Магистратура
2025/2026
Сбор и аналитика производственных данных
Статус:
Курс по выбору (Бизнес-аналитика и системы больших данных)
Кто читает:
Департамент бизнес-информатики
Где читается:
Высшая школа бизнеса
Когда читается:
2-й курс, 2, 3 модуль
Охват аудитории:
для своего кампуса
Преподаватели:
Макаров Сергей Львович
Язык:
английский
Кредиты:
6
Course Syllabus
Abstract
“Manufacturing Data Collection and Analytics” is an elective course taught in the 2d year of the master’s program. The course is designed to give students an overview of an industrial environment as a source of data and related techniques of big data analytics. The duration of the course covers two modules. The course is taught in English and worth 6 credits.
Learning Objectives
- The present course is to introduce students to the core concepts of Manufacturing Data Collection and Analytics.
- Course gives an overview of a industrial applications with BD analytical approach
- The complete technological stack for Machine Data Collection up to cloud analytics
Expected Learning Outcomes
- Be able to understand the main problems of the Big Data Analytics in Industry, get acquainted to the architectural components and programming models used for scalable data analysis
- Know the fundamental concepts, principles and approaches to description of the Big Data Landscape in Industry
- Learn how to use one of the most common frameworks and tools
Course Contents
- Industrial revolutions
- Industry 4.0. Definition, components, design principles
- 4th Industrial revolution. Features, drivers and challenges
- Data Analytics. Manufacturing Analytics
- Sources of data in industrial environment
- Industrial Control Fundamentals
- IoT and IIoT. Evolution of the industrial products and devices
- IoT Gateway: collecting low-level shopfloor data
- Smart Factory
- Data analytics concepts
- Data analytics methodologies and architectures
- Data analytics tools and platforms application in industry
- SQL and noSQL databases
- Industrial use cases
- CAP theorem, eventual consistency
- HBASE: architecture, core work principles
- Reference architectures in Industry 4.0
- RAMI 4.0 – The Reference Architectural Model for I4.0
- National and alternative reference architectures
- Criteria for I4.0 products
Assessment Elements
- Homework AssignmentHomework assignment is developing a network of things using one of the system on module boards as a base and the according software and operating system. Passing the assignment means to show the whole system working and to be able to answer any questions regarding the code of the assignment script and theory. Each student gets different assignments which he/she chooses him-/herself.
- Laboratory WorksEach student follows the methodical guide on the course practice works and develops 8 hardware + software projects. Passing the ap-plication means to show the whole system working and mobile application controlling it or getting some information from the hardware system and to be able to answer any questions regarding the code of the assignment script and theory.
Bibliography
Recommended Core Bibliography
- Lin, J., & Dyer, C. (2010). Data-Intensive Text Processing with MapReduce. Morgan & Claypool Publishers.
- White T. Hadoop: The Definitive Guide. - O'Reilly Media, 2015.
- White, T. (2015). Hadoop: The Definitive Guide : Storage and Analysis at Internet Scale: Vol. 4th edition. O’Reilly Media.
Recommended Additional Bibliography
- Buyya, R., Calheiros, R. N., & Vahid Dastjerdi, A. (2016). Big Data : Principles and Paradigms. Cambridge, MA: Morgan Kaufmann. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1145031