Nowadays, statistical and data mining tools are used to find patterns and relationships when dealing with a large dataset. One of the essential data extracted from large sets is the outliers. It is categorized as a sample or event that is irregular with the rest of the data. Recently, an urgent need has emerged to study and understand one of the essential stages in preprocessing data mining, a detection phase of outliers’. Outlier detection techniques can analyze large amounts of data in a relatively short amount of time. Thus, that could reduce the risk of missing any outliers that could indicate helpful information. This research should detect the outliers of students’ data whether the outliers are good or bad. This thesis aims to use one of the data mining techniques, outliers’ detection, to analyze the students’ performance using R tool. The analysis depends on taking into consideration four parameters which are semester GPA, earned hours, number of courses that are taken during the semester, and percentage of those who passed. The dataset used in this research consists of 705 female students and 241 male students enrolled in the Computer Science Department at Umm Al-Qura University. This study has addressed outliers’ observations as a descriptive approach, which is one of the data analytics types. Data was visualized graphically to provide information about the parameters in a dataset, represent the data distribution, and highlight potential outliers easily.