Data Science for the Internet of Things
About This Course
This module covers data analytics for the Internet of
Things. It starts with an introduction to the Internet of
Things (IoT) systems, including the enabling technologies,
IoT network architectures and protocols. IoT systems
have applications such as semiconductor manufacturing,
smart power grids, and healthcare. The module then
covers data science fundamentals such as Bayesian
statistics, classification, supervised learning, unsupervised
learning, and deep learning. The module also covers
basic machine learning algorithms such as decision trees,
logistic regression, support vector machines, and neural
networks. Students will visualize and analyze real-world
data sets via practical IoT case studies.
What You'll Learn
Things. It starts with an introduction to the Internet of
Things (IoT) systems, including the enabling technologies,
IoT network architectures and protocols. IoT systems
have applications such as semiconductor manufacturing,
smart power grids, and healthcare. The module then
covers data science fundamentals such as Bayesian
statistics, classification, supervised learning, unsupervised
learning, and deep learning. The module also covers
basic machine learning algorithms such as decision trees,
logistic regression, support vector machines, and neural
networks. Students will visualize and analyze real-world
data sets via practical IoT case studies.
Entry Requirements
Polytechnic diploma related to Electrical/Electronics Engineering or equivalent