Boston housing Dataset without the racial profiling attribute

Like many data scientists, I use the UCI datasets extensively

Specifically, the Boston Housing Dataset is useful to teach Data Science

For example, I use it in the Data Science for IoT course because its a dataset which people can relate to easily(finding median value of house prices)

The attributes are

    1. CRIM      per capita crime rate by town
    2. ZN        proportion of residential land zoned for lots over 
                 25,000 sq.ft.
    3. INDUS     proportion of non-retail business acres per town
    4. CHAS      Charles River dummy variable (= 1 if tract bounds 
                 river; 0 otherwise)
    5. NOX       nitric oxides concentration (parts per 10 million)
    6. RM        average number of rooms per dwelling
    7. AGE       proportion of owner-occupied units built prior to 1940
    8. DIS       weighted distances to five Boston employment centres
    9. RAD       index of accessibility to radial highways
    10. TAX      full-value property-tax rate per $10,000
    11. PTRATIO  pupil-teacher ratio by town
    12. B        1000(Bk - 0.63)^2 where Bk is the proportion of blacks 
                 by town
    13. LSTAT    % lower status of the population
    14. MEDV     Median value of owner-occupied homes in $1000's
However, there is a problem with this dataset especially with this attribute
12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town

Hence, I use a modified version of the dataset which you can find as a CSV HERE

It removes the above attribute and it does not make any difference to the dataset

You can then upload into a dataframe using the following code and changing to your directory path

# Read the data from the csv file
Boston = read.csv(“c:\\futuretext\\Boston.csv”)

 

 

Data Science for Internet of Things course – Aug – Sep 2016 – now in its fourth batch

Course Outline

In a nutshell

Now in its fourth batch, the Data Science for Internet of Things course is designed to prepare you for the role of a Data Scientist for the Internet of Things(IoT) domain.

 

The course starts in Aug – Sep  2016  

Please contact info@futuretext.com 

 

This niche, personalized course is suited for:

  • Developers who want to transition to a new role as Data Scientists
  • Entrepreneurs who want to launch new products covering IoT and analytics
  • Anyone interested in developing their career in IoT Analytics from a strategic perspective by choosing the Strategic/non programming option

 

Duration: The course starts from Sep 2016 and extends to Feb  2017. We work with you for the next six months after that on a specific project and to help transition your career to Data Science through our network. The extra time also allows you to catch up on specific modules in the course

 

Scope: Created by Data Science and IoT professionals, the course covers infrastructure (Hadoop – Spark), Programming / Modelling (Python/R/Time series) and Deep Learning (Theano, H2O) within the context of the Internet of Things.

Internet of Things: We cover unique aspects of Data Science for IoT including Deep Learning, Complex event processing/sensor fusion and Streaming/Real time analytics

 

Investment: The course is conducted Online and Offline(london). Please contact us for pricing (info@futuretext.com)

 

Contact  us at info@futuretext.com to signup

 

Top Ten Reasons to join this course

Here are the top 10 reasons to join the course:

1)      Work towards the goal for being a Data Scientist for the Internet of Things

2)      Personalization of content

3)      Flexibility(chances to catch up if needed)

4)      Coaching approach

5)      Affordable

6)      Covers complex elements of IoT such as Deep learning, Sensor fusion, Streaming

7)      Based on an Open methodology and Open source

8)      External publishing and personal branding

9)      Project based

10)  Being part of a global community

 

Benefits

The course aims to equip you to be a Data Scientist for the Internet of Things domain

  • You can transition your career to Data Science for IoT. This could mean a new job, new role in your existing company, a  project or a start-up idea
  • You are not alone: Toolkits and community support to start working on real Data science problems for IoT
  • You master specific skills: Spark, R, Python, Scala, IoT platforms, Data analysis, Deep Learning and SQL among others in context of IoT analytics
  • The course content can be personalized (see below)
  • While we focus on IoT only, the Data Science principles can apply to other domains

 

Testimonials

 

Jean Jacques Bernand – Paris – France

“Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful.

As for myself, it allowed me to go into topics of interests that help me in reshaping my career.”

Johnny Johnson, AT&T – USA

“This DSIOT course is a great way to get up-to-speed.  The tools and methodologies for managing devices, wrangling and fusing data, and being able to explain it are taking form fast; Ajit Jaokar is a good fit.  For me, his patience and vision keep this busy corporate family man coming back.”

Yongkang Gao, General Electric, UK.
“I especially thank Ajit for his help on my personal project of the course — recommending proper tools and introducing mentors to me, which significantly reduced my pain in the beginning stage.”
karthik padmanabhan Manager – Global Data Insight and Analytics (GDIA) – Ford Motor Pvt Ltd.

“I am delighted to provide this testimonial to Ajit Jaokar who has extended outstanding support and guidance as my mentor during the entire program on Data science for IoT. Ajit is a world renowned professional in the niche area of applying the Data science principles in creating IoT apps. Talking about the program, it has a lot of breadth and depth covering some of the cutting edge topics in the industry such as Sensor Fusion, Deep Learning oriented towards the Internet of things domain. The topics such as Statistics, Machine Learning, IoT Platforms, Big Data and more speak about the complexity of the program. This is the first of its kind program in the world to provide Data Science training especially on the IoT domain and I feel fortunate to be part of the batch comprising of participants from different countries and skill sets. Overall this journey has transformed me into a mature and confident professional in this new space and I am grateful to Ajit and his team. My wish is to see this program accepted as a gold standard in the industry in the coming years”.

Peter Marriott – UK – www.catalystcomputing.co.uk

Attending the Data Science for IoT course has really helped me in demystifying the tools and practices behind machine learning and has allowed me to move from an awareness of machine learning to practical application.

