Big Data analytics

Description: The course is aimed at developing the skills of analyzing large amounts of data to solve scientific and technological problems in the direction of dissertation research. The doctoral student acquires practical skills in solving experimental and theoretical problems in the field of big data analytics. The acquired practical skills will make it possible to present the results of the dissertation research in scientific journals and research reports in the form of a computer executable module of the computational model of experimental data attached to the article.

Amount of credits: 5

Пререквизиты:

• Introduction to Data Mining methods

Course Workload:

Component: Component by selection

Cycle: Profiling disciplines

Goal

• Acquisition of practical skills in conducting scientific research using modern data analysis technologies. Development of big data analysis skills to solve a wide range of applications, including analysis of corporate data, financial data from global data warehouse markets, data storage and processing modeling, forecasting of complex indicators.

Learning outcome: knowledge and understanding

• Understand the theory and foundations of storing, processing and analyzing big data, advanced tools for collecting, storing, transferring and visualizing big data.

Learning outcome: applying knowledge and understanding

• Be able to process and analyze large amounts of data using modern software

Learning outcome: formation of judgments

• the ability to independently apply methods and means of knowledge, learning and self-control, to be aware of the prospects of intellectual, cultural, moral, physical and professional self-development and self-improvement, to be able to critically assess their strengths and weaknesses.

Learning outcome: communicative abilities

• arry out communications in the professional sphere and in society as a whole, including in a foreign language, analyze existing and develop independently technical documentation, clearly state and protect the results of complex engineering activities in the field of IT technologies

Learning outcome: learning skills or learning abilities

• readiness to change social, economic, professional roles, geographic and social mobility in the face of dynamics of change, to continue learning independently

Teaching methods

When conducting training sessions, the use of the following educational technologies is provided: - Technology of research activities - Technology of educational and research activities - Communication technologies (discussions, press conference, brainstorming, educational debates, etc.) - Information and communication (including remote) technologies

Key reading

• Rajaraman, J. Leskovec and J. D. Ullman, Mining of Massive Datasets, 2nd Edition
• Xiaoyuan Su and Taghi M. Khoshgoftaar, Review Article: A Survey of Collaborative Filtering Techniques.

Further reading

• R. Burke, Hybrid Web Recommender Systems.