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| en:iot-reloaded:curriculum:data [2024/03/25 09:22] – pczekalski | en:iot-reloaded:curriculum:data [2024/12/10 17:20] (current) – pczekalski |
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| ====== Module: Data Analytics ====== | ====== Module: IoT Data Analysis (M3) ====== |
| <todo @Agris> Review Data Analytics</todo> | |
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| | **Study level** | Master || | | **Study level** | Master || |
| | **ECTS credits** | 3 || | | **ECTS credits** | 3 || |
| | **Study forms** | Hybrid or fully online || | | **Study forms** | Hybrid or fully online || |
| | **Course 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 data in IoT systems. || | | **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 [[en:iot-reloaded:curriculum:general|"Introduction to IoT"]]) || | | **Pre-requirements** | Has some understanding of IoT (passed module [[en:iot-reloaded:curriculum:general|"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 || | | **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** | __[[en:iot-reloaded:iot_data_analysis|IoT Data Analysis]]__\\ [[en:iot-reloaded:data_products_development|Data products development]]\\ [[en:iot-reloaded:data_preparation_for_data_analysis|Data preparation for data analysis]]\\ [[en:iot-reloaded:regression_models|Regression models]]\\ [[en:iot-reloaded:clustering_models|Clustering models]]\\ [[en:iot-reloaded:classification_models|Classification models]]\\ [[en:iot-reloaded:introduction_to_time_series_analysis|Introduction to time series analysis]]\\ [[en:iot-reloaded:hints_for_further_readings_on_ai|Hints for further readings on AI]] || | | **Topics** | __[[en:iot-reloaded:iot_data_analysis|IoT Data Analysis]]__\\ [[en:iot-reloaded:data_products_development|Data products development]]\\ [[en:iot-reloaded:data_preparation_for_data_analysis|Data preparation for data analysis]]\\ [[en:iot-reloaded:regression_models|Regression models]]\\ [[en:iot-reloaded:clustering_models|Clustering models]]\\ [[en:iot-reloaded:classification_models|Classification models]]\\ [[en:iot-reloaded:introduction_to_time_series_analysis|Introduction to time series analysis]]\\ [[en:iot-reloaded:hints_for_further_readings_on_ai|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 || | | **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** | [[https://multiasm.eu/mooc/course/view.php?id=8]] || | | **Blended learning** | Along with MOOC course in hybrid mode. || |
| | **References to\\ literature** | 1. IOT-OPEN.EU, [[https://iot-open.eu/iot-coursebook/| Introduction to the IoT]], 2019\\ 2. Book 2 || | | **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** | || | | **Lab equipment** | || |
| | **Virtual lab** | || | | **Virtual lab** | || |
| | **MOOC course** | [[https://multiasm.eu/mooc/course/view.php?id=8]] || | | **MOOC course** | http://edu.iot-open.eu/course/view.php?id=8 || |
