Technologies of Data Analytics

Major: Data Science
Code of subject: 7.124.03.O.008
Credits: 5.00
Department: Information Systems and Networks
Lecturer: Dr. Tech. Sc. Andrii Berko
Semester: 2 семестр
Mode of study: денна
Мета вивчення дисципліни: 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.
Завдання: 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.
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 Data Analytics development On-line OLAP data analytics Data analytics concept Methods of data analysis Tasks and applications of data analytics technologies Technologies of storage and processing of data analytics Apache Hadoop data analytics platform Technologies of visualization of results in data analysis
Опис: • Current control (40%): written reports on laboratory work, essay, oral examination; • Final control (60% exam): in written, verbally.
Assessment methods and criteria: • Current control (40%): written reports on laboratory work, essay, oral examination; • Final control (60% exam): in written, verbally.
Критерії оцінювання результатів навчання: • Current control (40%): written reports on laboratory work, essay, oral examination; • Final control (60% exam): in written, verbally.
Порядок та критерії виставляння балів та оцінок: 100-88 points - certified with an “excellent” grade - High level: the student demonstrates an in-depth mastery of the conceptual and categorical apparatus of the discipline, systematic knowledge, skills and abilities of their practical application. The mastered knowledge, skills and abilities provide the ability to independently formulate goals and organize learning activities, search and find solutions in non-standard, atypical educational and professional situations. The applicant demonstrates the ability to make generalizations based on critical analysis of factual material, ideas, theories and concepts, to formulate conclusions based on them. His/her activity is based on interest and motivation for self-development, continuous professional development, independent research activities, implemented with the support and guidance of the teacher. 87-71 points - certified with a grade of “good” - Sufficient level: involves mastery of the conceptual and categorical apparatus of the discipline at an advanced level, conscious use of knowledge, skills and abilities to reveal the essence of the issue. Possession of a partially structured set of knowledge provides the ability to apply it in familiar educational and professional situations. Aware of the specifics of tasks and learning situations, the student demonstrates the ability to search for and choose their solution according to the given sample, to argue for the use of a particular method of solving the problem. Their activities are based on interest and motivation for self-development and continuous professional development. 70-50 points - certified with a grade of “satisfactory” - Satisfactory level: outlines the mastery of the conceptual and categorical apparatus of the discipline at the average level, partial awareness of educational and professional tasks, problems and situations, knowledge of ways to solve typical problems and tasks. The applicant demonstrates an average level of skills and abilities to apply knowledge in practice, and solving problems requires assistance, support from a model. The basis of learning activities is situational and heuristic, dominated by motives of duty, unconscious use of opportunities for self-development. 49-00 points - certified with a grade of “unsatisfactory” - Unsatisfactory level: indicates an elementary mastery of the conceptual and categorical apparatus of the discipline, a general understanding of the content of the educational material, partial use of knowledge, skills and abilities. The basis of learning activities is situational and pragmatic interest.
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. 6. Jeffrey Dean, Sanjay Ghemawat // MapReduce: Simplified Data Processing on Large Clusters // Google Inc., 2004.

Technologies of Data Analytics (курсова робота)

Major: Data Science
Code of subject: 7.124.03.O.009
Credits: 2.00
Department: Information Systems and Networks
Lecturer: Dr. Tech. Sc. Andrii Berko
Semester: 2 семестр
Mode of study: денна
Мета вивчення дисципліни: 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.
Завдання: 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.
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 Data Analytics development On-line OLAP data analytics Data analytics concept Methods of data analysis Tasks and applications of data analytics technologies Technologies of storage and processing of data analytics Apache Hadoop data analytics platform Technologies of visualization of results in data analysis
Опис: • Current control (40%): written reports on laboratory work, essay, oral examination; • Final control (60% exam): in written, verbally.
Assessment methods and criteria: • Current control (40%): written reports on laboratory work, essay, oral examination; • Final control (60% exam): in written, verbally.
Критерії оцінювання результатів навчання: • Current control (40%): written reports on laboratory work, essay, oral examination; • Final control (60% exam): in written, verbally.
Порядок та критерії виставляння балів та оцінок: 100-88 points - certified with an “excellent” grade - High level: the student demonstrates an in-depth mastery of the conceptual and categorical apparatus of the discipline, systematic knowledge, skills and abilities of their practical application. The mastered knowledge, skills and abilities provide the ability to independently formulate goals and organize learning activities, search and find solutions in non-standard, atypical educational and professional situations. The applicant demonstrates the ability to make generalizations based on critical analysis of factual material, ideas, theories and concepts, to formulate conclusions based on them. His/her activity is based on interest and motivation for self-development, continuous professional development, independent research activities, implemented with the support and guidance of the teacher. 87-71 points - certified with a grade of “good” - Sufficient level: involves mastery of the conceptual and categorical apparatus of the discipline at an advanced level, conscious use of knowledge, skills and abilities to reveal the essence of the issue. Possession of a partially structured set of knowledge provides the ability to apply it in familiar educational and professional situations. Aware of the specifics of tasks and learning situations, the student demonstrates the ability to search for and choose their solution according to the given sample, to argue for the use of a particular method of solving the problem. Their activities are based on interest and motivation for self-development and continuous professional development. 70-50 points - certified with a grade of “satisfactory” - Satisfactory level: outlines the mastery of the conceptual and categorical apparatus of the discipline at the average level, partial awareness of educational and professional tasks, problems and situations, knowledge of ways to solve typical problems and tasks. The applicant demonstrates an average level of skills and abilities to apply knowledge in practice, and solving problems requires assistance, support from a model. The basis of learning activities is situational and heuristic, dominated by motives of duty, unconscious use of opportunities for self-development. 49-00 points - certified with a grade of “unsatisfactory” - Unsatisfactory level: indicates an elementary mastery of the conceptual and categorical apparatus of the discipline, a general understanding of the content of the educational material, partial use of knowledge, skills and abilities. The basis of learning activities is situational and pragmatic interest.
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. 6. Jeffrey Dean, Sanjay Ghemawat // MapReduce: Simplified Data Processing on Large Clusters // Google Inc., 2004.