Home/ Majors directory/Computer Sciences/Data Visualization
Data Visualization
Major: Computer Sciences
Code of subject: 7.122.04.E.033
Credits: 5.00
Department: Artificial Intelligence Systems
Lecturer: Boyko Nataliya
Semester: 2 семестр
Mode of study: денна
Завдання: The study of an educational discipline involves the formation of competencies in students of education:
Integral competence:
The ability to use in-depth theoretical and fundamental knowledge in information technology and artificial intelligence to effectively solve complex, specialized tasks and practical problems during professional activity or in the learning process, which involves their application to the development of complex systems characterized by complexity and uncertainty of conditions.
General competences:
1. the ability to communicate in a second language.
2. the ability to learn.
3. the ability to communicate orally and in writing in Ukrainian.
4. the ability to search and analyze information from various sources;
5. the ability to identify, formulate and solve problems;
6. ability to apply knowledge in practical situations;
7. ability to make informed decisions;
8. ability to conduct research at the appropriate level;
9. ability to work in a team;
10. knowledge and understanding of the subject area and experience of the profession;
11. ability to think abstractly, analyze and synthesize;
12. ability to develop and manage projects;
13. ability to work independently.
Professional competences of the speciality:
1. the ability of a flexible way of thinking, which makes it possible to understand and solve problems and tasks while maintaining a critical attitude to established scientific concepts;
2. the ability to use in-depth theoretical and fundamental knowledge in the field of artificial intelligence to develop complex systems;
3. the ability to build appropriate models of complex systems, to research them for the construction of information systems projects;
4. the ability to formulate (making presentations or presenting reports) new hypotheses and scientific problems in the field of artificial intelligence and natural language processing, to choose appropriate directions and appropriate methods for their solution;
professional competencies of specialization:
1. the ability to effectively use deep learning methods for applied tasks;
2. the ability to design and parameterize components of an intelligent information system based on mathematical models and methods of artificial intelligence;
3. the ability to form requirements for the development of intelligent systems;
4. the ability to orientate at the level of a specialist in a particular narrow field of artificial intelligence systems, which lies outside the boundaries of the chosen specialization;
5. the ability to effectively conduct system analysis, select a conceptual model of the information system environment based on mathematical models and methods of artificial intelligence, and parameterize the components of the intelligent information system;
6. the ability to be a leader in the development and implementation of an intelligent information system project
Learning outcomes: Results of learning knowledge
1. The ability to formulate and improve a significant research problem, collect the necessary information for its solution, and develop conclusions that can be defended in a scientific context.
2. The ability to use professional knowledge and practical skills to optimize the design of information systems of any complexity, to solve specific tasks of designing intelligent information systems for managing objects of different physical nature.
3. Ability to carry out practical communication activities of the information system project development team.
4. The ability to work with expert and textual sources of information to integrate data and knowledge in the organisation field using methods of knowledge acquisition, presentation, classification and compilation of knowledge.
Skill (UM):
1. The ability to create mathematical models and decision-making algorithms using algorithmic and software, using machine learning, artificial neural networks, evolutionary modelling, genetic methods of optimization, the inductive modelling method and the mathematical apparatus of fuzzy logic.
2. The ability to develop mathematical models and algorithms for pattern recognition and object classification in intelligent decision-making systems in designing pattern recognition systems with the help of appropriate mathematical support, using formal system representation procedures.
3. The ability to develop a functional environment of open systems, application programming interfaces, application programs and applications with the following properties: extensibility, scalability, interoperability, the ability to integrate, readiness and reliability of the system.
Communication:
1. ability to communicate, including oral and written communication in Ukrainian and foreign languages ??(English, German, Italian, French, Spanish).
2. the ability to use various methods, including modern information technologies, for effective communication at the professional and social levels.
Autonomy and responsibility:
1. the ability to adapt to new situations and make appropriate decisions.
2. the ability to realize the need for lifelong learning to deepen acquired and acquire new professional knowledge.
3. the ability to take responsibility for the work performed, make decisions independently, and achieve the set goal in compliance with professional ethics requirements.
Teaching and learning methods:
• Performance of laboratory works and their protection.
• Writing test papers.
• Writing calculation and graphic work
Methods of assessing the level of achievement of learning outcomes:
• Evaluation of laboratory works.
• Evaluation of calculation and graphic works.
• Testing.
Required prior and related subjects: Previous academic disciplines:
Intelligent data analysis
Machine learning
Associated and following academic disciplines:
Big data mining
Semantic data analysis
Summary of the subject: Educational discipline Data visualization of the "master's" qualification level will provide students with the acquisition of in-depth theoretical and practical knowledge, skills and understanding related to the areas of artificial intelligence systems, which will allow them to effectively perform tasks of an innovative nature at the appropriate level of professional activity, which is focused on research and development solving complex problems of designing and developing information systems to meet the needs of science, business and enterprises in various fields.
Опис: 1. INTRODUCTION
Purpose, tasks and content of the course. Basic concepts and approaches to intelligent data analysis and their visualization processes. The discipline's connection with other subjects is related to the study of visual data analysis technologies.
2 Decision support systems and the concept of data storage
The main tasks are solved by decision support systems (DSS). The general architecture of SPPR. Approaches to the implementation of databases (DBs) used in SPPR. OLTP systems. The concept of data warehouses (DW). Structures of SPPR with different types of SD. Organization of data in SD. Data cleaning methods in SD. Conclusions regarding the organization of SPPR and SD.
3 OLAP systems
Definition of OLAP system and data model used in OLAP systems. Requirements for OLAP systems. Types of OLAP systems. Conclusions on the advantages and disadvantages of various architectures of OLAP systems.
