Traditional Methods used in Data Science

After when the BI reports and Dashboards are prepared. and the executive has extracted the insights of the business. You use it to predict some future values as accurately as possible. That is why at this stage we stop dealing with the analysis and start applying analytics.

Traditional Methods, in summary, can be said as assess Potential Scenarios by using advanced statistical methods. Techniques are:

(I) Regression:- In business statistic, regression is a model used for quantifying causal relationships among the different variables included in your analysis. It can be of two types: Linear Regression Model and Non-Linear Regression Model.

(II)Clustering:- When the data is divided into a few groups called clusters. You can apply cluster Analysis. This is another technique that will take into account that certain observation exhibits similar outcomes. In this analysis, we can conclude the factors on which the required variable is depending upon.

(III) Factor Analysis:- When the data is of different types as well as different amount of variables are present for calculations then this analysis is used to calculate and optimize the number of factors on which outcome is depending upon.

(IV) Time-Series Analysis:- We use this technique, especially if we are working in economics or finance. In this field, you will have to follow the development of certain values over time such as stock prices or sales volume. We can associate a time series with plotting values against time. Time will always be in a horizontal line.