Over the last year and a half, I have been teaching and evolving the concept of Data Science for Internet of Things
Here is how our current course outline looks like(and the rationale behind the approach)
comments welcome
If you are interested in being a part of the next batch, please contact me at [email protected]
Data Science for Internet of Things is based on time series data from IoT devices – but with three additional techniques: Deep learning, Sensor fusion (Complex Event Processing) and Streaming.
We consider Deep learning because we treat cameras as sensors but also include reinforcement neural networks for IoT devices
The course is based on templates(code) for the above in R, Python and Spark(Scala). It is hence suited for people with a Programming background(even if from other languages)
The ideas learnt in the core modules are implemented in Projects. Projects could last as long as six months
The diagram is representative of the course (not an application per se). It shows the core modules(ex time series etc). The advanced modules(ex Sensor fusion) are built on these
Much of our work has been published in leading blogs like KDnuggets and Data Science Central etc
The course has evolved based on active contribution from participants: ex Jean Jacques Barnard(methodology), Peter Marriot(Python), Sibanjan Das(H2O/Deep learning), Shiva Soleimani(methodology), Yongkang Gao(Nvidia TK1), Raj Chandrasekaran(Spark) , Vinay Mendiratta(systems level optimization of IoT sensors). We plan to open source most of our code
We use Apache Spark for Streaming and Apache flink for sensor fusion.
Ironically, due to the emphasis on Data, the course is strictly not an IoT course ie we are concerned primarily with applying predictive learning algorithms on IoT datasets
Finally, the course is personalized. I see it more as coaching than a course. – for example you can choose to focus on a smaller subset of topics which is decided in the personal learning plan at the outset
Interested ? Email [email protected] for details of the September batch (now in it’s fourth batch)
