The continuous progress in Information technology and electronic commerce has multiplied the amount of data produced and stored by organisations. These data are now helping organisations to grow their business. Nowadays, business intelligence of an organisation is highly backed by the data preserved by that organisation. Data Mining is the process enabling this aspect. We will discuss more regarding the Data Mining in this article.
What is Data Mining?
Data mining is the process of analyzing the data in order to find hidden patterns and systematic relationships. These relationships and patterns are then used to predict the future behaviors. Data mining finds insights from the huge amount of structured and unstructured data that help businesses make more fact-based decisions. Even though the term data mining is a relatively new term, the practice itself is not. Companies have been using data mining techniques such as supermarket scanners that track customer purchases. With the advent of Big Data and advancement in computer technology, Data Mining grew more prevalent and feasible. Various techniques and tools are used to mine data. Each tool consists of its own peculiarities and merits. The tools are selected according to the requirement.
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Data Mining Tools
For quicker analysis of data, it is important to use the proper tool for the requirement. Following are the few essential tools used for Data Mining
Rapid Miner – It a very popular open source software that requires no programming to operate. It provides multifaceted data functions such as data pre-processing, predictive analysis and visualization.
Weka – It is a set of machine learning algorithms for data mining. They are either applied directly or called through a Java code. This tool is used to perform data pr-processing, clustering, regression, visualization and classification in data mining.
Orange – It is a Python library that powers Python scripts with Machine Learning and Data Mining algorithms. It is used for classifying, pre-processing, modelling, clustering and other miscellaneous functions.
R – It is an opensource software environment widely used for data mining tasks. It comes with huge community support as well as hundreds of libraries specifically built for data mining.
Knime – This tool is primarily used for data pre-processing in data mining. Data pre-processing means extraction, transformation and loading of data. Knime is very popular among the financial data analysts.
Data Mining Vs Data Science
Data Mining and Data science may go hand in hand when it comes to data. But basically, they are two completely different things. Data science is a field of study which constitutes everything from Data Mining, Data Visualization, mathematics and Big data Analysis. It is now considered as the fourth paradigm of science after Theoretical, Empirical and computational science. On the other hand, Data Mining is a technique that finds trends in a data set. Clearly, Data mining is a subset of data science with many applications in the current data-driven world.
The Career as a Data Mining Analyst
Data Mining Analysts plays a large role in realizing the business intelligence of organisations. They provide actionable ideas by analyzing the data. So, Data Mining Analyst is an important person in every industry. The average annual salary for a Data Mining Analyst in India is above â‚18 Lakhs. With more and more organisations trying to make use of data, the demand for qualified Data Mining Analyst is expected to go higher.
A smart step towards a successful career in Data Mining is to gain in-depth knowledge not just in the mining tools but also in the area of statistical methods and predictive models that support business needs. Visit Acadgild for great courses on Data Mining.