Data Science for Internet of Things: A coaching approach

In the Data Science for Internet of Things course I take a coaching approach. I have alluded to this in the post about foundation projects  and construtivism.

Coaching has a questionable reputation – with some justification

But here, we are talking of high performance coaching strategies

For example: Consider the approach of a book like The talent code 

The author explores the world’s greatest talent hotbeds: tiny places that produce huge amounts of talent ex a small gym in Moscow that produces a large number of gold medalists in athletics. He found that there’s a pattern common to all of them: methods of training, motivation, and coaching. They also place and emphasis on hard skills

So, what does this mean for participants in context of foundation projects?

a)      Start with what you know(ideally)

b)      Work collaboratively

c)       Push your limits(you can choose something different)

d)      Each group for a project will have one person/s who is knowledgeable

e)      Your outcomes should be specific

f)       You can see the big picture through the methodology for problem solving with Data Science for Internet of Things

g)      Your contribution should be measurable

h)      Your contribution should be based on acquiring a specific skill

i)        foundation projects have a quiz

From my perspective – as tutor / coach

  • I need to understand what the participants already know (baseline)
  • Provide measurable feedback
  • Extend your capabilities/push limits
  • Ensure you acquire definite skills
  • Keep you motivated
  • Keep your learning at the right pace
  • Foster a sense of community
  • Provide alternative mentors in the community
  • Use newer methods of learning ex concept maps
  • Create great conversations
  • Allow room for unplanned expansion

I think these techniques applied online are new – and there is so much to learn for all.

If you are interested in the Data Science for Internet of Things course, please email us at info at

Data Science for IoT – role of foundation projects(constructivist learning)


In the Data Science for Internet of Things course, I use some elements of constructivism through the use of foundation projects.

Foundation projects allow the participant to choose a learning context which is most familiar to them based on their  existing experience
Foundation projects are different from the Capstone projects for each participant
This form of context based learning is not familiar to most people hence some notes
1) Context based learning is based loosely on constructivism .
A concise description –  Constructivism is pedagogy / learning theory which advocates that people construct their own understanding and knowledge of the world, through experiencing things and reflecting on those experiences. The teacher makes sure she understands the students’ preexisting conceptions, and guides the activity to address them and then build on them.
adapted from source :
“The most important single factor influencing learning is what the learner already knows. Ascertain this and teach him accordingly”
Quote by Asubel one of the pioneers of this education:
In Holland and Germany, this form of education in Science is called by various names ex concept context learning (pdf)
What it means for learning in the Data Science for IoT course:
1)  we follow two modes of learning in parallel - instructivist (via the video based modules) and constructivist (via the foundation projects)
2) for the foundation projects, the participants choose a context most familiar to them from your prior experience. (ex healthcare, renewables, Industrial IoT etc)
The downside of applying constructivist methods to learning is .. they take a relatively long time – hence the longer duration of the course
for the current batch, the foundation projects are:
The foundation projects and project leaders are
Wearables: led by Quang Nam Tran (London)

Renewables:led by vaijayanti vadiraj(Bangalore)

Python for Data Science - temporarily led by me

Big Data: Spark and Cassandra for IoT - temporarily led by me – looking to handover and Trenton Potgieter (Austin)
Deep Learning with Nvidia: led by Jean Jacques Bernard(Paris) and Yongkang Gao(UK)
Data visualization with R: Barend Botha(London)
Predix: Industrial IoT – temporarily led by me – looking to hand over
ETL/Pentaho -
Deep learning and Machine learning with H2O led by Sibanjan Das(Bangalore)
Remote monitoring of elderly/patient care / healthcare - Manuel Betancurt(Sydney)

More details about the course:  Data Science for Internet of Things course

Image: Jean Piaget – the founder of Constructivism

I am listed no 19 among top 50 authorities on twitter for #iot

nice to be listed here amongst some great company

top 50 authorities on twitter for #iot

Young Data Scientist: Data visualizations of our Ardusat/ASE Space experiment using Python

These are visualizations from the live data from our satellite experiment with Ardusat ASE challenge which we won last year.

Will be part of a book called Young Data Scientist

Created using Python libraries json, pandas, matplotlib, statsmodels, numpy.

We use linear regression and logistic regression to detect cloud presence. will be released as part of the Young Data Scientist book (Countdown Institute)

I even got a mapping of the route of the satellite (equatorial orbit) For some background see

Using Space Exploration to teach Young People about Data Science

  Please email me at ajit.jaokar at with subject Young Data Scientist if you want to know more as we launch the book/initiative