DIFFERENT DATA ANALYSIS METHODS SERIES

The applications of neural network in data mining are very broad. It is also known as the вЂњNetwork of Artificial neuronsвЂќ. Neural networks are a set of algorithms, which are designed to mimic the human brain. The branches which do not divide any more are known as leaves. Decision trees are a top-down approach type, with the first decision node at the top, based on the answer at first decision node it will be divided into branches, and it will continue until the tree arrives at a final decision. Decision TreesДecision tree analysis is a graphical representation, similar to a tree-like structure in which the problems in decision making can be seen in the form of a flow chart, each with branches for alternative answers.

Methods based on Machine Learning and Artificial Intelligence 1. If we see scientifically, most of the measurements are executed over time.

DIFFERENT DATA ANALYSIS METHODS SERIES

Now, let us understand what is time-series data? Time series data is data in a series of particular time intervals or periods. Time series analysis is a data analysis technique, that deals with the time-series data or trend analysis. Factor analysis is also related to Principal Component Analysis(PCA) but both of them are not identical we can call PCA as the more basic version of exploratory factor analysis 5. It is the first step towards clustering and classification procedures. It helps with finding independent variables in the data set that describes the patterns and models of relationships. And some of the common measures of dispersion are variance, standard deviation, and interquartile range.ДЇactor analysis is a kind of data analysis technique, which helps in finding the underlying structure in a set of variables. If the difference between the value and average is very low, then we can say that dispersion is less in this case. In the mathematical approach, the dispersion can be defined in two ways, fundamentally the difference of values among themselves and secondly the difference between the average value. Dispersion AnalysisДispersion is the extent to which a distribution is stretched or squeezed. A few of them are Linear regression, logistic regression, and multiple regression. There are different types of regression models in usage. In data mining, this technique is used to predict the values of a variable, in that particular dataset. For example, consider the graph plotted below to have a clear understanding.

To see if there is any relationship between the variables or not we first need to plot the data on a chart and it will be evident if there is any relation. In this kind of technique, we can see the relationship between two or more variables of interest and at the core, they all study the influence of one or more independent variables on the dependent variable.

Regression analysis is one of the dominant data analysis techniques that is being used in the industry right now. Descriptive analysis of a huge data set can be made easy by breaking down it into two categories, they are descriptive analysis for each individual variable and descriptive analysis for combinations of variables. It provides us with an idea of the distribution of data, helps detect outliers, and enables us to identify associations among variables, thus preparing the data for conductingВ further statistical analysis. Descriptive AnalysisДescriptive analysis is an important first step for conducting statistical analysis. Mathematical and Statistical Approaches 1.

Methods based on artificial intelligence and machine learning.

Methods based on Mathematical and Statistical Approaches.

Hadoop, Data Science, Statistics & others

Different data analysis methods