2025/2026
Manufacturing Data Collection and Analytics
Type:
Mago-Lego
Delivered by:
Department of Business Informatics
When:
2, 3 module
Open to:
students of one campus
Instructors:
Sergey Makarov
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
- basics of Internet of Things functionality, purposes, implementations, applications
- how to work with the according hardware
- how to use certain libraries in the software environment dedicated to Internet of Things
- how to code and use the hardware boards to run the code
- how to use IoT cloud platforms for collecting and analyzing the sensors data
- how to develop a mobile application
Course Contents
- Introduction to Internet of Things. IoT Architecture, Examples, Implementations. Gartner Hype Cycle Curve for Emerging Technologies. IoT types.
- Sensors, resistors, breadboard, modules, displays and other usual components of an IoT kit. Arduino Uno. Types, connections, ports, modules, electrical circuits, etc.
- IoT Operating Systems. Raspberry Pi. Connections, ports, modules, etc. Cloud IoT platforms, data collection and analysis. IoT implementations.
- Practice with Arduino Uno and Raspberry Pi. Android Studio and Arduino IDE. Practice with cloud IoT platforms, data collection and analysis. IoT implementations (Smart Home, Smart Factory, Remote Weather Station, Voice Controlled LED Strip etc.).
- Building IoT project
Assessment Elements
- 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.
- 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.
Bibliography
Recommended Core Bibliography
- Nihtianov, S., & Luque, A. (2018). Smart Sensors and MEMS : Intelligent Sensing Devices and Microsystems for Industrial Applications: Vol. Second edition. Woodhead Publishing.
- Olof Liberg, Marten Sundberg, Eric Wang, Johan Bergman, Joachim Sachs, & Gustav Wikström. (2020). Cellular Internet of Things : From Massive Deployments to Critical 5G Applications. Academic Press.
- Papadopoulos, G., Theoleyre, F., & Vilajosana, X. (2020). Industrial Internet of Things: Specificities and Challenges. https://doi.org/10.1002/ITL2.172
- Zoran Gacovski. (2019). Internet of Things. [N.p.]: Arcler Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2013945
Recommended Additional Bibliography
- 9781789538304 - Giacomo Veneri, Antonio Capasso - Hands-On Industrial Internet of Things : create a powerful Industrial IoT infrastructure usingIndustry 4.0 - 2018 - Packt Publishing - https://search.ebscohost.com/login.aspx?direct=true&db=nlebk&AN=1948705 - nlebk - 1948705
- AZZOLA, F. (2017). Android Things Projects : Efficiently Build IoT Projects with Android Things. [Place of publication not identified]: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1547024
- Gutschow, E. (2019). Big Data-driven Smart Cities: Computationally Networked Urbanism, Real-Time Decision-Making, and the Cognitive Internet of Things. Geopolitics, History & International Relations, 11(2), 48–54. https://doi.org/10.22381/GHIR11220197
- Hassan, Q. F., Khan, A. ur R., & Madani, S. A. (2018). Internet of Things : Challenges, Advances, and Applications. Boca Raton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1663018
- Mohamad Jamil, & Muhamad Said. (2018). The Utilization of Internet of Things (IoT) for Multi Sensor Data Acquisition using Thingspeak. Volt: Jurnal Ilmiah Pendidikan Teknik Elektro, (1). https://doi.org/10.30870/volt.v3i1.1962
- Макаров, С. Л. Arduino Uno и Raspberry Pi 3: от схемотехники к интернету вещей : руководство / С. Л. Макаров. — Москва : ДМК Пресс, 2018. — 204 с. — ISBN 978-5-97060-730-5. — Текст : электронный // Лань : электронно-библиотечная система. — URL: https://e.lanbook.com/book/116131 (дата обращения: 00.00.0000). — Режим доступа: для авториз. пользователей.