Content and approach for a Data Science for IoT course/certification

 

 

 

 

 

 

UPDATE: 

Feb 15:  Applications now open -  Data Science for IoT Professional development short course at Oxford University  - more coming soon. Any questions, please email me at ajit.jaokar at futuretext.com

We are pleased to announce support from Mapr, Sigfox, Hypercat and Red Ninja for the Data Science. Everyone finishing the course will receive a University of Oxford certificate showing that they have completed the course. Places are limited – so please apply soon if interested

In a previous post, I mentioned that I am exploring creating a course/certification for Data Science for IoT

Here are some more thoughts

I believe that this is the first attempt to create such a course/program

I use the the phrase “Data Science” to collectively mean Machine learning/Predictive analytics

There are ofcourse many Machine Learning courses – the most well known being Andrew Ng’s course at Coursera/Stanford and the domain is complex enough as it is.

Thus, creating a course/ certification covering both Machine Learning/Predictive analytics and also IoT can be daunting

However, the sector specific focus gives us some unique advantages

Already at UPM (Universidad Politechnica de Madrid) I teach Machine Learning/Predictive analytics for the Smart cities domain through their citysciences program (the remit there being to create a role for the Data Scientist for a Smart city)

So, this idea is not totally new for me ..

Based on my work at UPM (for Smart cites) – teaching DataScience for a specific domain (like IoT) has both challenges but also some unique advantages

The challenges are: You have an extra level of complexity to deal with (in teaching IoT alongwith Predictive analytics)

But the advantages are:

a) The IoT domain focus allows us to be more pragmatic by addressing unique Data Science problems for IoT

b) We can take a Context based learning approach - a technique more common in Holland and Germany for teaching Engineering disciplines – and which I have used in teaching computer science to kids at feynlabs

c)  We don’t need to cover the maths upfront

d)  The participant can be productive faster and apply ideas faster to industry

Here are my thoughts on the elements such a program could cover based on the above approach: 

1) Unique characteristics – IoT ecosystem and data

2) Problems and datasets. This would cover specific scenarios and datasets needed (without addressing the predictive aspects)

3) An overview of Machine learning techniques and algorithms (Classification, Regression, Clustering, Dimensionality reduction etc) – this would also include the basic Math techniques needed for understanding algorithms

4) Programming python scikit-learn

5) Specific platforms/case studies

 Time series data(Mapr)

Sensor fusion for IoT(Camgian – Egburt)

NoSQL data for IoT (ex mongodb for IoT) ,

managing very high volume IoT data Mapr loading time series database 100 million points second

I also include image processing with sensors / IoT(ex surveillance cameras)

Hence,

IBM – Detecting skin cancer more quickly with visual machine learning

Real time face recognition using Deep learning algorithms

and even – Combining the Internet of Things with deep learning / predictive algorithms @numenta 

To conclude:

The above approach for teaching a course on Data Science for IoT  would help focus Machine Learning / Predictive algorithms in a real life problem solving scenario for IoT

Comments welcome.

You can sign up for more information at  futuretext and also follow me on twitter @ajitjaokar

Image source: wired