What is Data Mining? 

What is Data Mining? 

by Ragini Salampure

Companies are collecting data at unprecedented volumes and speeds that have a multitude of formats. Data-driven decision-making is no more a value add-on; it has become necessary to stay competitive in this dynamic workplace. Uncovering insights from big data is helping businesses achieve a lot of benefits like improving processes, optimizing resources, driving better actions across teams, and ultimately designing high-end products for customers. This process of extrapolating trends and patterns from the data a company collects is called data mining. 

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Basically, data mining is the practice of searching huge data sets automatically so as to discover correlations that go beyond simple analysis. It is a branch of data science that assists companies in mitigating risks, solving various problems, and exploring new opportunities. People use data mining to answer questions that have, lately, been too time-consuming to solve manually. Through a range of statistical techniques for analyzing data, data mining is being used to predict what event is likely to occur in the future so that business leaders can take action accordingly and influence business outcomes. 

If you are seeking a career in data science, then knowledge of data mining is crucial. Even when you take any data science course, Post Graduate Program or PGP in Data Science, you will learn about data mining. Read on to know more about this interesting topic.

How Data Mining Works?

When you plan to start the data mining process, the first step you will need to perform is data collection. Such data can be records, logs, social media data, application data, website visitor data, sales data, and so on. If you have dealt with data at any point in your career, you must be aware that data that is collected from different sources isn’t ready for analysis. So, data cleaning is performed to ensure that redundant data like duplicate values, missing values, or corrupt data is removed and the data is transformed into a usable format.    

The team that is performing data mining should clearly understand the objectives and scope of the project. The business stakeholders will likely have a problem or a question that can be solved through data mining. Next, one needs to prepare data sets that are relevant to the problem at hand. Some exploratory analysis can be performed to identify some initial trends and patterns. Post this, the data preparation phase begins, which means the team prepares the final data set and stakeholders will identify the variables and dimensions to explore and prepare the final set for modeling. 

Data modeling is the next phase, and appropriate modeling techniques are used for the given data set. The commonly followed techniques are classification, predictive models, clustering, destination, or a combination. When the models are created, they need to be tested, and their success needs to be measured against answering the question identified in the beginning. If the model doesn’t align with the set business goals, it needs to be edited and improved. Once the model achieves the desired accuracy, it becomes ready for the final phase, i.e., deployment. It can be deployed within the organization, with the stakeholders, or shared with the customers to check its reliability.

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Data Mining – What Techniques are Used?

Data mining involves multiple techniques that help to answer business questions or solve a given problem. Discussed below are some of those techniques. 

Data mining involves a basic technique referred to as identifying trends or patterns in the given data set. You may monitor the value of a certain variable over time and notice what happens at different intervals. For example, eCommerce websites can monitor their sales over time and identify a trend that it increases whenever there is a festive season. Similar to this technique, there is another data mining technique known as association. Here, people look for dependently linked variables, i.e., specific attributes that are highly correlated with another parameter. A simple example of an association rule is the ‘Frequently Bought Together’ section of an eCommerce application. 

Next, you will come across neural networks that work as a technique for data mining. Basically used for deep learning, neural networks process training data by simulating the interconnectivity of the human brain through various layers of nodes. Such networks can perform analytical activities like planning, reasoning, learning, and problem-solving. 

Have you heard of classification or regression? Decision trees use these methods to classify or estimate potential outcomes based on a set of decisions. Used as a data mining technique, decision trees use tree-like visualizations to present the potential outcomes of these decisions. Similar to classification is the clustering technique. It refers to the method of grouping chunks of data together based on their similarities. In other words, clustering is a non-parametric algorithm that classifies data points based on their association and proximity to other available data. 

Now that you know what data mining is all about don’t pause your learning. Data science is a multidisciplinary field, and learning about some of its aspects can really improve your career prospects. You can either do self-study or take up an online data science course to gain the necessary skills. The world is wide open for professionals seeking a data-related position, and their demand doesn’t seem to decrease in the future as well.  

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