System Analysis Data

Description: The discipline studies methods of data analysis and interpretation in order to identify patterns, trends and patterns. In this course, undergraduates study various data processing techniques, statistical models, machine learning and artificial intelligence for analyzing large amounts of information. The training program includes the study of machine learning algorithms, data visualization, optimization methods and social network analysis. The main purpose of the discipline is to train undergraduates to use data analysis tools to make informed decisions in various fields such as business, science, healthcare and technology

Amount of credits: 5

Пререквизиты:

• Production technologies

Course Workload:

Component: University component

Cycle: Profiling disciplines

Goal

• Get a set of knowledge and understanding about the basic categories related to data analysis, knowledge management. know: - about types of numerical and non-numerical data, differences, methods of working with them; - methods and means of increasing the efficiency and quality of scientific research

Objective

• to gain skills in completing tasks through reasoning, conclusion, calculation

Learning outcome: knowledge and understanding

• the formation of students' knowledge and skills in the field of system research, system design and system analysts

Learning outcome: applying knowledge and understanding

• to apply knowledge and understanding, as well as the ability to solve problems in new and unfamiliar contexts in wider (interdisciplinary) contexts related to their field of study

Learning outcome: formation of judgments

• critically analyze existing concepts, theories and approaches to the analysis of processes and events during design and research tasks

Learning outcome: communicative abilities

• To carry out the solution of a part of the general task during group work

Learning outcome: learning skills or learning abilities

• conduct informational-analytical and informational-bibliographic work involving modern information technologies

Teaching methods

lectures, practical works

Assessment of the student's knowledge

Teacher oversees various tasks related to ongoing assessment and determines students' current performance twice during each academic period. Ratings 1 and 2 are formulated based on the outcomes of this ongoing assessment. The student's learning achievements are assessed using a 100-point scale, and the final grades P1 and P2 are calculated as the average of their ongoing performance evaluations. The teacher evaluates the student's work throughout the academic period in alignment with the assignment submission schedule for the discipline. The assessment system may incorporate a mix of written and oral, group and individual formats.

The evaluating policy of learning outcomes by work type

Evaluation form

The student's final grade in the course is calculated on a 100 point grading scale, it includes:

• 40% of the examination result;
• 60% of current control result.

The final grade is calculated by the formula:

 

Where Midterm 1, Midterm 2are digital equivalents of the grades of Midterm 1 and 2;

E is a digital equivalent of the exam grade.

Final alphabetical grade and its equivalent in points:

The letter grading system for students' academic achievements, corresponding to the numerical equivalent on a four-point scale:

Topics of lectures

• Introduction: basic definitions
• System research
• System analysis
• Modeling of systems
• Physical and mathematical modeling
• Algorithm of mathematical model creation
• Assessment of complex systems
• Main types of measurement scales
• Main types of measurement scales Basic principles of the system analysis
• Stages and sequence of the system analysis
• Some known models
• Model validation
• Basic Requirements
• Expectation and variance
• Deductor

Key reading

• 1. Системный анализ и аналитические исследования: руководство для профессиональных аналитиков /А.И.Ракитов, Д.А.Бондяев, И.Б.Романов, С.В.Егерев,А.Ю.Щербаков. - М. - 2009. 2. Курносов Ю.В., Конотопов П.Ю. Аналитика: методология, технология и организация информационно - аналитической работы. - М.:Русаки, 20о4. - 512 с. http://coollib.eom/b/223786/read 3. Толковый словарь по искусственному интеллекту / Авт.-сост. А.Н. Аверкин, М.Г. Гаазе- Рапопорт, Д.А. Поспелов. - М.: Радио и связь, 1992. - 256 с. http://www.raai.org/librarv/tolk/aivocDred.html 4. А.В. Палагин, Н.Г. Петренко Системноонтологический анализ предметной области. // URL: htto://www.aduis.com.ua/books/1.Ddf 5. Онтологический реинжиниринг бизнеспроцессов оператора связи. //
• 1. Rakitov A.I., Bondyaev D.A., Romanov I.B., Egerev S.V., Shcherbakov A.Yu. System analysis and analytical research: a guide for professional analysts. - M. - 2009. 2. Kurnosov Yu.V., Konotopov P.Yu. Analytics: methodology, technology and organization of information and analytical work. - M.: Rusaki, 20o4. - 512 p. http://coollib.eom/b/223786/read 3. Explanatory Dictionary of Artificial Intelligence / Ed. A.N. Averkin, M.G. Gaaze-Rapoport, D.A. Pospelov. - M.: Radio and communication, 1992. - 256 p. http://www.raai.org/librarv/talk/aivocDred.html 4. A.V. Palagin, N.G. Petrenko System ontological analysis of the subject area. // URL: htto://www.aduis.com.ua/books/1.Ddf 5. Ontological reengineering of telecom operator business processes. // 2. URL: http://ubs.mtas.ru/uDload/librarv/UBS3301.Ddf 6. Livshits V.N. Fundamentals of system thinking and system analysis. - M.: Institute of Economics RAS, 2013. // URL: http://www.inecon.org/docs/2013/Livshits.pdf 7. Gorchinskaya O. Big data analysis. 2012. http://www.oracle.com/ru/corporate/bigdata-analytics-1915803-ru.pdf 8. Susan Tindal. Big Data: Everything You Need to Know, 2012. http://www.pcweek.ru/idea/article/detail.php?ID=14196 9. SIO Magazine #9 September 2012 - http://www.computerra. en/cio/wp-content/uploads/2012/09/CIQ 09-12.pdf