What is Data Mining?

A number of companies acorss the world claim to be a part of the data-driven world. No wonder data science is contributing to determining the success of any business.  Today, digital technology has helped almost every industry, especially during the onset of the COVID-19 pandemic. The data-driven correlations and insights are helping businesses to adopt the changes quickly and fulfill customer expectations. Moreover, many companies are now relying on data to offer the finest customer service.

If you aren’t familiar with data science, it basically means an interdisciplinary field that emphasizes finding meaningful insights from raw data that can lead to better decision-making. The data science lifecycle consists of various stages like data collection, data cleaning, data processing, data analysis, and data visualization. A number of job roles are available for professionals willing to start a career in data science, including a data scientist, data analyst, machine learning engineer, business intelligence analyst, data engineer, and data architect.

You will have to dive into several concepts to become acquainted with data science. As it is a vast field, many professionals prefer taking a data science Bootcamp to gain job-ready skills. One of the core disciplines in data science that you will come across is data mining. Let us know what data mining is all about and why is it important in the overall data science process.

Data Mining Explained

As Gartner describes, data mining is the process of identifying meaningful correlations, trends, and patterns by sifting through massive amounts of data stored in repositories. It employs pattern recognition technologies, along with statistical and mathematical techniques. Initially, this description may seem quite similar to that of data science. However, data mining can be considered a sub-category of data science. Today, data mining can be performed easily and valuable insights can be extracted faster with the advent of advanced data analytics and visualization tools.

The data mining process involves certain steps which may vary from organization to organization. However, these are the major steps followed by organizations generally – data collection, data preparation, mining data, and data analysis and interpretation. Before collecting the data, setting the business objective is important, as per IBM. It is observed that companies spend quite a little time on this crucial step. As part of setting the business objective, data scientists need to collaborate with business leaders or stakeholders to clearly define the business problem and identify the questions that can be answered through data mining.

The above-mentioned step can simplify the next step, i.e. collection of data. When data scientists clearly know the business problem to be solved, they can identify which data is relevant and needs to be collected. Data is generated through a number of resources, and appropriate data is then collected by data scientists in a data lake for carrying out the next steps. Now, professionals involved in data science must already know that the collected data isn’t ready for analysis directly. Data scientists first perform data exploration, data profiling, and pre-processing before moving on to data cleaning. This step basically ensures that the data is of good quality and is consistent. As part of data cleaning, data pros identify missing values, duplicate values, corrupt values, or any other redundant information and delete them or modify them as they find appropriate. Moreover, not all the data may be present in a single format. So, data is also transformed into a single usable format so as to carry out data analysis without errors.

Now is the time to mine the data. There are various techniques used by data scientists to mine data for different applications. The first kind is descriptive modeling in which trends and outliers are identified in historical data to find answers to business problems. The techniques used in this kind are clustering, association rule learning, anomaly detection, affinity grouping, and principal component analysis.

The next kind is predictive modeling; this is where data is mined to predict future outcomes or estimate the likelihood of any event. Some of the techniques used for this purpose are neural networks, regression, decision trees, and support vector machines. Lastly, you will come across prescriptive modeling – a process that uses machine learning algorithms and statistical models to identify possibilities and recommend certain course of action.

The results obtained through data mining are then used in the data analysis and interpretation phase. Here, data scientists build analytical models that can help make effective business decisions and take the right action. The findings obtained at this stage are then visualized using tools like Tableau, Power BI, or QlikView. Data visualization is conducted to easily communicate the insights to business leaders and stakeholders. Interactive dashboards and visual charts show the trends and correlations clearly that can easily be comprehended by people even without advanced technical knowledge.

Learn Data Mining Today!

Now that you know data mining is at the heart of data science process and applicable across various business verticals, why not learn more about it. Whether you are applying for data scientist, data analyst, or data engineer job roles, the knowledge of data mining is highly valued for these positions. Though independent study is a good option, it isn’t feasible for all working professionals. So, we suggest taking an online data science course to build the foundational skills in data mining. Every data science course explains this topic in detail and make you familiar with all the techniques used in performing data mining. Moreover, it will demonstrate your seriousness for a data-related job and make you stay ahead of the competitors.

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