For example, regression might be used to predict the price of a product, when taking into consideration other variables. Regression analysis allows modeling the relationship between a dependent variable and one or more independent variables. In data mining, this technique is used to predict the values, given a particular dataset. It allows us to learn from past behaviors, and find out how they might influence future performance. Some methods and techniques are well known and very effective.ĭescriptive analysis is an insight into the past. This statistical technique does exactly what the name suggests -“Describe”. It looks at data and analyzes past events and situations for getting an idea of how to approach the future.ĭescriptive analytics looks at past/historical performance to understand the reasons behind past failure or success.

In fact, most data mining techniques are statistical data analysis tools. Mathematical and statistical sciences have much to give to data mining management and analysis. Mathematical and Statistical Methods for Data Analysis Here we will see a list of the most known classic and modern types of data analysis methods and models.

Visualization and graphical method and tools.

Methods based on artificial intelligence, machine learning.

Mathematical and statistical techniques.

It uses the methodologies and techniques of other related areas of science.Īmong the methods used in small and big data analysis are: In fact, data mining does not have its own methods of data analysis.

No doubt, that it requires adequate and effective different types of data analysis methods, techniques, and tools that can respond to constantly increasing business research needs. Our modern information age leads to dynamic and extremely high growth of the data mining world.

Data analysis tools for research