Data Mining vs Data Science

What is Data Mining?

Data mining is extracting crucial information from a large set of datasets using a mathematical algorithm and transforming it into an understandable structure for future use. With the extraction of useful data, data mining finds the hidden pattern with the raw data, and transforms, cleans, and integrates data. It analyzes and correlates data from the raw data, which often helps to identify future events. It is an important step in the process of knowledge discovery. Data mining requires powerful computational technologies. 

It can be applied in various fields for data analysis to find the results. Retail and financial industries use data mining to analyze data and identify a pattern that helps increase the customer base and foretell stock market variation. Furthermore, it is used by artificial intelligence to build machine learning models. Data mining experts build algorithms to discover the data structure. To succeed in data mining, you should have an eye for identifying the pattern and hand-on coding. 

What is Data Science?

Data science is the field of study that deals with a massive amount of data; the data studied helps to find innovative solutions to difficult problems. It extracts useful information from structured and unstructured data by combining modern tools, complex algorithms, and machine-learning concepts. It is getting insights, capturing, analyzing, and utilizing the data. The information extracted helps businesses in making decisions.

Data scientists collect information from various sources, which are present in different formats, then analyze and communicate the findings, which affects business by making effective data-driven decisions; it combines the business with computer science and statistics. A data scientist is considered a blend of a data analyst, to some extent, an AI researcher, a deep learning engineer, and a machine learning engineer. They might do the role of a data engineer as well.

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Data Mining vs Data Science- Comparison in a broader way

What is it?

• Data Mining- Data mining is mainly about finding hidden patterns through useful information from the existing database. It is a technique that deals with analyzing data to reach conclusions. It is for businesses that need specific information from the data, involves statistical modeling, and deals with only structured information that can give historical reports which can be compared to current data.
• Data Science- It is a diverse field that contains the whole process of taking insights, capturing, analyzing, and obtaining valuable information. It is the science of getting information from a large database. Data scientist deals with every type of data, whether it is structured, semi-structured, or unstructured. It covers broad techniques, applications, and fields. Data captured can also be used in scientific studies.

Focus

• Data Mining- Data mining mostly helps businesses and industries to have insights and gain valuable information, and discover the hidden patterns which will help them in future planning, learning about the key problems with the customer database, which will help them to grow their organization and business by taking the correct decision with the help of data. It is knowledge discovery through the database.
• Data Science- Data science is a combination of AI, deep learning, and machine learning. Performs multiple operations, including analyzing and creating structure from the data gained. Able to forecast future events by the extracted data. The data captures are also used for scientific study and purposes and various other operations.

Goal

• Data Mining- The goal of data mining specialists is to make data more essential and usable by extracting only important information from the massive database and transforming it into organized information. The specific information needed by businesses is gained through data mining and also discovering meaningful patterns and structures.
• Data Science- The goal of a data scientist is to build data-centric products for an organization. It explores, sorts, and analyzes data to reach a conclusion which helps in the process of decision making. It also helps scientists in research to develop the technology. They create predictive models. 

Output

• Data Mining- Data mining algorithms process the data to produce output in the form of patterns or rules. Then, such patterns and principles derive fresh or practical knowledge or information.
• Data Science- Data science uses analytics results to find solutions to issues. According to data scientists, using data to investigate something is just analysis. Data science advances analysis to better understand and address issues. Data analytics and data science differ from one another in terms of timeline.

Purpose

• Data Mining- Data science employs the findings from analytics to resolve problems. Data scientists claim that using data to research something is a simple analysis. Data science improves analysis to more fully comprehend and resolve problems. Data science and data analytics have different timelines from one another.
• Data Science- In a word, the answer is straightforward: The goal of data science is to identify patterns. Knowing patterns helps you comprehend the world. Finding a pattern is the initial step towards development in everything, from a mechanic car repair to a scientist producing a scientific discovery.

Vocational Perspective

• Data Mining- Currently, administrators in the field of education are under great strain because of the fast rise in higher vocational student enrollment. Student management, curriculum design, and teaching evaluation contribute to this strain. To address these issues scientifically, a vast amount of data in the field of vocational education needs to be discovered and investigated. The use of data mining technology is ideal.
• Data Science- Such platforms, technologies, and tools as Linux, Python, R, Java, SQL, Tableau, TensorFlow, Apache Spark, Hadoop, Docker, etc., should be taught to the students. To craft thought-provoking inquiries and comprehend events and problems from various angles, they must develop their critical thinking skills.

