Data sorting:

Data sorting is any procedure that organizes data into a meaningful order to make it easier to comprehend, analyze, and visualize. Sorting is a typical strategy for presenting research data in a manner that facilitates comprehension of the story being told by the data.

It is essential that the data you use to analyze findings and draw conclusions be 'clean.' This means that it is accurate, complete, and consistent.

Data cleaning:

Data cleansing is the process of correcting or deleting incorrect, corrupted, improperly formatted, duplicate, or insufficient data from a dataset. When several data sources are combined, there are numerous chances for data duplication and mislabeling. Incorrect data renders outcomes and algorithms untrustworthy, despite their apparent accuracy. There is no single, definitive method for prescribing the precise steps of the data cleansing procedure, as the methods differ from dataset to dataset. However, it is essential to build a template for your data cleansing process so that you can be certain you are always performing the steps correctly.

Why are they Important:

You should be on the lookout for any irregularities within the numerical data or the demographic information associated with it. For instance, a student number may be missing, a cell may lack information, or a student's grade level may be wrong. Ensuring that data is clean before working with it can assist prevent misinterpretations and the need to redo the procedure if flaws are discovered later.

Each data entry must be consistent with the others. When obtaining information from multiple sources, it must be in the exact same format. Before combining data (such as records from many classes), ensure that the format is identical.

Progress data should always be matched to ensure that the same students are reflected in both sets of figures.

The information must be precise. Examine all data for abnormalities; are you certain that you have the correct student records and test results? If data is entered manually, the accuracy of the data entry should be double-checked.

Each data record must be comprehensive. Ensure that there are no student records with no results associated with them. Although it is essential to determine why some students do not have assessment results, their records should be removed for the purposes of rapid analysis. Empty records can bias data when computing medians and means, thus this is very critical.

Now its your turn to express to your feedback why you think they are Important