Presents a big picture view of Data Science for IoT techniques. #Deeplearning, #Sensorfusion, #Streaming, #recurrent #neuralnetworks, #R, #Python, #Spark , #Scala, #kdnuggets, #dscentral, #nvidia, #opensource, #flink, #predictive #algorithms, IoT #datasets Interested ? Email info@futuretext.com for details of the September batch (now in it’s fourth batch)

1. DEEP LEARNING
IOT principles
1) Data Science for IoT is based on mostly time series data from IoT devices – but with three additional
techniques: Deep learning, Sensor fusion and Streaming.
2) We consider Deep learning because we treat cameras as sensors but also include reinforcement neural
networks for IoT devices
3) 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)
4) The ideas learnt in the core modules are implemented in Projects. Projects could last as long as six months
5) Much of our work has been published in leading blogs like KDnuggets and Data Science Central etc
6) The course has evolved based on active participation 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
7) We use Apache Spark for Streaming and Apache flink for sensor fusion.
8) Ironically, due to the emphasis on Data, the course is strictly not an IoT course ie we are concerned only with
applying predictive learning algorithms on IoT datasets
Interested ? Email info@futuretext.com for details of the September batch (now in it’s fourth batch)
PROJECTSSENSOR FUSION
STREAMING
CODE IMPLEMENTATIONS
(R, SPARK, PYTHON)
AND METHODOLOGY IE PROBLEM
SOLVING FOR IOT ANALYTICS
Statistics
foundations
Time Series
Spark
Data Science
Principles
NOQL
databases