Abstract
The research looks into the methods of visual representaƟ on of points accumulated by students in a single study course that are employed in modern data science. StaƟ sƟ cal indicators and prospects for improving the subject based on them are discussed: which parts of the subject should be adjusted to increase the training course's quality. Data processing and display methods that the course instructor can employ eff ecƟ vely in the pro-cess of observing conƟ nuous visualisaƟ on of scores are discussed.The arƟ cle introduces a novel approach for enhancing the content of a training course through staƟ sƟ cal anal-ysis and visual representaƟ on of student scores, uƟ lising React.js technology. The proposed plaƞ orm empowers course instructors to incorporate various assessment methods such as quizzes, presentaƟ ons, mid-term exams, and fi nal exams and subsequently display students' grades based on each assessment method. The plaƞ orm em-ploys advanced staƟ sƟ cal indicators and robust visual presentaƟ on capabiliƟ es to calculate and illustrate the points achieved. This enables instructors to easily idenƟ fy desired evaluaƟ on methods, observe the distribuƟ on of points, and simultaneously track mulƟ ple evaluaƟ on methods to monitor the dynamic progression of the training course. Consequently, this holisƟ c approach enables instructors to idenƟ fy and address problemaƟ c secƟ ons within the course content, leading to meaningful improvements.
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