At first, when we get the data, rather than applying extravagant algorithms and making a few predictions, we first attempt to comprehend the data by applying statistical methods. By doing this, we can comprehend what kind of data we have. To analyze data, we have a different kind of statistical analysis:

  • Descriptive analysis
  • Diagnostic analysis
  • Prescriptive analysis

But in this blog, we will only cover the following questions:

  1. What is a descriptive analysis?
  2. What are the means to perform the descriptive analysis?

So without any further ado, let’s jump to our first section:

What is a descriptive analysis?

A descriptive analysis is a significant initial step for leading statistical analysis. It gives you a thought of the appropriation of your data, causes you to distinguish exceptions and errors, and empowers you to recognize the relationship among variables, preparing you to lead further statistical analysis.

In any case, with the accessibility of such huge numbers of kinds of summary and graphical approaches, professionals get befuddled on how to deal with the analysis of their data. They either wind up the leading scope of analysis, in turn burning through their time or totally avoid this pivotal step of statistical analysis, consequently expanding their odds of settling on mistaken choices.

In any case, descriptive analysis are neither troublesome nor tedious, whenever done methodically. It is simpler to consider descriptive analysis on the off chance that you partition them into two kinds:

  • Descriptive analysis for every individual variable
  • Descriptive analysis for combinations of variables

The best methodology for directing descriptive analysis is to initially choose the kinds of variables and afterward utilize approaches for descriptive analysis dependent on variable types.

Extensively, variables can be arranged into quantitative and qualitative. Quantitative variables speak to amounts or numerical qualities (for example age, weight, volume, distance, and so on.) while qualitative variables portray quality or attributes of people (for example ethnicity, complexion, gender, nationality, and so on.). Both of these variable types have further sub-classifications yet the wide classification is adequate for choosing approaches for descriptive analysis.

How to perform Descriptive Data Analysis

Moving forward, let’s take a look at how descriptive analysis is performed. Descriptive strategies frequently incorporate developing tables of means, standard deviation, variance, and "crosstabs" or cross-tabulations that can be utilized to look at numerous disparate hypotheses. The differences observed across subgroups are mentioned in these disparate hypotheses. Specialized descriptive strategies are utilized to quantify discrimination, segregation, and disparity. Segregation is regularly estimated with the help of review studies or audit techniques. Advanced segregation by type or imbalance of results need not be entirely positive or negative in itself, however, it is frequently viewed as a marker of unreasonable social procedures; the exact estimation of the levels across space and time is a basic requirement for properly understanding these processes.

A table of means by subgroup can show significant contrasts across these subgroups, and this sort of descriptive analysis frequently welcomes causal inference. Whenever we see a hole in income, for instance, we normally need to find the discrepancies for this behavior. However, this enters the territory of estimating impacts, and various procedures are required.

A two-way tabulation or crosstab shows the proportions of units with unmistakable qualities for every one of two variables. For instance, we may solicit what extent from the populace has a secondary school degree and gets food or money help, which requires a crosstab of education versus receipt of public assistance. At that point, we may likewise inspect row proportions, or the parts in every education bunch who get help, maybe observing help levels forcefully lower at advanced education levels.

We could likewise see column proportions, for the part of beneficiaries with various degrees of education, however, this is the other way from any causal impacts. We may see a shockingly high number or extent of beneficiaries with an advanced degree, yet this may be a consequence of bigger quantities of school graduates than individuals with not exactly a secondary school degree (the column proportions of the absolute populace regardless of receipt of public assistance).

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Steps to do descriptive analysis:

Step 1: Draw out your objectives

In this step, you need to define why you need to do this analysis, what are your objectives, how you will lead the way, what you have to ignore and in which form do you need your data to be.

Step 2: Collect your data

After defining the objectives, you need to collect data. This is a very crucial step as collecting the wrong data might deviate you from your goal.

Step 3: Clean your data

The next step is to perform data cleaning. Irrelevant information or noise in your working data set can cloud your results. For accurate results, you should clean your data based on the requirements. Data cleaning can prove to be tricky if you’re handling big data. To learn more about big data analysis, take big data training online sessions.

Step 4: Data analysis

This whole data analysis process may seem a single process but now you know what and how much it takes to reach this stage. After cleaning the data, different analysis techniques are applied. The descriptive analysis describes the basic features of the data in the form of detailed descriptive summaries.

Step 5: Interpret the results

Once the analysis is performed on your data set, you can interpret the results based on your objectives. If you’ve achieved the expected results, the analysis was successful otherwise you have to look for the loopholes in your approach and re-follow these steps to achieve better results.

Step 6: Communicating Results

This step might seem really simple and easy, but in actuality, it is not. Communicating the results can be tricky when you’re presenting your analysis to the non-technical stakeholders and teammates. For smoothly winding up this process, data visualization comes to your rescue. You can use different data visualization techniques like charts, pie charts, graphs and etc. to communicate the results.

To conclude

As we have reached the end of our discussion, we hope all your queries about the descriptive analysis are answered.

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