2023/2024![Learning Objectives](/f/src/global/i/edu/objectives.svg)
![Expected Learning Outcomes](/f/src/global/i/edu/results.svg)
![Course Contents](/f/src/global/i/edu/sections.svg)
![Assessment Elements](/f/src/global/i/edu/controls.svg)
![Interim Assessment](/f/src/global/i/edu/intermediate_certification.svg)
![Bibliography](/f/src/global/i/edu/library.svg)
Manufacturing Data Collection and Analytics
Type:
Mago-Lego
Delivered by:
Department of Business Informatics
When:
1, 2 module
Open to:
students of all HSE University campuses
Instructors:
Ковалев Илья Александрович
Language:
English
ECTS credits:
6
Contact hours:
48
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.
- Data analytics concepts.
- Data Analytics. Manufacturing Analytics.
- Reference architectures in Industry 4.0. RAMI 4.0
- Smart Factory
- IoT Gateway: collecting low-level shopfloor data
- SQL and noSQL databases
- Predictive Analytics
- CUDA
- WEB technologies for data analytics
- Data Science for Industrial Data
- Criteria for I4.0 products
- Industrial Data Collection Methodology
- IoT and IIoT.
- Industrial Control Fundamentals
- HBASE
- Python DS
- Industrial Protocols
- Industrail control systems
- Reference architectures in Industry 4.0
- National and alternative reference architectures
- Industrial use cases
- IoT and IIoT devices
- Big Data in Smart Factory
- Auto Identification
Assessment Elements
- Home task
- Exam
- Activity during classesParticipation in lectures and topics discussions
Interim Assessment
- 2023/2024 2nd module0.2 * Activity during classes + 0.52 * Exam + 0.28 * Home task
Bibliography
Recommended Core 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
- 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
- Mahmood, Z. (2016). Data Science and Big Data Computing : Frameworks and Methodologies. Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1203573