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Regular version of the site
Master 2021/2022

Manufacturing Data Collection and Analytics

Area of studies: Business Informatics
When: 2 year, 1, 2 module
Mode of studies: offline
Open to: students of all HSE University campuses
Master’s programme: Big Data Systems
Language: English
ECTS credits: 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

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

Expected Learning Outcomes

  • Know the fundamental concepts, principles and approaches to description of the Big Data Landscape in Industry
  • 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
  • Learn how to use one of the most common frameworks and tools
Course Contents

Course Contents

  • Industrial revolutions
  • 4th Industrial revolution. Features, drivers and challenges
  • Industry 4.0. Definition, components, design principles
  • 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
  • Industrial use cases
  • SQL and noSQL databases
  • 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

Assessment Elements

  • non-blocking Activity during classes
    Participation in lectures and topics discussions
  • non-blocking Home task
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.2 * Activity during classes + 0.5 * Exam + 0.3 * Home task
Bibliography

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