Data Science for Internet of Things course is a niche, world leading education community.

We aspire to transform your career through training in Big Data and Data Science with an emphasis on the Internet of Things.

We invite you to participate in the program – either online or at our venue in London.

In a nutshell,

The role of a Data Scientist is one of the hottest new skills.

If you are a Programmer (using Java, Oracle, Web technologies, Scala, Ruby etc) – you can leapfrog your career by upskilling to Data Science with an emphasis on IoT

The program is loosely based on a course conducted by Ajit Jaokar for Companies at Oxford University and UPM (Technical University of Madrid). 

Designed for individual developers and ICT contractors as an independent, personalized program (i.e. not associated with a University), the program allows you to upskill in the Big Data ecosystem (Hadoop) and Data Science with an emphasis on IoT.

Data Science for Internet of Things course is personalized to each participant (see below) based on the individual Personal Learning Plan

Starting from April/May 2015, we launch in small batches – both online and offline.

The program leads to an industry led certification (as opposed to an academic certification) for Data Science with an emphasis on IoT.


The Personal learning plan

The personal learning plan enables the program to be personalized to you in the following ways:

  • Each participant chooses a tailored path
  • Where possible, the syllabus co-relates to your existing skills(ex Java, Web, Oracle, Ruby etc)
  • You choose the platform(s) you wish to specialize in (ex MapR, Azure, MongoDB etc)
  • You can customize the project you wish to work with
  • The program is limited to very small numbers
  • The London sessions are conducted face to face
  • For the London sessions, we also offer (if included in the Personal learning plan) help with career transition

Building your own brand in the industry

We aspire to create an intellectually elite community with a world class excellence for IoT and Data Science.
The learning community also helps you build your own brand in the industry. The Personal Learning Plan includes the following options:

  • Contribute a chapter to a book for Data Science for IoT. This helps build your brand and distinguish from others. Support will be provided for the actual writing of the chapter
  • Leveraging your vertical (domain knowledge) – There are various ways in which you can use your existing domain knowledge for IoT. For example – Wearables are expected to be significant in Banking, Beacons are changing the future of shopping etc.  We choose projects which build upon your existing skillsets
  • The fablab London location gives us access to companies, networks and skills
  • Our advisor network helps to get contacts and jobs on your behalf. Advisors include Jeremy Geelan, Tony Fish, Lee Omar, Thomas Power, Azeem Azhar and others
  • We are creating and contribution to Open source software (in partnership with Open source foundations)
  • We are collaborating with other experts globally which helps us to leverage technologies fast


Many courses start with the Algorithms. This has two disadvantages

  • Algorithms and Maths are often unfamiliar and hard
  • In reality, most people are more familiar with Programming – and the platform (ex Python, Azure, MapR etc) offers a softer way to get started through libraries which encapsulate the Algorithms (for example scikit learn for Python)

In contrast, the focus on IoT allows us to take a ‘problem solving first approach’
We first identify many complex use cases in each IoT vertical (Wearables, Healthcare, Manufacturing, Retail, Supply chain, Smart energy, Smart cities, Smart Home). We then work backwards to the programming and algorithms. In turn, algorithms allow us to learn from Data, build models and make predictions (machine learning). 

Thus, by taking a problem solving approach and working backwards – you are empowered to work across many verticals and technologies
The program has the following strands:

  • Use Cases
  • Code
  • Algorithms
  • Architecture
  • Maths
  • IoT
  • Projects 
  • Data
  • Real time and
  • Deep learning(because we include cameras and video as sensors).

And we also have Goals : 

  • Customization
  • Brand
  • Community.

These are reflected in the content and modules.

Content and Modules

The customized program allows you to tailor specific modules based on the content below. So, you may choose to do more (or less) from a specific module in your personalized plan within the overall framework.

Programming in Hadoop has evolved from Mapreduce to more complex frameworks  based on the HDFS. Now, Hadoop vendors also provide downloadable Hadoop environments/sandboxes  which cover more than core Hadoop (ex Spark for Real time). These implementations are more practical ways to acquire new skills. The program will thus make use of Hadoop foundations to teach Data Science using specific implementations.

