Course: Data science

Structural unit: Faculty of information Technology

Title
Code
ОК 32
Module type
Обов’язкова дисципліна для ОП
Educational cycle
First
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
5 Semester
Number of ECTS credits allocated
4
Learning outcomes
To develop software modules of subject environments, to choose a programming paradigm from the point of view of convenience and quality of application for the implementation of methods and algorithms for solving problems in the field of computer science. Use tools for the development of client-server applications, design conceptual, logical and physical models of databases, develop and optimize queries to them, create distributed databases, data stores and showcases, knowledge bases, including on cloud services, using web languages -programming. Apply methods and algorithms of computational intelligence and intelligent data analysis in tasks of classification, forecasting, cluster analysis, search for associative rules using software tools to support multidimensional data analysis, based on Data Mining, Text Mining, Web Mining technologies.
Form of study
Full-time form
Prerequisites and co-requisites
Know the basics of building intelligent systems, knowledge presentation models, reasoning models in artificial intelligence. Be able to analyze information flows, build information-logical and conceptual schemes of the subject environment, design knowledge bases of expert systems. Possess the skills of creating expert systems, the basics of HTML markup.
Course content
The purpose of the discipline is the formation of a system of professional competence in knowledge management in students, the study of the theoretical foundations of ontology engineering as structural units of knowledge presentation on the Internet, the acquisition of practical skills in the design, implementation and application of ontologies as part of an informational web resource. The basics of Semantic Web construction and technology, open data LOD, WikiData, DBPedia, Knowledge Graph are considered. A significant part of the course is devoted to the consideration of ontologies, their main components, the definition and description of classes, the technology of development of the ontology of the subject area, the application of descriptive logic in the work with ontologies is considered, the ontology description languages RDF, RDFS, OWL, and the SPARQL query language are considered. The course covers the process of creating ontologies using the Protégé editor.
Recommended or required reading and other learning resources/tools
Planned learning activities and teaching methods
Lectures, laboratory work, individual work
Assessment methods and criteria
Assessment of students is carried out during the semester for all types of work, including the study of the theoretical material of the course, the performance of laboratory work. To determine the level of achievement of learning outcomes, students present the results of the developed program during the defense of laboratory work reports, answer the teacher's questions, to test the acquired skills, the teacher can give additional tasks that must be implemented by the student during the defense of the work. During the semester, two current written control tests MKR1, MKR2 are conducted. Tests include theoretical material, practical tasks (solving problems) and test. The condition for receiving a positive grade in the discipline is to achieve at least 60% of the maximum possible number of points. The maximum number of points that a student can receive for work during the semester is 100 points. The credit is issued to the student based on the results of the work during the semester.
Language of instruction
Ukrainian

Lecturers

This discipline is taught by the following teachers

Departments

The following departments are involved in teaching the above discipline