Yair Meidan Israel – https://il.linkedin.com/in/yairmeidandatamining

“As a PhD student with an academic and practical experience in analytics, the DSIOT course is the perfect means by which I extend my expertise to the domain of IoT. It gradually elaborates on IoT concepts in general, and IoT analytics in particular. I recommend it to any person interested in entering that field. Thanks Ajit!”

Parinya Hiranpanthaporn, Data Architect and Advanced Analytics professional Bangkok

“Good content, Good instructor and Good networking. This course totally answers what I should know about Data Science for Internet of Things.”

 

Sibanjan Das – Bangalore

Ajit helped me to focus and set goals for my career that is extremely valuable. He stands by my side for every initiative I take and helps me to navigate me through every difficult situation I face. A true leader, a technology specialist, good friend and a great mentor. Cheers!!!

Manuel Betancurt – Mobile developer / Electronic Engineer. – Australia

I have had the opportunity to partake in the Data Science for the IoT course taught by Ajit Jaokar. He have crafted a collection of instructional videos, code samples, projects and social interaction with him and other students of this deep knowledge.

Ajit gives an awesome introduction and description of all the tools of the trade for a data scientist getting into the IoT. Even when I really come from a software engineering background, I have found the course totally accessible and useful. The support given by Ajit to make my IoT product a data science driven reality has been invaluable. Providing direction on how to achieve my data analysis goals and even helping me to publish the results of my investigation.

The knowledge demonstrated on this course in a mathematical and computer science level has been truly exciting and encouraging. This course was the key for me to connect the little data to the big data.

Barend Botha – London and South Africa – http://www.sevensymbols.co.uk

This is a great course for anyone wanting to move from a development background into Data Science with specific focus on IoT. The course is unique in that it allows you to learn the theory, skills and technologies required while working on solving a specific problem of your choice, one that plays to your past strengths and interests. From my experience care is taken to give participants one to one guidance in their projects, and there is also within the course the opportunity to network and share interesting content and ideas in this growing field. Highly recommended!

- Barend Botha

Jamie Weisbrod – San Diego - https://www.linkedin.com/in/jamie-weisbrod-3630053

Currently there is a plethora of online courses and degrees available in data science/big data. What attracted me to joining the futuretext class “Data Science for ioT” is Ajit Jaokar. My main concern in choosing a course was how to leverage skills that I already possessed as a computer engineer. Ajit took the time to discuss how I could personalize the course for my interests.
I am currently in the midst of the basic coursework but already I have been able to network with students all over the world who are working on interesting projects. Ajit inspires a lot of people at all ages as he is also teaching young people Data science using space exploration.

 Robert Westwood – UK – Catalyst computing
“Ajit brings to the course years of experience in the industry and a great breadth of knowledge of the companies, people and research in the Data Science/IoT arena.”

Big Picture

Below is a big picture to our approach.

  • Data Science for Internet of Things is based on time series data from IoT devices – but with three additional techniques: Deep learning, Sensor fusion (Complex Event Processing) and Streaming.
  • We consider Deep learning because we treat cameras as sensors but also include reinforcement neural networks for IoT devices
  • The course is based on templates (code) for the above in R, Python and Spark (Scala, Pyspark,SQL). It is hence suited for people with a Programming background(even if from other languages). The exception is if you choose to do a Strategic option(non programming certification – see below)
  • The ideas learnt in the core modules are implemented in Projects. Projects could last as long as six months
  • The advanced modules (ex Sensor fusion) are built on top of the core modules(ex Time series etc)
  • Much of our work has been published in leading blogs like KDnuggets and Data Science Central etc
  • The course has evolved based on active contribution from existing 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
  • We use Apache Spark for Streaming and Apache flink for sensor fusion.
  • Ironically, due to the emphasis on Data, the course is strictly not an IoT course ie we are concerned primarily with applying predictive learning algorithms on IoT datasets
  • Finally, the course is personalized. I see it more as coaching than a course. – for example you can choose to focus on a smaller subset of topics which is decided in the personal learning plan at the outset

 

Interested ? Email info@futuretext.com for details of the September batch (now in it’s fourth batch)

 

Coaching ..

The course takes a coaching approach i.e. the relatively small numbers allow us to customize the content and take a personalized approach through one on one calls/hangouts etc

Timeline and Modules

Notes:

  • The modules and the sequence are subject to change)
  • You can start in Sep if you are away on holiday in August (because the first two weeks are personalization and onboarding). However, if you can start in August, we recommend you do so

 

 

Aug 15 Personal learning plans and onboarding
Aug 22
Aug 29
Sep 5 Data Science for IoT core concepts
Sep 12 Data Science for IoT core concepts Quiz and hangout
Sep 19 IoT Platforms
Sep 26 IoT Platforms Quiz and hangout
Oct 3 Fundamentals of R Programming
Oct 10 Fundamentals of R Programming Quiz and hangout
Oct 17 Problem solving with Data science Part One
Oct 24 Problem solving with Data science Part One – Quiz and hangout
Oct 31 Problem solving with Data science Part Two
Nov 7 Problem solving with Data science Part Two – Quiz and hangout
Nov 14 Time series and NoSQL databases
Nov 21 Time series and NoSQL databases – Quiz and hangout
Nov 28 Spark ecosystem – part one
Dec 5 Spark ecosystem – part one – Quiz and hangout
Dec 12 Spark ecosystem – part two
Dec 19 Spark ecosystem – part two – Quiz and hangout
Jan 9 2017 Deep learning
Jan 16 2017 Deep learning – Quiz and hangout
Feb 2017 to June 2017 Project and optional modules

