Data mining is one of the most widely used methods to extract information from large datasets. There are various techniques of data mining. What data mining technique to use depends on what problem you are trying to solve. There are tonnes of data available but very little knowledge. The biggest challenge is to analyse the data to extract meaningful information that can be used to solve a problem or for the growth of the business. There are powerful tools and techniques available to mine data and find insights from it.
Below is the list of the most common data mining techniques
Classification Analysis is used to classify data into different classes. With the help of advanced algorithm, data can be classified into pre-defined classes and segregated data can then be further analysed for better results. Machine learning makes optimum use of classification analysis and you can train a machine to segregate data based on the conditions (algorithms) you feed the machines with.
In Statistics, regression analysis is the process of determining a relationship between multiple variables. It can help you understand the characteristic of the dependent variable if the value of independent variable changes. It also helps you in determining whether the variables are dependent on each other and if yes, to what degree.
Association rule is a technique that can help you in finding some interesting relation between variables in very large datasets. Association rule can help you in extracting hidden patterns in the data that are not otherwise visible. Retails industry uses association rule the most. It helps in catalogue design, shipping basket data analysis and product clustering. IT professionals use association rule in building the software capable of self-learning (machine learning).
Today, the demand for data analysts and data scientists is so high that the companies are struggling to fill their open positions. There will be a shortage of around 200,000 data scientists in the U.S. alone by 2020. A data scientist is the most in-demand job title in the market and as per the trend will continue to remain so for next couple of decades.
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