Manufacturing Data Collection and Analytics

2023/2024

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

Full 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 classes
Participation in lectures and topics discussions

Interim Assessment

2023/2024 2nd module
0.2 * 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

Course Syllabus