 

Optional modules: Choose 2 modules from

1. Spark and scala with an emphasis on distributed algorithms for IoT use cases(

2. Deep learning – based on H20

3. Sensor fusion

4. Python (Python covers all aspects of Python including Deep learning via Theano)

 

Module and Content notes

The content includes

 

Mathematical and statistical techniques (covered throughout the course)- Stats, how to solve a problem, where algorithms fit in, motivational examples, how to choose an algorithm, end to end steps etc) – may have multiple parts and will broadly include

Hypothesis testing – Descriptive statistics – Data loading – Split-out validation dataset – Basic Data visualizations – Prepare Data – Data Cleaning – Feature Selection – Data Transforms – Test and Evaluate Algorithms – Improve Accuracy

(Algorithm Tuning, Ensembles etc) – Time series – mulivariate regression

 

Methodology - How to solve an IoT analytics problem

 

Algorithms

Regression

Clustering

multivariate

Time series (air quality and temperature)

Big Data: Spark and Scala(optional)

Deep learning: H20(optional)

 

An overview of Data Science

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

 

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)

 

The Apache Spark ecosystem

Apache spark in detail including Scala, SQL, SparkR, Mlib and GraphX

 

The Data Science for IoT methodology

A specific approach to solve Data Science problems for IoT including strategy and development

 

Mathematical foundations of Machine learning

Here we formally cover the mathematics for Data science including Linear Algebra, Matrix algebra, Bayesian Statistics, Optimization techniques (Gradient descent) etc. We also cover Supervised algorithms, unsupervised algorithms (classification, regression, clustering, dimensionality reduction etc) as applicable to IoT datasets – covered throughout the course as needed

 

Unique Elements for IoT

This module emphasises the following unique elements for IoT

  • Complex event processing (sensor fusion)
  • Deep Learning and
  • Real Time (Spark, Kafka etc)

FAQ: Summary of Benefits and Features

 

Impact on your work Designed for developers/ICT contractors/Entrepreneurs who want to transition their career towards Data science roles with an emphasis on IoT
Typical profile A developer who has skills in programming environments like Java, Ruby, Python, Oracle etc and wants to learn Data Science within the context of Internet of Things with the goal of becoming a Data Scientist for IoT. You may also choose a strategic option (i.e. excluding  Programming)
Community support? Yes. Also includes the Alumni network i.e. beyond the duration of the course at no extra cost.
Approach to Big Data For Big Data, the course is focussed on Apache Spark – specifically Scala, SQL, mlib. Graphx and others on HDFS
Approach to Programming see scope below
Approach to Algorithms see scope below
Is this a full data science course? Yes, we cover machine learning / Data science techniques which are applicable to any domain. Our focus is Internet of Things. The course is practitioner oriented i.e. not academic and is not affiliated to a university.
InvestmentThe course is conducted Online and Offline(london). Please contact us for pricing (info@futuretext.com)
Help with jobs/employment yes, we aim to transition your career. Hence, we are selective in the recruitment for the course. There are no guarantees – but a career transition is a key goal for us. We work with you  over the duration of the course(including the Project) to typically achieve one of the following :  a) Help you to get a new role in Data Science/IoT.  B) Support you in your existing Data science role Often career transition includes support for Startups also or working with your existing compant
Created by professionals See my profile below
Personalization The course is based on a PLP (Personal learning plan) which allows you to customize for language, projects, domains, career goals, entrepreneurial goals etc . The course can be personalized. Examples include a focus on CEP/Sensor fusion,  RNNs and Time series, Edge processing, SQL  etc. There is no extra cost for this but we agree scope before we start through a Personal Learning Program(PLP).
Duration The course starts from Aug / Sep 2016 and late Jan 2017. We work with you for the next six months after that on a specific project and to help transition your career to Data Science through our network. The extra time also allows you to catch up on specific modules in the course
Projects A significant part of the course is Project based. Projects are based on   predictive analytics algorithms for IoT applications. Projects use our methodology which is based on a formalized way of solving IoT analytics  problems. Projects can be based in any of the Programming Languages we cover i.e. R or Python. Spark(Scala) and SQL(distributed processing i.e. Big Data) and  Theano and deeplearning4j for Deep learning . If you want to work on a specific project you should indicate in advance(or if you want to explore some ideas deeper)
Access to knowledge We do not restrict access to knowledge by specialization. For example – if you choose to focus on sensor fusion – you will still have access to all material for Deep learning
Batch sizes Are limited to ensure personalized attention
Time per week about 5 hours/week. No additional materials needed to buy etc
Certificate of completion Yes – based on the quiz and projects. See below for levels of certification
Delivery of content via video. You do not have to be online at specific times with the exception of hangouts
Is there a selection process? Yes. I spend a lot of time with participants and this approach may not be suitable for everyone. Hence, there is an initial discussion before you signup

 

Who has created this course / Who is the tutor?

The course is created by Ajit Jaokar

 

Ajit”s work spans research, entrepreneurship and academia relating to IoT, predictive analytics and Mobility.

His current research focus is on applying data science algorithms to IoT applications. This includes Time series, sensor fusion and deep learning(mostly in R/Apache Spark). This research underpins his teaching at Oxford University (Data Science for Internet of Things) and ‘City sciences’ program at University of Madrid.

His book is included as a course book at Stanford University for Data Science for Internet of Things. In 2015, Ajit was included in top 16 influencers (Data Science Central), Top 100 blogs( KDnuggets), Top 50 (IoT central)

Ajit has been involved with various Mobile / Telecoms / IoT projects since 1999 ranging from strategic analysis, Development, research, consultancy and project management. From 2011, he has further specialized in the predictive analytics for IoT.

