A report on data analysis can help interpret the raw data gathered during a research project or business project. It is a thorough examination of how that information relates to and supports a hypothesis. It is also designed to assist in making decisions and provide conclusions.
Data analysis encompasses two broad types of approaches – descriptive analytics and inferential analysis. Descriptive analytics concentrate on what has happened in the span of time. For instance the number of views or sales of a particular product. Diagnostic analytics, however, focuses on the reasons for why something happened. This usually involves a variety of data inputs, and some theories (e.g. how did the weather affect sales of beer?).
Before you begin data analysis, you need to tidy up the raw data or “scrub” it. This means removing duplicate observations, and ensuring all observations are complete and accurate. This may also involve standardizing formats and identifying possible errors.
The next step is converting the data into a graphical format that is easy to understand. This can be done by using tools for data mining or visualization tools. In this case it is crucial to think about the audience. You might have to create a glossary of terms, or explain your approach in case your readers aren’t familiar with the terms.
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