Depending on your choice of method different types of data is collected in your degree project. It can, for example, be execution time of an algorithm, answers from a questionnaire about how useful a tool is or a recording of an interview about security policies in an organization. The collected data must be presented in the report so readers can easily understand it, and analyzed so conclusions can be drawn. How to analyze and present data depends on the type of data you have.

For numerical data, you typically present both the collected data (raw data) and the results from any calculations you do on the data. Tables are very useful for presenting this type of data. If a table takes up too much space, you can put it in an appendix and only present a short summary in the main text.

Example: Table 1 below shows how data from the experiment where five sorting algorithms have been compared can be presented.

Run	Bubble	Quick	Selection	Insertion	Merge
1	17384	24	3258	3	30
2	17559	21	3386	3	27
3	17795	19	3344	4	28
4	17484	20	3417	3	28
5	17642	19	3358	3	30
Average	17572.8	20.6	3352.6	3.2	28.6

Table 1: Execution times for the five sorting algorithms on 100 000 random numbers between 0 and 10 000.

Tables can also be supplemented with graphs. It is important that a graph adds something to the understanding of your data, otherwise, it is not needed. A graph can, for example, be used to better visualize relative size differences between values, which can be difficult when only reading numbers in a table. Figure 1 shows the execution times of the five sorting algorithms in a graph. By looking at the graph the reader can get a quick overview of the relative speed of the algorithms.

Figure 1: Execution times for the five sorting algorithms shown as a graph.