Infopaket

«Big Data Analysis, Processing, Storage and Visualization.»

ОК9

Обов’язкова дисципліна для ОП

Second

2021/2022

2 Semester

5

 Develop mathematical models and data analysis methods (including Big Data).  Develop algorithms and software for data analysis (including Big Data).  Software testing  Conduct research in the field of computer science.  Possess knowledge of multi-agent systems design principles and approaches for organizing agent interactions, as well as the ability to use specialized tools for implementing these systems.  Understand the principles of designing knowledge bases and ontologies as the foundation of semantic systems, tools for semantic systems design, and the ability to use the Semantic Web

Distance form

1. Possess knowledge of the methods of intelligent data analysis, fundamentals in mathematical analysis, statistics, and data processing. Have knowledge of the organization of relational and NoSQL databases, knowledge bases, and computer architecture. 2. Have the ability to develop programs in high-level programming languages to accomplish a given task, and to compose complex queries using the SQL language. 3. Possess elementary skills in data processing and analysis, database design, and searching in specialized databases.

The course «Big data analysis, processing, storage and visualization» focuses on the fundamentals of big 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 big data, data preparation, and cleaning. Through laboratory works students will apply theoretical knowledge, select appropriate models and methods to analyze and process big 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

Ukrainian