Everyone knows how omnipresent data is in today’s world. And for this reason, we are not going to get started on that topic. Data is exceptionally important for organizations of all sorts, nature, and sizes. Making a career in the field of data can only be made possible with the right training and certification program.
Once you begin learning about data, you will realise that there are a lot of processes, techniques, and tools that are used. As an expert in the field, you will be expected to know about these. Among the myriad of tools, techniques, and processes is data mining.
In this blog, we are going to provide you answers around data mining. We are also going to cover that what is data mining process. And anyone who wants to make a career in this field must also know that what are the different applications of data mining. You are going to find the answer to these and many other questions in this blog.
Without any further ado, let us begin to uncover more about data mining, one answer at a time! Let us begin with what is data mining.
What is Data Mining?
Data comes in large sets. With the process of data mining, the expert can sort through humongous datasets. This helps them identify relationships and patterns for solving business problems via data analysis. By implementing data mining tools and techniques, businesses predict future trends and consequently, make well-informed business decisions.
Are wondering what is the difference between data analytics and data mining? Well, data analytics is an umbrella term that holds data mining within it. However, data mining is an integral aspect of data analytics. Data mining is also a core discipline in data science. With this, you must have understood how important this process is for businesses.
Data mining makes use of advanced analytics techniques that leads to gaining meaningful insights in datasets. This is a computational process that helps discover patterns in gigantic datasets. Various methods are used to conclude this process include statistics, artificial intelligence, database systems, and machine learning.
The goal here is to extract information and then transform it for future use by turning it into an understandable structure.
Data mining has a wide array of alternate names to itself. People from different walks of the tech world refer it through different names including –
- Knowledge extraction
- Business intelligence
- Knowledge discovery in data
- Data dredging
- Pattern/ data analysis
- Information harvesting
- Data archaeology
Why is Data Mining Important?
Any organization that has adopted analytics initiatives knows how important data mining process is. The information derived from data mining is extensively used in real-time analytics applications, advanced analytics applications, and business intelligence, among others.
When data mining is effective, different aspects of managing operations and planning business strategies are made easy. The strategies pertain to all the aspects of an organization. Customer-facing functions like sales, customer support, marketing, and advertising are also benefitted. Additionally, a positive change happens in supply chain management, HR, manufacturing, and finance.
Data mining also helps in cybersecurity planning, risk management, and fraud detection, along with other critical business use cases.
If we are to give a detailed explanation of why businesses use data mining, even the entire blog space would not be enough. As you begin your training and certification course in data science by Grras Solutions, you will learn everything necessary.
What is Data Mining Process?
It is typically a data scientist, a skilled data analyst or business intelligence professional who carries out the data mining process. However, data-savvy workers, executive, and business analysts are also capable of doing it, in certain conditions.
Data is prepared for analysis by combining a few core elements. These include statistical analysis, machine learning, and data management tasks, among others. With artificial intelligence (AI) and machine learning algorithm tools, the process has become more automated. Thus, mining bigger massive data sets such as log files, customer databases, and transaction records from mobile apps, sensors, and webs servers become easier.
What are the Stages of the Data Mining Process?
While the data mining process is lengthy, it can be categorized into four essential stages. Here is a brief about each of the stage. With the right training and certification course in India, you will be able to get ahead of your competitors in the right way.
- Data Gathering – The first stage is all about identifying and assembling relevant data for related analytics application. The data can be located in a data lake, in multiple source systems, or a data warehouse. It is also possible to use external data sources. Irrespective of where the data is coming from, it is often moved to a data lake by the data scientist to continue the future stages.
- Data Preparation – Data preparation is all about getting the data prepared to get mined through a few essential steps. The first step is data exploration, followed by profiling and pre-processing, leading to data cleansing. Data transformation is done to gain consistent datasets. However, this step can be skipped if the data scientist wishes to analyse unfiltered raw data for any specific application.
- Data Mining – After the data is prepared, it is time for the data scientist to choose an apt data mining technique. This is done by implementing multiple algorithms for mining. When talking about machine learning applications, sample datasets are used to train the algorithms on. Once this is done, then they are run against complete datasets.
- Data Analysis & Interpretation – The derived data mining results are then used for creating analytical models to help decision-making actions, along others. It is the responsibility of the data scientist or a different team member to communicate their findings to the users and the business executives. This is done through data visualization and various data storytelling techniques.
What are the Different Applications of Data Mining?
Data is only useful after being converted into useful information. Today, humongous quantities of data gets collected form various sources. This data is converted into something useful by putting it through multiple tools and techniques. Data mining is one such process.
Data mining offers quite a lot of benefits to companies. There are a lot of different areas where data mining has found its application. Here are some of the different applications of data mining.
Scientific Analysis –
Bulks of data is being generated by scientific simulations. Data is collected from various sources nuclear laboratories, etc. With data mining techniques and tools, it is possible to capture and store more data in a faster manner. Scientific analysis includes –
- Classification of astronomical objects
- Sequence analysis in bioinformatics
- Medical decision support
Business Transactions –
Business transactions are a part of the business industry that certainly does not have an end. these transactions pertain to a lot of things such as intra-business operations, inter-business deals, and even time-related ones. With data mining, these business transactions get analysed, help identify market approaches, and enhances decision-making power. For instance –
- Customer segmentation
- Direct mail targeting
- Churn prediction
- Stock trading
Educational Data Mining or the EDM method is used for analysing the data in the education sector. With this method, patterns are generated that are being used both by the educators and the learners. Some of the educational tasks performed by using data mining EDM are –
- Predict student profiling
- Predict student placement opportunities
- Predict admission rate for higher education
- Teachers teaching performance
- Predict student performance
- Curriculum development
Healthcare & Insurance –
Data mining is a big part of the pharmaceutical sector. It helps in examining new deals force activity, figuring out the best promotional activities, how to improve the focus of high-value physicians, etc. Similarly, in the insurance sector, various data mining techniques are used to determine what sector of customers will bend towards which policy, identify any fraudulent behaviour, and capture risky customer patterns.
Market Basket Analysis –
This is a technique that facilitates the analyst in getting a centralized study of purchases carried out by a customer in a marketplace. Thus, it helps the organization learn about the patterns hidden in the customer’s purchase items, price range, and brand preference. It also helps in offering special deals and enhance company sales.
Data mining techniques have widely been used for classification, grouping, predictions, associations, and clustering with perfection. In majority of technical research in data mining, testing models and training models are created to measure how precise the proposed model is. For instance –
- Web mining
- Classification of uncertain data
- Domain-oriented data mining
- Information-base clustering
- Smart farming IoT
- Decision support system
- IoT and cybersecurity
When we talk about the data mining process, it comes as no doubt that how important it is. With the continuously increasing data around us, processing it at a faster rate has become even more important. And for that purpose, the need for data scientists and data analysts is felt globally.
Get started in this field by enrolling with Grras Solutions’ data science training and certification program. You can also choose to go with our data analytics training and certification course. Begin now and get trained by industry experts for unprecedented success.
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