Modules covered (note modules are evolving and may be subject to some change)

Module One

An overview of Data Science, What is Data Science? What problems can be solved using Data science - Extracting meaning from Data - Statistical processes behind Data - Techniques to acquire data (ex APIs) - Handling large scale data - Big Data fundamentals

Module Two

An exploration of the Hadoop ecosystem and it’s evolution including Hadoop Fundamentals - Introduction to the Hadoop ecosystem - SQL NoSQL

Module Three

Data Science and IoT, The IoT ecosystem, Unique considerations for the IoT ecosystem - Addressing IoT problems in Data science (time series data, enterprise IoT  edge computing,   real-time processing, cognitive computing, image processing,   introduction to deep learning algorithms, geospatial analysis for IoT/managing massive geographic scale, strategies for integration with hardware,  sensor fusion)

Module Four

IoT datasets, An exploration of IoT datasets and APIs by application: Healthcare, Manufacturing, wearables, Energy etc

Module Five

Developing for IoT devices - Covers the design and development of a typical IoT solution from the perspective of the flow of Data

Module Six

Mathematical foundations of Machine Learning algorithms: Linear Algebra including Matrix algebra, Bayesian Statistics, Optimization techniques (such as Gradient descent) etc

Module Seven

Machine Learning techniques and algorithms: Supervised algorithms, unsupervised algorithms (classification, regression, clustering, dimensionality reduction etc).

Module Eight

The Python ecosystem: In this phase, we use our Python based platform and a unique methodology. We use Python based tools and libraries like the iPython notebook, the Anaconda distribution, sci-kit learn, Pandas and Wakari

Module Nine

Co-relation to existing skills: In this phase, we use co-relate the knowledge to the existing    programming skills you already possess – ex Java etc to reinforce new concepts with familiar paradigms

Module Ten

Platforms and Projects
Here, you work with specific commercial platforms (ex Azure, MapR, MongoDB etc) and IoT related projects. We use the platforms like MapR to study the evolution of Hadoop as applicable to IoT (stream processing, in-memory processing, Spark, Storm and its variants beyond map reduce) and SQL.  In this phase, we work with projects based on the platforms with companies/start-ups based in the London area. 


The program leads to an industry led – project based certification.

There are two modes:

Sessions in London - for an investment of £1,200 + VAT
Online only - $600 USD + VAT where applicable

The content is the same and is always personalized to the participant based on the Personal learning plan

The additional advantages for London based sessions are

  • Face to face engagement with sessions at fablab London
  • Help with career transition with UK/EU companies (note we do not process work permits etc)

Both the online and offline programs will always have limited numbers. Due to the small group sizes and the personalized approach, spaces are very limited.

About the Tutor

Ajit’s work spans more than two decades in technology and strategy covering: Large Scale Data Warehousing; Mobility / IoT and Data Science.
Ajit conducts a course at Oxford University for Data Science for IoT. He also conducts a course at UPM(Technical University of Madrid) on Data Science for Smart cities.
He has been involved with various Mobile / Telecoms / IoT projects since 1999 ranging from strategic analysis, research, consultancy and project management. From 2011 onwards, he has further specialized in the predictive analytics aspect of IoT. Ajit’s current work is based on understanding the interplay between IoT and Machine Learning/Predictive analytics - for Telecoms and Smart cities. This research underpins his teaching at the City sciences program at the Technical University of Madrid (UPM) and his Telecoms courses at Oxford University Department of Continuing education.

In 2009, Ajit was nominated to the World Economic Forum’s ‘Future of the Internet’ council. In 2011, he was nominated to the World Smart Capital program (Amsterdam). Ajit moderates/chairs Oxford University’s Next generation mobile applications panel. In 2012, he was nominated to the board of Connected Liverpool for their Smart city vision. Ajit has been involved in IOT based roles for the webinos project (EU funded Fp7 project). Since May 2005, he has founded and run the OpenGardens blog which is widely respected in the mobile/telecoms industry. Ajit has spoken at MobileWorld Congress (4 times) ,CTIA, CEBIT, Web20 expo, European Parliament, Stanford University, MIT Sloan, Fraunhofer FOKUS;University of St. Gallen. He has been involved in transatlantic technology policy discussions – both in the European Parliament and at Capitol Hill. 

Venue and Logistics

Where applicable, sessions in London are held at starting from April 2015 on Saturdays. The schedule is based on the personal learning plan for each participant

Fablab London
1 Frederick’s Place
Off Old Jewry

All participants will work with an online learning platform which allows us to learn dynamically and to monitor progress through analytics. The platform uses a blend of multiple modalities combining passive learning (videos) with active learning (ex: flashcards). 

We take a learning community approach – allowing you an ongoing opportunity to stay up to date and get support. The offer allows you to be part of the learning community for two years at no extra cost.

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