Ajit works with Predictive learning algorithms(R and Spark) with applications including Smart cities, IoT and Telecoms

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 (Fp7 project). Since May 2005, he has founded the OpenGardens blog which is widely respected in the 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.

Ajit is passionate about teaching Data Science to young people through Space Exploration working with Ardusat

Additional details

Why is this course unique?

The course emphasizes some aspects are unique to IoT (in comparison to traditional data science). These include: A greater emphasis on time series data, Edge computing, Real-time processing, Cognitive computing, In memory processing, Deep learning, Geospatial analysis for IoT, Managing massive geographic scale(ex for Smart cities), Telecoms datasets, Strategies for integration with hardware and Sensor fusion (Complex event processing). Note that we include video and images as sensors through cameras (hence the study of Deep learning)

What is the implication of an emphasis on IoT?

In 2015/6, IoT is emerging but the impact is yet to be felt over the next five years. Today, we see IoT driven by Bluetooth 4.0 including iBeacons. Over the next five years, we will see IoT connectivity driven by the wide area network (with the deployment of 5G 2020 and beyond). We will also see entirely new forms of connectivity (example: LoRa , Sigfox etc). Enterprises (Renewables, Telematics, Transport, Manufacturing, Energy, Utilities etc) will be the key drivers for IoT. On the consumer side, Retail and wearables will play a part. This tsunami of data will lead to an exponential demand for analytics since analytics is the key business model behind the data deluge. Most of this data will be Time series data but will also include other types of data. For example, our emphasis on IoT also includes Deep Learning since we treat video and images as sensors.  IoT will lead to a Re-imagining of everyday objects. Thus, we believe we are only just seeing the impact of Data Science for Internet of Things.

How is this approach different to the more traditional MOOCs?

Here’s how we differ from MOOCs

a)  We are not ‘Massive’ – this approach works for small groups with more focused and personalized attention. We will never have 1000s of participants

b)  We help in career leverage: We work actively with you for career leverage – ex you are a startup / you want to transition to a new job etc

c)  We are vendor agnostic

d)  We work actively with you to build your brand(Blogs/Open source/conferences etc)

e)  The course can be personalized to streams (ex with Deep learning, Complex event processing, Streaming etc)

f)  We teach the foundations of maths where applicable

g)  We work with a small number of platforms which provide current / in-demand skills – ex Apache Spark, R etc

h)  We are exclusively focused on IoT (although the concepts can apply to any other vertical)

 

What is your approach to Programming?

The main Programming focus is on R and Python. We also use Spark (Scala, SQL and R). We  use  H2O and Theano(for Deep learning).

 

What is your approach to working with Algorithms and Maths?

The course is based on modelling IoT based problems in the R and Python programming languages.  We follow a context based learning approach – hence we co-relate the maths to specific R based IoT models. You will need an aptitude for maths. However, we cover the mathematical foundations necessary. These include: Linear Algebra including Matrix algebra, Bayesian Statistics, Optimization techniques (such as Gradient descent) etc.

 

Where do you stand on the R vs. Python debate?

The primary language for the course is R. But we also cover code in Python. Because the course lasts for a year,  you can easily cover both. We believe that commercially R will be more valuable because of the uptake from companies like Microsoft (Azure), HPE(Vertica), SAP(Hana), Oracle, Hitachi/Pentaho etc.

What is your view on Open source?

We actively encourage Open Source. We intend to Open Source most of your code and also our methodology. We encourage participants to contribute to our Open Source github also. This helps to build your personal brand. The code and content is part of our forthcoming book.

What is your view on Publishing?

Like Open Source, we encourage Open publishing. Many of the course participants have featured in blogs like KD Nuggets and Data Science Central. For example: Deep Learning for Internet of Things Using H2O by Sibanjan Das and Ajit Jaokar on KDnuggets

Can you explain more about Constructivism?

The learning philosophy of the course is based on a concept called Constructivism.

 

Constructivism (constructivist learning philosophy) is based on learning mechanisms in which knowledge is internalized by learners through processes of accommodation and assimilation. The learner has an internal representation of a concept. They then encounter new ideas and concepts. These ideas and concepts are assimilated through learning. In doing so, they may incorporate new experiences into the existing framework. Alternately, if they find that the new experience contradicts their existing framework – they may evolve their internal representation or change their perception.  Thus, learning is an on-going process of concept assimilation. We incorporate constructivist ideas into the course content, Projects and the methodology. Here are the implications and findings of this approach.

 

Specifically, that means for this course:

 

  • Understanding existing knowledge in the onboarding stage:  To acquire new knowledge in complex domains, the context is important. Specifically, the starting point of the Learner is important so that new ideas can be co-related to their existing knowledge base. This is different for everyone. Hence, we take some time to understand your existing knowledge in the onboarding stage
  • Co-relating concepts: New concepts and ideas have hierarchical dependencies and also lateral connections. These need to be incorporated into the learning process. This is included in the content as far as possible. It implies that you can learn another language (Python) based on the main language( R  ) relatively easily
  • Personalization and longer duration: The process is slow and personalized. It is not possible to use this approach in a mass/massive mode
  • Flexible learning paths: There is a broad structure to acquiring knowledge but not a fixed path
  • The use of projects in context of constructivism:  Projects provide the Physical context. But Projects need to be seen in the wider context. Projects and Methodology go together. The methodology provides the panoramic view to the problem in context of the Big Picture. Thus, Projects are the core of our course – but we use the idea of Projects in a wider learning context.

