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Regular version of the site
Master 2019/2020

Big Data Analytics for Industrial Internet

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Type: Elective course (Big Data Systems)
Area of studies: Business Informatics
Delivered by: Department of Innovation and Business in Information Technologies
When: 2 year, 1, 2 module
Mode of studies: offline
Instructors: Olga A. Tsukanova, Ляпина Екатерина Романовна
Master’s programme: Big Data Systems
Language: English
ECTS credits: 6
Contact hours: 56

Course Syllabus

Abstract

“Big Data Analytics in Industrial Internet” is an elective course taught in the 2d year of the master’s program. The present 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 Big Data Analytics in Industrial Internet.
Expected Learning Outcomes

Expected Learning Outcomes

  • Ability to understand the fundamental concepts, principles and approaches to description of the Big Data Landscape in Industry.
  • Ability to use the most common frameworks
  • Ability to analyze the main problems of the Big Data Analytics in Industry
  • Acquantance to the architectural components and programming models used for scalable data analysis
Course Contents

Course Contents

  • Industrial revolutions. 4th Industrial revolution. Features, drivers and challenges. Industry 4.0. Definition, components, design principles
  • Big Data Definition. Data Mining. Data Analytics. Manufacturing Analytics. Sources of data in industrial environment. IoT Gateway: collecting low-level shopfloor data. Smart Factory
  • Data analytics concepts. Data analytics methodologies and architectures. Data analytics tools and platforms: Hadoop framework, MapReduce, HDFS, Tableau. Industrial use cases. SQL and noSQL databases. HBASE: architecture, core work principles
  • CAP theorem, eventual consistency. 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 Practical tasks, individual presentations on selected topics
  • non-blocking paper-based exam
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.4 * paper-based exam + 0.6 * Practical tasks, individual presentations on selected topics
Bibliography

Bibliography

Recommended Core Bibliography

  • Berman, J. J. (2018). Principles and Practice of Big Data : Preparing, Sharing, and Analyzing Complex Information (Vol. Second Edition). London: Academic Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1731816
  • Prabhu, C. S. R. (2019). Fog Computing, Deep Learning and Big Data Analytics-Research Directions. Singapore: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1994845

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