Miami Young Data Scientists – Pleased to be the winning team in the 2015 Association of Space Engineers/Astrosat challenge










For the last two years, I have worked with teaching Computer Science for young people.

This venture has had its ups and downs.

But we have had the support of many who believed in the vision.

So, It was very nice to see this

We (Countdown Institute – i.e. now me and Richard Schuchts based in Miami ) submitted an entry in the ASE AstroSat Challenge (supported by Northrop Grumman Corporation). The Association of Space Explorers is the unique professional organization composed of astronauts who have orbited Earth. They have 375 members from 35 countries and are passionate about encouraging students to pursue science, technology, engineering, and math education, as well as careers in astronautics. The ASE AstroSat Challenge is designed to give students a taste of the exciting world of satellite operations. The ASE AstroSat Challenge is made possible with the generous support of the Northrop Grumman Corporation.

Only 15 teams were selected to run a Space experiment – And our team (Miami Young Data Scientists/Countdown Institute) were one of them

Its amazing to get here.

It means the team of ‘young data scientists’ from Miami will be able to run a Space Experiment live in Space and also learn Data Science

The winning entry was based on teaching Data Science to young people.

Specifically, using Regression algorithms to make predictions on Space data from Ardusat (more on this soon)

This is different from our original idea and is more complex .. but I think it would make a difference to get more young people into Data Science (as per Harvard – the hottest profession in future)

Thus, I think the biggest winners are the young people of Miami who are a part of the winning team.

The main variation/evolution from the original idea is to focus on Data Science and inspiring students to take up Data Science through visualization of data and predictions using scientific methodology.

Its a way to get more students(both boys and girls) interested in Data science using Space exploration by coding on a live satellite.

Hence, the regression algorithms/iPython notebooks etc.

Also a bit more math. and hence slightly for older students(aroundn 15 to 17). All this also aligns with my ‘day job’ so to speak!

Here is the full list of winners of the astrosat competition

I am happy to share more. If you want to know more about this – please email me at ajit.jaokar at

Free Download – Data Science for IoT course papers

Here is a set of papers from the Data Science for Internet of Things – practitioners course

These have been published by course participants in top Data Science blogs like KDnuggets and Data Science Central

The Zip file includes the following papers:

1) Recurrent neural networks, Time series data and IoT – Part One

2) Spark SQL for Real Time Analytics – Part One

3) Spark SQL for Real Time Analytics – Part Two

4) Time Series IoT applications in Railroads

5) Nov 13 update: Kalman filters 1 and 2

Free Download – Data Science for IoT course papers


Data Science for Internet of Things: Practitioners course – modules list










Course URL : Data Science for Internet of Things: Practitioners course

Note that the modules are customizable i.e. as per your personal learning plan – you may choose to do more or less of a specific topic. For example, more Deep Learning vs Sensor fusion. But overall, we will follow this plan.

Overall themes covered in the course

  • IoT
  • Data Science
  • Big Data
  • Machine Learning
  • Deep Learning
  • Sensor fusion
  • Use Cases (application domains) and IoT Datasets
  • Math foundation
  • Time Series
  • IoT stream processing
  • Apache Spark ecosystem
  • Programming (R, Scala, SQL)

Weekly schedule


Week 0 Orientation, introductions, Personal learning plans, Platform signup
Week 1 nov 16 Foundations:An analytics Driven Organization – IoT and Machine Learning  - Data Science for IoT – Unique characteristics – Data Science for IoT – why now?
Nov 23 Machine Learning conceptsDeep Learning concepts
Nov 30 An introduction to IoT (Internet of Things)
Dec 7 IoT platforms – From sensor to Cloud
Dec 14 Concepts of Big Data Part One
Dec 21 Concepts of Big Data Part Two
Jan 11 Market drivers for IoT
Jan 18 Choosing a model – what technique to Use?
Jan 25 Use Cases  and IoT datasets (these will continue throughout the course)
Feb 1 Time series and NoSQL databases
Feb 8 Streaming analytics part One
Feb 15 Streaming analytics part two
Feb 22 Deep learning part one
Feb 29 Deep learning part two
Mar 7 Machine learning algorithms – part one
Mar 14 Machine learning algorithms – part two
Mar 21 Mathematical foundations – part one
Mar 28 Mathematical foundations – part two





Week 0 Orientation, introductions, Personal learning plans, Platform signup
 Nov 16
Nov 23
Nov 30 Intro to R, Installations, Basics of R
Dec 7
Dec 14 Data Frames in R & Tabular Data
Dec 21
Jan 11 Data Processing & Data Visualization in R
Jan 18
Jan 25 Scala basics
Feb 1
Feb 8 Spark batch processing I
Feb 15
Feb 22 Spark Batch Processing II
Feb 29
Mar 7 Spark SQL
Mar 14
Mar 21 Spark Streaming
Mar 28


My forthcoming book – Data Science for IoT


Hello all

As many of you know, I have been working on the Data Science for IoT course for the last year or so (Both at Oxford Uni and for my consulting work)

As part of this work, I have been covering many complex areas like Sensor fusion/kalman filters(published on Kdnuggets)  and Deep learning (Recurrent neural networks) for IoT (published on Data Science central) 

Last week, I was approached by a program at Stanford University about this work

In a nutshell, the content of the Data Science for IoT course will be included as a recommended book for a forthcoming program taught at Stanford University

Its been a while since I have written a book ..

Excluding books for young people(teaching coding and computer science), the last major effort was with Tony Fish (Mobile Web 2.0) which launched my career into Mobile.

A book is a major undertaking

However, the existing course (Data Science for IoT) and the collaboration with Stanford University program for the book gives me the opportunity to create the book iteratively (in sections as we teach)

The book will have co-authors (more on that soon) and also many contributors from the Data Science for IoT course

This enables me to keep the content very fresh – which is critical in such a rapidly evolving field

We have had a great response to the Data Science for IoT course from all over the world.

Most of the participants are from USA and UK – but we also have participants from as far as Australia and Nicaragua.

If you are interested in being part of the course – please sign up now (we start next week)See course Data Science for IoT course

Happy to discuss part payments if you want.

We have had some excellent participants already and I look forward to learning and sharing more insights as part of the book and the course

kind rgds