Extent

• Data Mining- Research and science are just two examples of the many domains where data mining is used. With the use of data mining, organizations may use resources more effectively by learning more about their consumers and creating more successful plans for various company tasks.
• Data Science- With many tools, algorithms, and machine learning concepts, data science aims to extract hidden patterns from unorganized data.

Deals with (the type of data)

• Data Mining- Data mining is the procedure of examination and analysis of huge chunks of data to discover significant patterns and trends. Several applications exist, including database marketing, fraud detection, spam email screening, and even user sentiment analysis.
• Data Science- The area of study, data science, operates with a number of amounts of data using cutting-edge tools and methods to uncover hidden patterns, glean valuable information, and make business decisions. Data science makes quite predictive models using sophisticated machine learning algorithms.

Other less popular names

• Data Mining- There are a lot of data mining tools like Orange, Knime, Sisense, and Apache Mahout. 
• Data Science- There are a lot of Data Science, D3, BigML, and Tableau.

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Applications of Data Science

1.Fraud and risk detection

Data science helps in fraud and risk detection by analyzing the data and finding useful patterns and correlations from large datasets. It also identifies the fraudulent activity and risk before it takes place. It helps the banking services, tax department, and different industries to help them identify the unusual pattern using data science techniques.

2.Targeted advertising

It helps businesses to find the right people and the right instant to promote or advertise. Data scientist helps to collect customer databases by analyzing digital marketing algorithms and checking purchase; it allows the marketer to promote the product to the right person. It saves time and money for the advertiser as they find a targeted audience that is interested in the message they are delivering. 

3.Speech recognition

Speech recognition is accomplished through data science; processes and algorithms are implemented from structured or unstructured data. With the help of machine learning, big data analytics, and deep learning, speech recognition is achieved. Speech is converted into text format like in virtual assistant.

4.Healthcare

Many datasets are present in the healthcare system that need to be managed and analyzed, which is done with the help of data science tools. It helps the doctor with information gained from big data, which includes patients' medical records and all other data in health care also helps in finding defects in the human body through image recognition.

5.Website recommendations

It improves personalized user experience by recommending to users similar websites for the product or the user of the resource searching for; it also uses our past data to analyze and give you the recommendation. E-commerce website uses data science to help customers with the product they are searching for.

6.Advanced image recognition

Data science helps in advanced image recognition; you can upload an image to search for the source of the image or if you want to find information regarding the image. Also, if we upload an image on social media with the help of image recognition, it recognizes the faces in the picture and suggests you tag the person.

7.Internet search

It is used in SEO (Search Engine Optimization). The search engine uses the data science algorithm to give the required search results. It checks which website is visited often and which website includes the keyword you are searching for. Most visited links come at the top because of the algorithm. 

8.Airline route planning

Data science is helping in the airline sector with planning about the handling and fueling of flights, knowing passenger demands and occupancy, the routes, and also letting you know if the flight is going to be delayed. It uses AI with a built-in machine learning algorithm to take insights and analyze flight data.

Applications of data mining

1. Market analysis

A lot of different organizations, including the Banking sector make use of data mining to have a better understanding of market risks. It finds its utility when it comes to credit ratings as well as intelligent anti-fraud systems which are responsible for the analysis of transactions, card transactions, purchasing patterns, and many other financial data relating to the customer.

2. Financial analysis

Data mining techniques are often used for the extraction of subliminal patterns and prediction of future trends as well as behaviors when it comes to financial markets. The financial analysis of data is an important thing to take into consideration for businesses as it will provide them an insight into the ways to make a stable and profitable investment.

3. Higher education

When it comes to educational data mining, it can be utilized for the prediction of the performance of a student, dropouts, as well as professors. It helps schools and universities to track the academic progress of students and help them do better after analyzing their performance.

4. Fraud detection

If we understand the fundamentals of data mining, it is all about understanding and analyzing patterns, outlining anomalies, and understanding outliers. When it comes to Fraud detection analytics, relies on machine learning to reveal patterns and send the information to algorithms. This, in turn, makes the process of detection easy and prevents the happening of fraudulent actions in the future.  

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Conclusion

The huge amount of data around us needs to be handled to help businesses with opportunities and future growth by identifying the main problems. Data mining turns raw data into useful information, while data science is a diverse field that includes capturing, storing, analyzing, and obtaining valuable insights from the database. Both have the same objective of handling existing data, which helps different organizations grow but may differ in using tools, technology, and responsibilities.

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