AID403 IoT Fundamentals

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
AID403 IoT Fundamentals 3 2 6
Prerequisite CS103 It is a prerequisite to


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Course Objectives 1. To examine the components and relationships within the Internet of Things (IoT), construct sensor and actuator systems using the Arduino microcontroller, and develop Python programs that enable IoT capabilities for the Raspberry Pi single-board computer.
2. To create an IoT system that can address challenges in manufacturing, healthcare, or energy systems.
3. To visualize data sourced from IoT sensors and analyze data for crucial insights, a proficiency that is appreciated by employers.
Textbook Cisco Academy Networking Program IoT Fundamentals: Connecting Things, Big Data & Analytics.
Additional Literature
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. Explain and understand the concepts, opportunities, and challenges of digital transformation using IoT.
  2. Build an IoT Systems solution.
  3. Demonstrate an understanding of the role and principles of Big Data and Analytics in IoT systems.
  4. Describe the different steps of the data analysis lifecycle and put this knowledge into practice in four different labs using the data analysis tools RapidMiner and RStudio.
Teaching Methods This course employs a range of teaching and learning methods such as Lectures with presentations and notes, in-class examples and discussions, interactive Activities, and video demonstrations. Practical skills and hands-on experience tasks for engaged learning and continuous feedback on progress.
Teaching Method Delivery Teaching Method Delivery Notes
Week 1 Course Introduction
Week 2 Things and connections
Week 3 Sensors, Actuators, and Microcontrollers
Week 4 Software is everywhere
Week 5 Networks, Fog and Cloud Computing
Week 6 Digitization of the Business | IoT applications in Business
Week 7 Creating an IoT Solution
Week 8 Midterm Exam
Week 9 Data and the Internet of Things
Week 10 Fundamentals of Data Analysis
Week 11 Data Analysis
Week 12 Advanced Data Analytics and Machine Learning
Week 13 Storytelling with Data
Week 14 Architecture for Big Data and Data Engineering
Week 15
Assessment Methods and Criteria Evaluation Tool Quantity Weight Alignment with LOs
Final Exam 1 30
Semester Evaluation Components
Midterm 1 20
Quizzes 2 10
Class participation 1 10
Hands on Labs 10 30
***     ECTS Credit Calculation     ***
 Activity Hours Weeks Student Workload Hours Activity Hours Weeks Student Workload Hours
Lecture hours 3 14 42 Hands on Labs 2 14 28
Home study 3 14 42 Midterm Exam Study 5 5 25
Final exam study 13 1 13
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 01/09/2023
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