This document is the preface of the
The main purpose of the Modern Data Science Technologies Workshop is providing a forum for researchers to discuss models, methods and information technology for data science, data analysis and business analysis, and their real-life applications [1][2][3][4][5]. In MoDaST Workshop, we encourage the submission of papers on machine learning, deep learning, decision making, and multicriteria decision analysis areas. The MoDaST Workshop is soliciting literature review, survey and research papers comments including, whilst not limited to, the following areas of interest: Analytical methods; Ontological engineering; Business analysis of information processes; The language of Modern Data Science Technologies Workshop is English. The Modern Data Science Technologies Workshop took the form of oral presentation by peer-reviewed individual papers. The papers were distributed among 32 external reviewers from The Netherlands, Finland, Germany, France, United Kingdom, China, Austria, Czech Republic, Portugal, India, Poland, Ukraine and Ukraine.
The Modern Data Science Technologies Workshop gathered participants from different countries including Germany, Algeria, The Netherlands, Finland, Poland, and Ukraine.
This year Organizing Committee received 55 submissions, out of which 27 were accepted for presentation as a regular paper. These papers and extended abstracts were published in this Volume II of the 6 th International Workshop on Modern Data Science Technologies (MoDaST 2024) proceedings.
The Modern Data Science Technologies Workshop would not have been possible without the support of many people. First of all, we would like to thank all the authors who submitted papers to Modern Data Science Technologies Workshop and thus demonstrated their interest in the research problems within our scope. We are very grateful to the members of our Program Committee for providing timely and thorough reviews and, also, for being cooperative in doing additional review work. We would like to thank the Organizing Committee of the workshop devotion and efficiency made this instance of Modern Data Science Technologies Workshop a very interesting and effective scientific forum. We would like to thank Modern Data Science Technologies Workshop Chairs, as well as Program Committee and all Reviewers, for their diligence in selecting the papers and ensuring their high scientific quality.