Infopaket

Modeling and Visualization of Multidimensional Data

ННД 1.08

Вибіркова дисципліна для ОП

Second

2021/2022

2 Semester

5

Apply methodological principles of scientific research; work with a disciplinary array of publications: conduct search, accumulation, and processing of scientific information; select and formulate the research problem; choose the methodological foundation of the research; formulate the object and subject of the study; formulate and test scientific hypotheses; develop a set of methodologies for researching the chosen subject. Apply modern methods of processing and analysis of large sets of statistical data used to solve current economic challenges in business; perform modeling of business processes using methods of intelligent data analysis; construct hypercubes for visualization and analysis of multidimensional data; present and interpret results obtained during the analysis of multidimensional data, formulate conclusions and recommendations; make informed management decisions based on the analysis of trends in the development of key business segments.

Possess knowledge of the methods of intelligent data analysis, fundamentals in mathematical analysis, data processing. Have knowledge of the organization of databases, knowledge bases, and computer architecture. Ability to write programs to execute simple algorithms and compose queries. Possess elementary skills in data processing and analysis, database design, and searching in specialized databases.

The course «Modeling and Visualization of Multidimensional Data» focuses on the fundamentals of multidimensional data analysis, processing, modeling, and visualization. Students will explore data representation models, dimensionality reduction methods, and techniques for discovering hidden knowledge in data. The course covers key methods for processing and analyzing multidimensional data, data preparation, and cleaning. Through laboratory works students will apply theoretical knowledge, select appropriate models and methods to analyze and process multidimensional data, generate reports, and create visualizations, dashboards.

1.Marchenko O.O., Rossada T.V. Aktualʹni problemy Data Mining: Navchalʹnyy posibnyk dlya studentiv fakulʹtetu kompʺyuternykh nauk ta kibernetyky. — Kyyiv. — 2017. — 150 s. 2.Zgurovsky M.Z., Zaychenko Y.P. Big Data: Conceptual Analysis and Applications. Springer, 2020. – 298 p. 3.Davy Cielen, Arno D. B. Meysman, and Mohamed Ali Introducing Data Science: Big data, machine learning, and more, using Python tools. – Manning, 2016. – 320 p. 4. Foster Provost, Tom Fawcett Data Science for Business: What You Need to Know about Data Mining and Data-Analytic by Thinking K., Nash format – 2019, 400 p. 5. Balamurugan Balusamy, Nandhini Abirami R, Seifedine Kadry, Amir H. Gandomi Big Data: Concepts, Technology, and Architecture. Wiley; 1st edition. – 2021, 368 p. 6.Elizabeth Clarke. Data Analytics, Data Visualization & Communicating Data. -Kenneth M Fornari, 2022, 528 p.

Lectures, laboratory work, individual work

The achievement level of all intended learning results is determined by the results of laboratory work and tests. Throughout the semester, student evaluation is conducted for all types of work. The final assessment is an exam, which includes a test and a practical task. The maximum score for the exam is 40 points, and the minimum passing score is 24 points. The semester evaluation is calculated by adding up the points earned for all successfully evaluated learning results, the maximum value is 60 points. To be eligible to take the exam, students must complete at least 60% of the laboratory work, and the minimum score required for exam admission is 20 points. The total score is calculated as a weighted sum of the points earned by the student for all types of work and exam grade. The minimum score required to complete the course is 60 points, maximum is 100 points

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