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Introduction to Data Mining in Data Science
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swethakrishn017
Introduction to Data Mining in Data Science
Introduction
Data has grown in importance over the years and will continue to grow in importance in the coming years. In today's digital world, almost every task that was previously done manually is now done digitally. Nowadays, data is considered more valuable than oil. For example, sales records were manually recorded in a notebook in the grocery store records. It was recorded and then rendered useless. It was impossible to apply practical data science algorithms to that manually written data to gain valuable insights and future predictions. However, not all sales records are digitally stored using appropriate database software.
What is Data Mining?
Data mining is a critical component of data science, if not the essential part because it aids in discovering relationships between data items in a given data set. When we discuss the meaning of the word "mining," we learn that it means "extracting." Data mining is the process of extracting valuable relations and practical insights from a given dataset. Data mining is the process of managing massive datasets and extracting some beneficial relationships between the data set attributes and different types of helpful information by applying other data science and machine learning algorithms which can be learnt in the data science courseand the artificial intelligence course.
How Does Data Mining Help in Data Science?
Data mining is a prominent and essential component of data science. A data mining process is required for data science problems and research; its algorithms primarily find association rules mining and many other data mining algorithms. Every day, a large amount of data is generated. When it comes to social media data, it is produced in terabytes in a very short period of time. Similarly, health data is made in terabytes for a single person in a concise period. Data is being generated in massive quantities all over the place. It is critical to use this information for functional purposes.
Data science is a broad field that includes statistics, mathematics, particularly calculus, machine learning, artificial intelligence, and data mining. These subfields of data science are required to use this data and reap some benefits from it. Data mining is a subset of data science that provides some valuable algorithms and methods for dealing with large amounts of data, determining essential and valuable relationships between different attributes of the data, and gaining useful insights from the data.
If you want to use data mining on health data, the benefits of using data mining on health data will almost be for the entire community. If data from diabetes patients is collected, we must predict which symptoms of the diabetes data are directly related to symptoms of high blood pressure using data mining association rule mining algorithms. You can help patients and the community by extracting different types of results from these associations later on to determine the relationship between the various symptoms of the diseases.
Furthermore, if we consider the student data set, different data mining and machine learning algorithms can be used to extract various types of information and associations from the student data. For example, we have a student data set that contains records of student performance. We can deduce the relationship between various subject marks. This way, we will learn about the student's abilities and interests in specific fields. If a student receives high marks in all computer science subjects, it is likely that the student has a strong interest in the field of computer science and information technology. Similarly, we can forecast students' performance in the upcoming semester or year.
Similarly, we can forecast students' performance in the coming semester or year. So, data mining algorithms and some machine learning algorithms can assist us in extracting various types of relationships in records and predicting multiple vital aspects of a given dataset.
Summary
Preprocessing steps are also required by all machine learning algorithms when mining data. Data mining is critical in data science because it aids in the discovery of various relationships between data sets. We discussed the significance of data. Data mining uses various real-life examples and how data science, specifically data mining, can help us in real life.
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