The key idea in constructivism is: The starting point is familiar to the learner. Hence, we spend a lot of  time in the first few months trying to understand the learner’s current state of understanding. After this, the next phase is spent on the core modules which correlates new ideas to the existing understanding thereby building on existing concepts. In turn, these are further expanded in the  project.

The project itself is contrasted against a methodology  (we are creating an Open methodology for Data Science for Internet of Things as part of the course).

 

How do I ‘transition my knowledge’ and gain recognition?

Data Science for IoT is a new domain. It is also an amalgamation of two rapidly evolving domains in their own right(Internet of Things and Data Science). This gives you many opportunities to establish expertise in niche areas using the Zulu principle – become an expert on a clearly defined, narrow subsection of a field

Many on the course have pursued this concept with success: Dr Vinay Mendiratta(IoT systems level optimization), Robert Westwood(Markovian analysis for IoT datasets), Barend Botha(IoT visualization), Yongkang Gao(Deep learning), Vaijayanti Vadiraj(renewables), Ibrahim El Badawi(Drones)

Many of these areas have been suggested by the participants themselves…

We encourage you to publish your work externally.

How does Certification work?

There are three stages of certification

 

a)  Stage One: Strategic certification

b)  Stage Two: Project certification

c)  Stage Three: Programming certification

 

Where possible, we also validate projects externally especially from a business perspective.

For example if you are doing a project on renewables, we try to validate using someone in Renewables to give feedback for the project. The Programming certification will be in R language and Depending on the optional languages you have chosen. Projects can span a wide range of interesting areas as long as they pertain to IoT and analytics. These include renewables, automotive, Drones etc.

Is one year not too long?

Not really!.

I see many courses who claim to create a Data scientist in X weeks – that is not possible in my view

Especially with a subject like Data Science for IoT, we have complex interdependent concepts which need a longer timeframe to absorb. The longer timeframe also gives you (and me) more flexibility to manage the course. Finally, half the course is based on Projects – which also gives you flexibility.

Is this an academic course?

No. It is not. It is a practitioner based course. It is also not affiliated to any academic institution.

To sign up or for any other questions: please contact info@futuretext.com

Data Science for Internet of Things Course – Big Picture and Outline

Over the last year and a half, I have been teaching and evolving the concept of Data Science for Internet of Things

Here is how our current course outline looks like(and the rationale behind the approach)
comments welcome

If you are interested in being a part of the next batch, please contact me at info@futuretext.com

Data Science for Internet of Things is based on time series data from IoT devices – but with three additional techniques: Deep learning, Sensor fusion (Complex Event Processing) and Streaming.

We consider Deep learning because we treat cameras as sensors but also include reinforcement neural networks for IoT devices

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)

The ideas learnt in the core modules are implemented in Projects. Projects could last as long as six months

The diagram is representative of the course (not an application per se). It shows the core modules(ex time series etc). The advanced modules(ex Sensor fusion) are built on these

Much of our work has been published in leading blogs like KDnuggets and Data Science Central etc

The course has evolved based on active contribution 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

We use Apache Spark for Streaming and Apache flink for sensor fusion.

Ironically, due to the emphasis on Data, the course is strictly not an IoT course ie we are concerned primarily with applying predictive learning algorithms on IoT datasets

Finally, the course is personalized. I see it more as coaching than a course. – for example you can choose to focus on a smaller subset of topics which is decided in the personal learning plan at the outset

Interested ? Email info@futuretext.com for details of the September batch (now in it’s fourth batch)

Data Science foundation for Programmers – One day workshop in London, Miami and New York

 

 

 

Data Science foundation for Programmers is a one day course that introduces Programmers to developing Data Science applications.

The hands-on course uses the R Programming language to introduce machine learning algorithms.

The program includes a one day workshop followed by a one week online session to complete the Programming Exercises.

Workshop Outline

What is Data Science

  •  An introduction to Data Science
  • Data Science process flow/steps
  • Machine Learning algorithms
  • How to choose an algorithm

The R Programming Language

  • Why should you learn R and who is using it
  • R in the ‘Big Data world’
  • R syntax(Assignments, Data Structures, Flow Control, Functions)
  • R packages – an overview
  • Loading and Handling Data in R
  • Example Datasets

Exploratory Data analysis

1) Understanding your Data:

In this section, we understand the characteristics of the data which help us later in choosing an algorithm. This includes

  • Summary in R
  • Distributions
  • Dimensions of Data – Mean, Standard deviation, Mode
  • Data corelations

2) Preprocessing Data :

Here, we understand the steps in preprocessing data including Scale, Center,

Standardize, Normalization and Principal Component Analysis

3) Visualizing Data :

In this section, we discuss techniques to data visualization in R

From Programming to Statistical Programming

  • Making Predictions – Supervised and unsupervised learning
  • Understanding Linear Regression
  • Non linear regression techniques (ex Support vector machines, k nearest, Decision trees)
  • Linear classification techniques (ex Logistic regression)
  • Non linear classification techniques(ex Neural networks)
  • Model Evaluation

R in the wider context

  • R in the Big Data world – ex Apache Spark
  • Deep learning
  • R and Python

Dates and Venue:

London

July 22 9:30 am to 4:30 pm – venue in central London

Miami

Workshop one: Tuesday Aug 2 and Wednesday Aug 3 in the evening 5:30 to 8 pm

Workshop two: Saturday Aug 6 full day (9 am to 4:30 pm)

New York

Aug 10 and 11 in the evening 5:30 to 8 pm

Investment

$199 USD for New York and Miami and

£140 GBP + VAT for UK

For registration (including Online option) please contact info@futuretext.com

Notes

a) Workshops have limited places – please contact fast if interested

b) Outline and Syllabus subject to change

c) The course is hands-on – and you will need to have your own laptop and previously install R on it.(instructions will be provided)

d) You do not need to already know R Programming but you must have some Programming background in a language.

