Technologies of Operational Data Analysis

Major: Data Science
Code of subject: 7.124.03.E.030
Credits: 5.00
Department: Information Systems and Networks
Lecturer: Ph.D., Associate Professor Mykola Prodaniuk
Semester: 2 семестр
Mode of study: денна
Learning outcomes: Specialized conceptual knowledge, which includes modern scientific achievements in the field of systems analysis and information technology and is the basis for original thinking and research. Build and research models of complex systems and processes using methods of systems analysis, mathematical, computer and information modeling. Apply methods to reveal uncertainties in the problems of system analysis, to reveal situational uncertainties and uncertainties in the problems of interaction, counteraction and conflict of strategies, to find a compromise in revealing conceptual uncertainty. Develop and apply methods, algorithms and tools for predicting the development of complex systems and processes of different nature. Use risk assessment measures and apply them in the analysis of multifactorial risks in complex systems. To develop intelligent systems in the conditions of poorly structured data of different nature. Develop and apply models, methods, and algorithms for decision-making in conditions of conflict, unclear information, uncertainty, and risks.
Required prior and related subjects: • Discrete Math, • System Analysis, • Optimization Methods and Operations Research.
Summary of the subject: Prerequisites for the development of operational data analytics On-line OLAP data analytics The concept of operational data analytics Methods of operational data analysis Tasks and applications of operational data analytics Technologies for storage and processing of operational data analytics Apache Hadoop online data analytics tools Technologies for visualization of results in operational data analysis
Assessment methods and criteria: • Current control (40%): written reports on laboratory work, essay, oral examination; • Final control (60% exam): in written, verbally.
Recommended books: 1. White, Tom // Hadoop: The Definitive Guide // O'Reilly Media, 2009. 2. Hadoop. Apache Software Foundation // http://hadoop.apache.org/ 3. Finley, Klint // Steve Ballmer on Microsoft's Big Data Future and More in This Week's Business Intelligence Roundup // ReadWriteWeb, 2011. 4. Fay Chang, Jeffrey Dean, Sanjay Ghemawat & etc. // Bigtable: A Distributed Storage System for Structured Data // Google Lab, 2006. 5. Сухорослов, O. // Новые технологии распределенного хранения и обработки больших массивов данных // Институт системного анализа РАН, 2008. 6. Jeffrey Dean, Sanjay Ghemawat // MapReduce: Simplified Data Processing on Large Clusters // Google Inc., 2004.