Introduction to Data Mining

Today, the demand for data analysts and data scientists is so high that the companies are struggling to fill their open positions.

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. So learning about data mining techniques will surely help you in preparation for becoming data analyst or data scientist.

Data mining is a process of getting a useful information from an unorganised raw data.

These techniques is used to
  • predict the future trends
  • mainly to identify the customers and to develop marketing stratergies to increase the sales rate.
The ultimate goal of data mining is prediction – and predictive data mining is the most common type of data mining.
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.

There are various data mining techniques. Each technique helps us find different patterns.

Below is the list of the most common data mining techniques.


  • Collect the data by classifying into different classes based on their attribute.
  • These pre-defined classes will help in segregation of data for furthur analysis to give better results.
  • And the classification analysis is majorly used in machine learning algorithms.


  • In this analysis,you can find relationship between multiple variables.
  • It helps in identifying the amount dependency of the variable on other variables.
  • It will predict how one variable will change if a variable related to it changes.


  • It is used to find the relation between variables in large data set and it will extract the hidden patterns in the data.
  • Major application of this technique is in retail industry.


  • Placing data into groups based on similar values.
  • The grouping is done in such a manner that the objects within the same cluster are very similar to each other but they are very dissimilar to the objects in some other cluster.

Anamoly detection

  • As name suggests,it is used to detect unusual pattern.
  • It has wide applications on dectecting fraud in credit/debit card transactions or dectecting hack in network traffic.

Decision tree

  • Well, decision tree is represented graphically as hierarchical structures so they have a very unique property that they are easy to read and understand.
  • In fact, they are among the few models that are interpretable, where you can understand exactly why the classifier makes a decision.Also, it is able to handle numerical and categorical data.Read more

Neural network

A neural network is just an attempt to make computer models like a brain because if computers were more like the brain they could be good at some of the things humans are good at, like pattern recognition. So a neural network simulate a collection of neurons just as done in the brain and these simulated neurons take inputs and give outputs through their connections.Read more

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