 

Internet of Things world – the world’s largest IoT event

This year, as usual, we are proud to support IoT world in Santa Clara – the world’s largest IoT event

There is a great list of speakers

Topics I liked from the agenda include ..

  • How the Internet of Things Is Poised to Drive Business and Societal Transformation On a Global Scale
  • Revolutionizing Business Models; Driving Innovation & Embracing Disruption in a Future Connected World
  • Creating Value from connecting “things” Assessing the commercial feasibility and monetization of IoT
  • How will the “Internet of Things” remake your industry?
  • How secure is IoT in the home
  • Examining the role of Big data, Processing and Analytics for the Industrial Internet
  • The future of mobility: Assessing long term investment in car tech and how the industry will change over the next 10 years
  • The competing visions on the future of the connected car: Which horse should you bet on?
  • Examining the Smart Watch Trends, Timelines & Predictions
  • The Rise to Smarter Health Through Wearables
  • Examining Advancements and Adoption of Industrial Robotics
  • Building the “Smart Factory”
  • Showcasing a Connected City’s innovations and plans to move towards a smarter city
  • Transforming Public Services with IoT
  • Next Generation Data Visualization & Predictive Analytics
  • How is the Internet of Things is changing healthcare?
  • Remote Patient Monitoring with IoT
  • Building IoT solutions and taking data analytics into the cloud
  • Fog Computing: A Platform for IoT & Analytics
  • Hacking IoT
  • Building a Business Case for More Integrations in the Home
  • IoT in Shipping
  • Monetizing the Smart City with Location and Context
  • Connected Aviation Opportunities – Understanding Passenger Experience
  • Energy, Environment & Agriculture
  • Data Analytics & Visualization for Energy and Utility Companies
  • Utility Provider Case Study: Making the move towards the connected home versus providing
  • Examining the security risks associated with smart meters and smart grids
  • Embracing IoT in Agriculture: A Monsanto Case Study
  • IoT and the Farm: Challenges and Goals for the Future
  • Delivering an effective UX for the consumer
  • Vision for IoT: How Image Sensors Are Set To Influence the Next Generation of Connected Devices
  • Is your data center ready for IoT?
  • Moving towards the Next Generation of IoT Data with Machine Learning
  • Panel: Panel: Examining the role of Big data, Processing and Analytics for the Industrial Internet

for the full agenda and speakers see IoT world in Santa Clara – the world’s largest IoT event

Deep Learning for Internet of Things Using H2O

Pleased to see this blog on Kdnuggets – co-authored by me  Deep Learning for Internet of Things Using H2O

 

 

Deep Learning Applications for Smart cities

Background and Approach

This blog is based on my talk in London at the Re.work Connected City Summit on Deep Learning Applications for Smart cities. The talk is based on a forthcoming paper created with the help of my students at UPM/citysciences on the same theme. Please email me at  ajit.jaokar at futuretext.com  or follow me  @ajitjaokar  for more details.

Here are some notes on our approach:

  • When we speak of Machines – the media dramatizes the issue.  Yet,  city officials and planners plan for ten to twenty years in the future. They will have to consider many of these issues in a pragmatic way.
  • Deep Learning / Artificial Intelligence will impact many aspects of Smart cities. We decided to approach the subject in a pragmatic manner and to explore the impact of Deep Learning/AI technology on the lives of future citizens.

How could self-learning machines affect humanity in cities?

Initially, we started off with the usual Smart City approach i.e. domains such as Security – Transport – Health – Governance – Environment etc

Then, we were inspired by a statement “Man becomes the sex organs of the machine world – the bee of the plant world – enabling machines to evolve ever new forms” – Marshall McLuhan

It indicates that disruptive innovations like Deep Learning and AI cannot be viewed in silos. Instead, we decided to reframe the problem in a more disruptive way by asking the questions;

    What can Machines learn from Observations?

    What can Machines learn from Data?

    What impact does it have on new services, culture, citizens ?

    What are the threats?

    How will the lives of future citizens be impacted through self learning machines?

 

The shortest introduction to Deep learning:

Here is a brief introdcution to Deep Learning.  I have spoken of the Evolution of Deep Learning models and An introduction to Deep Learning and it’s role for future cities

Deep Learning can be seen more as a specific form of Machine Learning that leads to creating Self Learning Machines.  The whole objective of Deep Learning is to solve ‘intuitive’ problems i.e. problems characterized by High dimensionality and no rules.  With Deep learning, Computers can learn from experience but also can understand the world in terms of a hierarchy of concepts – where each concept is defined in terms of simpler concepts. The hierarchy of concepts is built ‘bottom up’ without predefined rules . This is similar to the way a child learns ‘what a dog is’ i.e. by understanding the sub-components of a concept ex  the behavior(barking), shape of the head, the tail, the fur etc and then putting these concepts in one bigger idea i.e. the Dog itself.

More specifically, a form of Deep Learning called Reinforcement Learning is making a huge impact in areas such as AlphaGo. Reinforcement Learning (RL) is based on a system of rewards. RL is a form of unsupervised learning – An RL agent learns by receiving a reward or reinforcement from its environment, without any form of supervision other than its own decision making policy.

