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
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
en/iot-reloaded/curriculum/data.txt · Last modified: 2024/06/10 15:37 by pczekalski
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