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#News(IoTStack) [ via IoTForIndiaGroup ]
[Guest blog by Ajit Jaokar. Ajit”s work spans research, entrepreneurship and academia relating to IoT, predictive analytics and Mobility. His current research focus is on applying data science algorithms to IoT applications. This includes Time series, sensor fusion and deep learning (mostly in R/Apache Spark). This research underpins his teaching at Oxford University (Data Science for IoT) and ‘City sciences’ program at UPM (Madrid) ]
Data Science for IoT has similarities but also some significant differences. Here are 10 differences between Data Science for IoT and traditional Data Science.
Working with the Hardware and the radio layers
Edge processing
Specific analytics models used in IoT verticals
Deep learning for IoT
Pre-processing for IoT
The role of Sensor fusion in IoT
Real Time processing and IoT
Privacy, Insurance, and Blockchain for IoT
AI: Machines teaching each other(cloud robotics)
IoT and AI layer for the Enterprise
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