In machine learning, the environment is typically formulated as a Markov decision process (MDP) as many reinforcement learning algorithms for this context utilize dynamic programming techniques. The main difference between the classical techniques and reinforcement learning algorithms is that the latter do not need knowledge about the MDP and they target large MDPs where exact methods become infeasible. Reinforcement learning differs from standard supervised learning in that correct input/output pairs are never presented, nor sub-optimal actions explicitly corrected. Further, there is a focus on on-line performance, which involves finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). (adapted from wikipedia)

Analysis

Here are the trends we note from the themes noted above. Link sources from Home of AI info and the web

What are machines learning from Data and Observations?

  • New computer program first to recognize sketches more accurately than a human
  • Deep Learning Algorithm ‘Paints’ in the Style of Any Artist it Copies
  • New big data system developed at MIT is more intuitive than humans
  • Artificial intelligence breakthrough as intuition algorithm beats humans in data test
  • MIT Develops Device That Can See People Through Walls
  • Lie-detecting algorithm spots fibbing faces better than humans
  • Machines That Can See Depression on a Person’s Face
  • An algorithm aims to be able to replace human intuition
  • ‘Psychic Robot’ System Guesses Intentions From Your Movements
  • MIT’s intelligent drone can avoid crashes and fly at 30 MPH
  • Facebook working on AI that can tell what’s in photos
  • Computer Algorithms Could Aid Schizophrenia Diagnose
  • Machines That Can See Depression on a Person’s Face
  • Robot Radiologists Will Soon Analyze Your X-Rays
  • Predicting change in the Alzheimer’s brain
  • A new computer program that can diagnose cancer in just two days!
  • Machine learning to help predict online gambling addiction
  • Predicting people’s daily activities with deep learning
  • MIT Scientists Create An AI System That Can Determine How Memorable Your Face Is
  • This Algorithm Is Better At Predicting Human Behaviour Than Humans Are
  • New Artificial Intelligence: Russia Endows Robots With Collective Mind
  • Scientist Develop New Machine Which Can Calculate Pattern Recognition with Near Human speed
  • Machine Vision Algorithm Learns to Recognize Hidden Facial Expressions
  • Artificial Intelligence: Scientists Developed a Handwriting Algorithm
  • Computer With Built-In Algorithm Beats Man In A Turing Test
  • Machine learning to differentiate between positive and negative emotions using pupil diameter

 

Self learning for Robots(from observation)

  • Giving robots a more nimble grasp
  • Why it is hard to teach robots to choose wisely
  • Machine learning plays vital role in the evolution of Man
  • Designing Robots That Learn as Effortlessly as Babies
  • How Robots Can Quickly Teach Each Other to Grasp New Objects
  • Why IBM just bought billions of medical images for Watson to look at
  • Read my lips: truly empathic robots will be a long time coming

 

Learning Culture, Humanity, emotions and ethics

  • Smart Programs Read Shakespeare
  • Artificial intelligence learns how to put together interactive stories just as good as a human
  • How do you teach a machine to be moral?
  • ‘Psychic Robot’ System Guesses Intentions From Your Movements
  • Lie detection software learns from real court cases
  • Why Helping Humanity Should Be Core to Learning
  • Could Artificial Morals and Emotions Make Robots Safer?
  • AI: In search of the sarcasm algorithm
  • Microsoft Teaches Computers To Be Funny
  • Microsoft’s Project Oxford Can Now Detect Emotions from Photos
  • Robots are learning to disobey humans: Watch as machine says ‘no’ to voice commands
  • Robots could be converted to religion someday: Scientists
  • Intimacy & Falling In Love With A Robot Could Happen In 50 Years Because Of Artificial …
  • Health
  • If We Want Humane AI, It Has to Understand All Humans
  • Humai Is Working On A Way To Bring Your Loved Ones Back From The Dead
  • Mum Robot Goes Darwinian on Her Kids

How does that (self) learning affect services and our lives in future cities

  • Artificial intelligence comes to toys
  • Beyond the Pill: Data Is the New Drug – Google Life Sciences Rebrands As Verily, Uses Big Data To Figure Out Why We Get Sick
  • Nvidia Aims To Power Flying Vehicles with Jetson TX1 Board
  • Motorcycle-riding robot may take on world champion racer
  • Meet Mercedes-Benz’s Vision Tokyo, a self-driving car for the megacity
  • How artificial intelligence could lead to self-healing airplanes
  • Trains with brains: how Artificial Intelligence is transforming the railway industry
  • A self-driving sailboat to patrol the oceans and monitor the environment
  • Malaysia testing ‘artificial intelligence’ for prisons
  • Real-Time Seizure Detection Possible with Learning Algorithm
  • Facebook Is Helping People With Blindness “See” the Photos on Their Walls
  • Mitsubishi Electric uses machine-learning tech to detect distracted drivers
  • Tinder matches made easy with new intelligent algorithm
  • Deep Learning Algorithm Successfully Identifies Potential Intracranial Haemorrhaging
  • An artificial intelligence based third Umpire
  • When children talk to toys, some are talking back
  • Predicting change in the Alzheimer’s brain
  • Robotic Automation Meets Agriculture
  • Food delivered by drones, driverless cabs and cyber PAs to organise your party: A revolution in …
  • AI will soon be forecasting the weather
  • How Artificial Intelligence Can Fight Air Pollution in China
  • Starfish-killing robot to protect Great Barrier Reef
  • Self-Driving Car Tech Allows Vehicle To ‘See’ Environment In Real Time
  • US Company On Plan To Bring People Back From Dead Using Artificial Intelligence
  • A trillion tiny robots in the cloud: The future of AI in an algorithm world
  • Teforia Is A Tea Brewing Robot That Uses Algorithms To Pour The Perfect Cup
  • Japanese artificial intelligence passes university exams (but still can’t quite get into the country’s …
  • Facebook AI built to help visually impaired people
  • Problem of Climate Change and Global Conflicts Can Be Solved Using Human and Computer …

