What are the important Data Analysis Techniques?

The data analysis techniques require data in a proper way, data understanding, and preprocessing helps to make data in the correct format.

The input data used by the modeling technique to build a model and the output data generates from the analysis.

You can examine several types of Data Modeling methods for various problems to reach at right one, who gives the correct result.

The Important categories of Data Analysis Techniques that commonly use are classification, regression, clustering, association analysis, and graph analysis.

Types of Data analysis Techniques

Statistical Analysis

Statistical techniques for data analysis help in finding the key insights from data like Summarize the data using mean, median, mode, rage, etc.

Calculating the measures of spread using Standard deviation, variance, Quartiles, etc. to get the changing patterns from data.

Statistical techniques like ANOVA for analyzing the groups of data, Z-test, and t-test for comparing two groups.

The variety of statistical methods used to inference the population and sample data, which test an experiment’s hypothesis.

It needs numerical data or it mostly applies to quantitative variables for different types of Data Modeling in quantitative research.

Regression Analysis

Your model predicts a numeric value, not a category output that task becomes a regression problem.

Regression analysis used MSE or RSS or RMSE etc. to find the best fit line in the model.

The stock price carries a numeric value that represents a regression problem, not a classification.

One of the best examples of regression is to predict the price of a stock over time.

The examples of the regression model for house prices prediction based on the area, location, size of a house, etc.

Classification Analysis

Classification analysis uses to classify categorical data the which is the goal to predict the category of the input data.

In the classification process, multiple classes can predict like good, neutral, and bad, etc. for the product review data.

An example of the Classification method is to classify credit card defaulter or not for credits data.

The classification belongs to binary classes as they are only two categories or you can have multiple categories as well.

For multiple category data, you can use different types of algorithms for a variety of classes in the classification process.

Clustering Analysis

The goal of clustering analysis is to find homogeneous subgroups among the data and you can use different types of data in cluster analysis.

The clustering analysis technique benefits obtaining the fundamental patterns from the unlabeled and unexplained data.

It introduces different techniques to discover comparable types of data points in a cluster from the whole data set.

Analysis carries the two best-known clustering algorithms called K-means clustering and hierarchical clustering.

Grouping customer data of company base into distinct segments for more effective targeted marketing like seniors, adults, and teenagers, etc.

Graph Analysis

The common connection within two product data converted into a graph representation with nodes and links.

The relationship between different data points uses graph structure this kind of data uses graph analytics to analyze your data.

Data with various codependency have a lot of entities and connections between those entities like social networks.

Graph analytics useful for exploring the spread of a disease or epidemic by analyzing hospital’s and doctor’s records.

Predictive Model Performance analysis

Evaluating the model depends on the type of analysis technique you used. how you can evaluate each model’s performance.

Classification and regression modeling gives the correct output for each sample in your input data.

Model Evaluation helps to find accurate results and we can compare the correct output predicted by the analysis algorithm.

The groups created from cluster modeling require to examined to see if they make sense for your application.

Association analysis and graph analysis require proper investigation to see if the results are correct.

Conclusion

The data analysis process used to select the right technique for the problem to build models and evaluating the results.

There are various types of business problems that use different types of analysis techniques for the best results.

We need to know about these techniques to make sure we apply the right technique to our dataset and problem.

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