Business Analysis and Big Date-analysis

Major: Public management and administration
Code of subject: 6.281.08.E.226
Credits: 4.00
Department: Administrative and Financial Management
Lecturer: prof. Podolchak N.Yu.
Semester: 7 семестр
Mode of study: денна
Learning outcomes: Possess competencies: ability to conduct analysis, research and visualization large data sets using modern information technologies. Analyze and select optimal solutions when designing distributed databases data, big data collection systems, apply Business Intelligence tools to business data processing and visualization and business intelligence. Use Business Intelligence tools for analysis, monitoring and visualization of business data and building business intelligence
Required prior and related subjects: Computer Science Fundamentals of administrative management Mathematics in public administration, part 1
Summary of the subject: Ensuring the visualization of primary data for further processing, including the usual statistical methods and similar methods, as well as the latest by means of machine learning, artificial intelligence, etc. is relevant today scientific and applied task which is an integral part of the functioning of modern enterprises.
Assessment methods and criteria: lectures, laboratory classes, practical classes, independent work 40% - assessment of practical and laboratory classes 60% - exam (including 10% of the oral component)
Recommended books: 1) Collier Michael S. Microsoft Azure Essentials: Fundamentals of Azure, Second Edition / Michael S. Collier and Robin E. Shahan // Microsoft Press, 2016. – 246 p. 2) Browne D. IBM Cognos Business Intelligence V10.1 Handbook / Dean Browne, Brecht Desmeijter, Rodrigo Frealdo Dumont, Armin Kamal and others // An IBM Redbooks publication, 2010. – 572 p 3) Aleksiev VO Information development of the portal of virtual management of transport service processes / VO Aleksiev, OP Aleksiev // Information technologies: problems and prospects: monograph / for general. ed. VS Ponomarenko. - Kharkiv: Publisher: Rozhko SG, 2017. - Sec. 2. - P. 32 - 47

Business Analysis and Big Date-analysis (курсовий проєкт)

Major: Public management and administration
Code of subject: 6.281.08.E.231
Credits: 3.00
Department: Administrative and Financial Management
Lecturer: prof. Podolchak N.Yu.
Semester: 7 семестр
Mode of study: денна
Learning outcomes: Possess competencies: ability to conduct analysis, research and visualization large data sets using modern information technologies. Analyze and select optimal solutions when designing distributed databases data, big data collection systems, apply Business Intelligence tools to business data processing and visualization and business intelligence. Use Business Intelligence tools for analysis, monitoring and visualization of business data and building business intelligence
Required prior and related subjects: Computer Science Fundamentals of administrative management Mathematics in public administration, part 1
Summary of the subject: Ensuring the visualization of primary data for further processing, including the usual statistical methods and similar methods, as well as the latest by means of machine learning, artificial intelligence, etc. is relevant today scientific and applied task which is an integral part of the functioning of modern enterprises.
Assessment methods and criteria: 100% - evaluation of the course project (including oral defense)
Recommended books: 1) Collier Michael S. Microsoft Azure Essentials: Fundamentals of Azure, Second Edition / Michael S. Collier and Robin E. Shahan // Microsoft Press, 2016. – 246 p. 2) Browne D. IBM Cognos Business Intelligence V10.1 Handbook / Dean Browne, Brecht Desmeijter, Rodrigo Frealdo Dumont, Armin Kamal and others // An IBM Redbooks publication, 2010. – 572 p 3) Aleksiev VO Information development of the portal of virtual management of transport service processes / VO Aleksiev, OP Aleksiev // Information technologies: problems and prospects: monograph / for general. ed. VS Ponomarenko. - Kharkiv: Publisher: Rozhko SG, 2017. - Sec. 2. - P. 32 - 47