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Data coding is an essential element in research for it to be efficient. Coding allows research teams to classify and arrange qualitative data to identify common themes amongst the collected data(Skjott Linneberg & Korsgaard, 2019). There are usually two data-coding methods: manual and automated coding. Automated coding is done by computer software that utilizes thematic analysis (Skjott Linneberg & Korsgaard, 2019). On the other hand, manual coding requires certain specific steps to complete. The first one is to determine whether to apply deductive or inductive coding. A deductive code begins with a predefined code and is then applied to new qualitative data (Skjott Linneberg & Korsgaard, 2019). The predefined codes usually are obtained from other themes or past research studies. Concurrently, inductive coding is when the codes are new and specifically created for the qualitative data (Skjott Linneberg & Korsgaard, 2019). The following step in the data code entails reading and understanding the data set. This is important as it helps the researcher get a picture of the data before assigning any code. The actual coding then follows this process. Under this, the researcher should be keen on studying the data to avoid errors; thus, also enhancing the code details (Skjott Linneberg & Korsgaard, 2019). The next process is categorization (Skjott Linneberg & Korsgaard, 2019). Under this, figures and codes are classified appropriately to suit the coding frame (Skjott Linneberg & Korsgaard, 2019). The last process is the identification of the most recurrent them and acting on it accordingly (Skjott Linneberg & Korsgaard, 2019).