Module: Data Analytics (M3)
| Study level | Master | |
| ECTS credits | 3 | |
| Study forms | Hybrid or fully online | |
| Module aims | The key aim of the course is to familiarize the students with the most important groundbreaking information technologies used in manipulating, storing, and near-real-time analyzing of data in IoT systems. | |
| Pre-requirements | Has some understanding of IoT (passed module "Introduction to IoT") | |
| Learning outcomes | After completing this course, the student: - identifies challenges in Data analytics - recognize main tools and frameworks for Data analytics - knows what are regression, clustering, and classification models - has overview of time series analysis in IoT - can apply data analytics on real-life IoT use case |
|
| Topics | IoT Data Analysis Data products development Data preparation for data analysis Regression models Clustering models Classification models Introduction to time series analysis Hints for further readings on AI |
|
| Type of assessment | Prerequisite of a positive grade is a positive evaluation of course topics and presentation of practical work results with required documentation | |
| Blended learning | Along with MOOC course in hybrid mode. | |
| References to literature | 1. M Vergin Raja Sarobin, J Ranjith, D Ashwath, K Vinithi, Smiti, V Khushi, Comparative Analysis of Various Feature Extraction Methods on IoT 2023, Procedia Computer Science (2024) Elsevier. 2. Dina Fawzy, Sherin M. Moussa, Nagwa L. Badr, An IoT-based resource utilization framework using data fusion for smart environments, Internet of Things, (2023) Elsevier. |
|
| Lab equipment | ||
| Virtual lab | ||
| MOOC course | http://edu.iot-open.eu/course/view.php?id=8 | |
