Syllabus - Data Analytics in IOT (IO 601)

Introducing IoT Analytics

Types of Data Sources, Understanding Big Data, Applications of Big Data Analytics, IoT Data and Big Data, Defining IoT Analytics, Challenges of IoT Analytics, IoT Analytics Lifecycle and Techniques

IoT, Cloud and Big Data Integration for IoT Analytics

Cloud-based IoT Platform, Requirements of IoT Big Data Analytics Platform, Functional Architecture, Data Analytics for the IoT, Characteristics of IoT Generated Data, Data Analytic Techniques and Technologies, Data Collection Using Low-power, Long-range Radios, IoT Analytics for the cloud

Searching the Internet of Things

A Search Architecture for Social and Physical Sensors, Local Event Retrieval, Using Sensor Metadata Streams to Identify Topics of Local Events in the City, Venue Recommendation, Development Tools for IoT Analytics Applications; IoT Analytics as a Service: Architecture for IoT Analyticsas-a-Service, Sensing-as-a-Service, Scheduling, Metering and Service Delivery, Sensing-as-a-Service Example, From Sensing-as-a-Service to IoT-Analytics-as-a-Service

Data Science for IoT Analytics

Machine Learning, Feature Engineering with IoT Data, Validation Methods, Bias, Variance, Comparing different Models to find the Best fit, Anomaly Detection, Forecasting, Deep Learning with IoT data; Strategies to organize data for Analytics; The Economics of IoT Analytics: Cost Considerations for IoT Analytics

IoT Analytics Applications and Case Studies

Data Analytics in Smart Buildings, Internet-of-Things Analytics for Smart Cities, Ethical IoT