10 Top Types of Data Analysis Methods and Techniques

Methods based on artificial intelligence, machine learning
Visualization and graphical method and tools

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

1. Descriptive Analysis
Descriptive 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.
Descriptive analytics looks at past/historical performance to understand the reasons behind past failure or success.
It allows us to learn from past behaviors, and find out how they might influence future performance.

2. Regression Analysis
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. For example, regression might be used to predict the price of a product, when taking into consideration other variables.
Regression is one of the most popular types of data analysis methods used in business, data-driven marketing , financial forecasting, etc.
There is a huge range of different types of regression models such as linear regression models , multiple regression, logistic regression, ridge regression, nonlinear regression, life data regression, and many many others.

3. Factor Analysis
Factor analysis is a regression-based data analysis technique, used to find an underlying structure in a set of variables.
It goes with finding new independent factors (variables) that describe the patterns and models of relationships among original dependent variables.
Factor analysis is a very popular tool for researching variable relationships for complex topics such as psychological scales and socioeconomic status.
FA is a basic step towards effective clustering and classification procedures.

4. Dispersion Analysis
Dispersion analysis is not a so common method used in data mining but still has a role there. Dispersion is the spread to which a set of data is stretched. It is a technique of describing how extended a set of data is.
The measure of dispersion helps data scientists to study the variability of the things.
Generally, the dispersion has two matters: first, it represents the variation of the things among themselves, and second, it represents the variation around the average value. If the difference between the value and average is significant, then the dispersion is high. Otherwise, it is low.

5. Discriminant Analysis
Discriminant analysis is one of the most powerful classification techniques in data mining. The discriminant analysis utilizes variable measurements on different groups of items to underline points that distinguish the groups.
These measurements are used to classify new items.
Typical examples of this method uses are: in classifying applications for credit cards into low risk and high-risk categories, classifying customers of new products into different groups, medical studies implicating alcoholics and non-alcoholics, and etc.

6. Time Series Analysis
You know that, in almost every scientific area, measurements are executed over time. These look-outs lead to a collection of organized data known as time series.
A good example of time series is the daily value of a stock market index.
Time series data analysis is the process of modeling and explaining time-dependent series of data points. The goal is to draw all meaningful information (statistics, rules, and patterns) from the shape of data.
Afterward, this information is used for creating and modeling forecasts that are able to predict future evolutions.