In the World 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. Data scientists are the most in-demand job title in the market and the trend is set to continue for next couple of decades.
Data mining is the act of identifying patterns in data you have. Many people wonder how it can make them money. One possible way is by using it to sell a product. There are various techniques of data mining. What data mining technique to use depends on what problem you are trying to solve. There are plenty of data available but very little knowledge. The biggest obstacle is to analyze the data to extract meaningful information that can be used to solve a problem or for the growth of the business.
Some of the most common data mining techniques are:
Classification Analysis
This is a form of Classification Analysis. It is used to classify data into different classes. Through the help of advanced algorithm, raw data can be classified into pre-defined classes and segregated data can then be further analyzed for enhanced results. Machine learning makes optimum use of classification analysis and you can train a machine to segregate data based on the conditions or algorithms you feed the machines with.
Regression Analysis
Often used 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 so, to what degree? think of an example like the product price and amount of sales realised.
Association Rule
Association rule is a technique that you can use to find some interesting relation between variables in very large datasets. This rule can help you in mining hidden patterns in the data that are not otherwise visible. The Retail industry uses association rules the most. Association rule helps in catalogue design, shipping basket data analysis(no surprise here) and product clustering. IT professionals use association rule in building the software capable of self-learning.
