INeuroinformatics is a rapidly developing inter-disciplinary field which provides an enormous amount of data to be classified, evaluated and interpreted. Usage of exploratory data analysis (EDA) methods is essential in evaluating clinical data in medicine and this analysis remains a big challenge because each new system has specific requirements. Visualiza-tions, models and illustrations of dependency can help in better understanding of measurements in diagnostics and in decision making. The

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... ber of available modern EDA packages for developers is increasing as well as the development in the Data Science field. The development of modern methods of data analysis must also be incorporated in university education. Objective: The aim of the study is to design and develop software, which implements current EDA packages and model making procedures for neuro-logical data analyses which could be easily modified. The second objective is to evaluate the possibility of supporting the education of biomedical engineering students at the undergraduate level in order to provide effective support in bi-omedical data analysis. MethOds: An application has been created under the reactive Shiny framework in the R language. Data in .csv or .tsv format are processed on the server side of the application. Results: We have developed a new easy-to-use software named NeuroEDA for interactive web-based biomedical data assessment. This application covers basic descriptive statistics, exploratory graphs and cluster analysis, which is also suitable for big data examination. Furthermore, this application offers methods for robust and non-parametric analysis. These are particularly useful in neuroinformatics from our long-term experience. The application was practically deployed in the evaluation of clinical neurological data and in teaching the subject Biomedical Statistics. Conclusion: We have introduced the possibility of creating biomedical software for clinical use and demonstration in teaching. Among the advantages of the application, is that it is easily expandability with new R packages and quick processing in web browsers. The interactive user interface allows one to work with R's functions without needing scripting/programming knowledge. Students can acquire practical experience in processing and transformation of heterogeneous medical data not only in biomedical engineering fields, but also at the medical faculties for Medical Informatics. This application is actively used for neuroinformatics data assessment and in discovering some potentially useable hypotheses.