4 Intelligent analysis of data and processes in adaptive systems
Problems are solved by methods of intellectual analysis of data and processes. Models used in the technologies of intelligent analysis of data and processes. Ways of building models of philosophical analysis of data and processes. Software products of intellectual analysis of data and processes.
5 Methods of classification and regression on the construction of mathematical functions
General analysis of classification and regression methods. The problem of inductive learning. Methods of construction of classification rules. 1R-algorithm. Naive Bayes method. Available formulation of the problem of constructing mathematical functions. Linear and non-linear methods. Support Vector Machines (SVM). Ways of improving the properties of classification and regression methods. Time series forecasting.
6 Inductive logical inference using solution trees
Definition of the solution tree. Properties of a solution tree, construction of a reduced solution tree. Structure of a top-down solution tree using the ID3 algorithm. Selection of properties (key attributes) in the solution tree completion algorithm ID3. Data processing problems for building a solution tree. Coverage algorithm.
7 Data clustering
Statement of the problem of cluster analysis. Presentation of cluster analysis results. Classification of clustering algorithms. Hierarchical algorithms: agglomerative and divisive clustering algorithms. Non-hierarchical clustering algorithms: k-means and Fuzzy C-Means algorithms. Principles of building adaptive clustering algorithms. Conclusions on the advantages and disadvantages of different clustering algorithms.
8 Visual analysis of data and processes. Analysis of textual data
Tasks and general principles of visual analysis of data and processes. Characteristics of software tools for visualization of data and processes: methods of geometric transformations, methods that rely on displaying "icons", methods that rely on pixels. Hierarchical images. The essence and stages of solving the problem of textual data analysis. Classification of text documents. Methods of text document clustering. Solving the task of annotating texts. Text information analysis tools: Oracle Text, Intelligent Miner for Text, Text Miner, TextAnalyst.
Assessment methods and criteria: 1. Performance of laboratory works and their protection.
2. Writing test papers.
3. Writing calculation and graphic work
4. Exam.
Критерії оцінювання результатів навчання: - current control (40%): written reports on laboratory work, practical tasks, oral examination;
- final control (60% of exam), testing (50%), oral component (10%).
Порядок та критерії виставляння балів та оцінок: 100–88 points – ("excellent") is awarded for a high level of knowledge (some inaccuracies are allowed) of the educational material of the component contained in the primary and additional recommended literary sources, the ability to analyze the phenomena being studied in their relationship and development, clearly, succinctly, logically, consistently answer the questions, the ability to apply theoretical provisions when solving practical problems; 87–71 points – ("good") is awarded for a generally correct understanding of the educational material of the component, including calculations, reasoned answers to the questions posed, which, however, contain certain (insignificant) shortcomings, for the ability to apply theoretical provisions when solving practical tasks; 70 – 50 points – ("satisfactory") is given for weak knowledge of the component's educational material, inaccurate or poorly reasoned answers, with a violation of the sequence of presentation, for invalid application of theoretical provisions when solving practical problems; 49-26 points - ("not certified" with the possibility of retaking the semester control) is awarded for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to apply theoretical provisions when solving practical problems; 25-00 points - ("unsatisfactory" with mandatory re-study) is awarded for ignorance of a significant part of the educational material of the component, significant errors in answering questions, inability to navigate when solving practical problems, ignorance of the main fundamental provisions.
Recommended books: 1. Savchenko, D. V. Fundamentals of processing and visualization of physical data in the OriginPro software environment 8. Computer workshop [Electronic resource]: training. manual for bachelor's degree holders in the educational program "Computer modeling of physical processes" in specialty 104 "Physics and astronomy / D. V. Savchenko. – Kyiv: KPI named after Igor Sikorsky, 2021. - 111 p.
2. Infographics: study guide / compiled by O. V. Gudim – Chernivtsi, Chernivtsi National University, 2017. – 107 p.
3. Horvat A.A., Molnar O.O., Minkovich V.V. Processing, visualization and analysis of experimental data using the Origin package: A tutorial. – Uzhgorod: Publishing House of UzhNU “Hoverla”, 2020. – 64 p.
4. Ashanin V.S., Pasko V.V. Processing and visualization of scientific research data. Tutorial. Part 1. Kharkiv: KhDAFK, 2020, 132 p.
5. Chen C. Handbook of Data VisualizaOon / C. Chen, W. Hardle, A. Unwin. - Berlin Heidelberg: Springer-Verlag, 2008. - 936 p.
6. Kvyetnyi R.N. Computer modeling of systems and processes. Calculation methods. Part 1: study guide / R. N. Kvetny, I. V. Bogach, O. R. Boyko, O. Yu. Sofina, O.M. Shushura; in general ed. R.N. Kvyetny – Vinnytsia: VNTU, 2012. – 193 p.
7. R. N. Kvyetny Filtering methods of textured images in recognition and classification tasks / R. N. Kvyetny, O. Yu. Sofina. – Vinnytsia: UNIVERSUM-Vinnytsia, 2011. – 119 p.
8. Lyubchak, V.O. Calculation methods and algorithms [Text]: teaching. manual / V.O. Lyubchak, L.D. Nazarenko. – Sumy: Sumy State University, 2008. – 313 p.
9. Matviychuk Y.M. Computer calculation methods and algorithms: teaching. manual / Y.M. Matviychuk. - Lviv: Liga-Press, 2008. - 84 p.
10. L. P. Feldman Numerical methods in computer science: a textbook / L. P. Feldman, M. Z. Zgurovsky, L. P. Feldman, A. I. Petrenko, O. A. Dmitrieva. - K.: Ed. BHV group, 2006. – 480 p.
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