommunications industry presents a consistent representation of its information across departments. When data from multiple information sets are received, the company can use the recommendations of the majority of its sections to find the best solution for each problem.
What is Data Mining?
Data Mining is a technique to extract vital information and knowledge from an extensive set/library of data. It derives insight by carefully extracting, reviewing, and processing the vast data to find out patterns and co-relations which can be necessary for the business. It is analogous to gold mining, where gold is extracted from rocks and sands. It is a technical part of the Knowledge Discovery in Data Base processes (KDD). The goal is to make data more vital and usable, i.e., by extracting only necessary information.
What are Data Mining Processes?
The steps below are involved in the data mining process:
- Data Understandings: Data collection is taken in this step, and all the gathered data is accumulated in the tool (if you use any tool). After this, the data is listed with its source data, location, and how it is achieved; if any problem occurs, data is visualized and quired to check its completeness.
- Business Understandings: Prominently, the central aspect is understanding the nature & work. The nature of the business is introduced, and the significant factors that help to achieve the target are subsequently discovered.
- Modeling: It involves choosing data mining techniques, for instance, generation of test design for evaluation of the selected model, decision tree induction, creating a model from the data sets, and analyzing the model with experts to know the result.
- Data Preparation: Data preparation involves selecting valuable data, cleaning the data, constructing attributes from data, and data integration from multiple databases.
- Evaluation: As the name itself suggests, evaluation determines the degree to which the created model meets business demands. This is done by testing the model based on real & practical applications.
Deployment: At this stage, a deployment plan is made, and a strategy to maintain and monitor the data mining model results and check the usefulness is formed.
Applications of Data Mining
Data mining is used in various industries to streamline their decision & precisely implement their campaigns. However, here are a few most common applications of Data Mining:
1. Financial analysis: The financial system relies on top-quality, accurate data. In their loan departments, the data related to finances and users can be used for multiple purposes, such as calculating credit ratings.
2. Market Analysis: This provides a wide range of data to help you plan your overall marketing strategy. The data relating to the market size can enable you to decide whether the market is good for investing or not; you also need to know how the market works.
3. Fraud detection: Traditional mechanisms used to detect frauds & related activities were time-consuming. After the introduction of data mining, fraud detection has become easier. Data Mining has made it more accessible to identify the patterns and help to take steps to ensure the privacy of users’ information.
4. Higher Education: With the increase in the requirement for higher studies worldwide, institutions are now looking for solutions to cater to the increasing demand. Various institutions use data mining to analyze which students would enroll in a specific program and who would need more assistance.
Key Differences between Data science & Data Mining
| Data Mining | Data Science |
| DM is the process of extracting useful information, patterns, and trends from massive databases. | DS refers to the process of obtaining valuable insights from structured and unstructured data by using various tools and methods. |
| DM is a technique. | DS is a broad field. |
| DM is Primarily used for business purposes. | DS is primarily used for scientific purposes. |
| DM is involved with the process. | DS emphasizes the science of the data. |
| DM aims to make data more essential and usable; it means extracting only helpful information. | The objective of DS is to create a dominant data product. |
| DM is a technique that is a part of KDD (Knowledge discovery in database process). | DS is related to the field of study like Mechanical engineering, Cloud architecture, etc. |
| DM primarily deals with structured data. | DS deals with any kind of data, structured, semi-structured, or unstructured. |
Conclusion!
In handling the exponentially increasing amount of data, data science and data mining play crucial roles in helping businesses identify opportunities and make effective decisions. The goal of both these fields remains deriving insights from data that can help a business grow. Major differences lie in the technological tools used, the nature of work, and the steps to perform respective responsibilities to attain that objective. Both fields have tremendous potential in the future, which doesn’t seem to fade away in the foreseeable future.
