DATA INTERPRETATION

Data interpretation is the process of reviewing data and arriving at relevant conclusions using various analytical research methods. Data analysis assists researchers in categorizing, manipulating data, and summarizing data to answer critical questions.The kinds of insights you get from your data depends on the type of analysis you perform. In data analytics and data science, there are four main types of data analysis: Descriptive, diagnostic, predictive, and prescriptive. In this post, we'll explain each of the four and consider why they're useful.

There are four steps to data interpretation: 1) assemble the information you'll need, 2) develop findings, 3) develop conclusions, and 4) develop recommendations. The following sections describe each step. The sections on findings, conclusions, and recommendations suggest questions you should answer at each step.Data interpretation is the process of reviewing provided data and using these data for calculating the required value. The data can be provided in various forms like in table format, pie chart, line graph, bar graph, or a combination of these.In this part, we will look at the two main methods of interpretation of data: qualitative and quantitative analysis

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