 

Risks to humanity and cities

  • ‘Only movies build bad robots‘ – famous last words?
  • Why human-in-the-loop computing is the future of machine learning
  • As Robots Steal Millennials’ Jobs, Young Workers Focus On Skills, Not Careers
  • Millions of jobs at risk from artificial intelligence
  • Davos report projects 5 million jobs will be lost to new technologies by 2020
  • Can Humanity Rein In The Rise Of The Machines?
  • Christian leader warns of ‘Frankenstein monsters’ due transhumanism
  • The rise of the killer robots — and why we need to stop them
  • Producer of Russia’s Armata T-14 plans to create army of AI robots
  • Inside the Pentagon’s Effort to Build a Killer Robot
  • How Technology Could Prevent Another Paris-Like Attack
  • Kaspersky deepens security offering through machine learning
  • Robots will declare war on humans within 25 years, claims artificial intelligence expert
  • Law firm bosses envision Watson-type computers replacing young lawyers
  • Hitachi Hires First ‘Artificial Intelligence’ Boss To Manage Workers

Conclusion and Evolution

We reframed the problem of Deep Learning and Smart cities by asking the Question:

How could self-learning machines affect humanity in cities?

    What can Machines learn from Observations?

    What can Machines learn from Data?

    What impact does it have on new services, culture, citizens

    What are the threats?

Please contact me at ajit.jaokar at futuretext.com to know more updates – especially if you are a city official. We are also planning to explore the implementation of these ideas by working with companies like Nvidia.

I would also like to thank the students who helped me with this project.


Wind energy forecasting using R : Data Science for Internet of Things Project

Participants from the Data Science for Internet of Things course are working on some excellent projects.

Here is a great example -

Created by Vaijayanti – and other collaborators from the course

Project name: 4Wind

Domain:  Renewables/Wind energy

Problem statement: To make day-ahead forecast for wind power (10 minute interval data)

Data: collected from around 5000 turbines

Duration: She has been working for more than a year and still ongoing

Programming Approach : Using R for data science. To evaluate various models: ARIMA, Holt-Winters forecast, neural networks ACF and PACF plots reveal 35 lags matter. Hence the data is recoded to have 35 columns of lags for both wind speed and wind power.

R packages: forecast, neuralnet, nnet

Challenges: Training neural networks has become heavy on a PC. Hence, experimenting with multiple models including Distributed(Spark) and AWS, Azure etc. 35 lags were based on ACF and PACF analysis. However, this number could be different. More analysis might yield different lags as predictors. Also, we could use not just consecutive lags but also the samples a day before and year before are also predictors.

UI: Using the Shiny package 

Publication/Open source methodology: Contribution to Data Science for IoT methodology and to forthcoming book on Data Science for IoT

Image source: wikipedia

 

 

The role of Constructivism in teaching Data Science for Internet of Things

The role of Constructivism in teaching Data Science

 

Constructivism, Data Science and Online Learning

I enjoy teaching.  I teach at University level (Oxford and UPM – Madrid). I also teach young people the principles of Computer Science using Space technology using a live Satellite in collaboration with Ardusat. Over the last decade, education itself has changed with more emphasis on Online education. This allows us (as teachers/tutors) the opportunity to explore new modes of  learning.  However, the principles of learning have not changed. I believe, in many ways, we now need to come back to the more traditional/timeless principles of teaching in a Digital world

This document explains the learning philosophy and the principles underpinning the Data Science for Internet of Things course. Specifically, it discusses a learning technique called Constructivism which I have been exploring for the teaching  of complex topics like Data Science for IoT. I  am thankful to Jean Jacques Barnard (a participant in the course) for his comments and feedback to this post.

Constructivism (constructivist learning philosophy) is based on learning mechanisms in which knowledge is internalized by learners through processes of accommodation and assimilation. The learner has an internal representation of a concept. They then encounter new ideas and concepts. These ideas and concepts are assimilated through learning. In doing so, they may incorporate new experiences into the existing framework. Alternately, if they find that the new experience contradicts their existing framework – they may evolve their internal representation or change their perception.  Thus, learning is an ongoing process of concept assimilation. We incorporate constructivist ideas into the course content, Projects and the methodology. Here are the implications and findings of this approach.

To summarize

a)   To acquire new knowledge in complex domains, the context is important. Specifically, the starting point of the Learner.

b)   New concepts and ideas have hierarchical dependencies and also lateral connections. These need to be incorporated into the learning process

c) The process is slow and personalized. It is not possible to use this approach in a mass/massive mode

d)  The Physical context matters. But Projects need to be seen in the wider context. Projects and Methodology go together. Projects provide the Physical context. The methodology provides the panoramic view to the problem in context of the Big Picture

e)  There is a broad structure to acquiring knowledge but not a fixed path

 

 

1)    Greater learning agility and flexibility

When I first met course participant Priscila Grison a few years ago in San Francisco, she mentioned an interesting Spanish language book by Julio Cortazar called Rayuela(in English Hopscotch). The author has a unique structure for the book. An author’s note suggests that the book would best be read in one of two possible ways, either progressively from chapters 1 to 56 or by “hopscotching” through the entire set of 155 chapters according to a “Table of Instructions” designated by the author. Cortázar also leaves the reader the option of choosing a unique path through the narrative.

I find this idea fascinating i.e. that the author should create one structure but the reader could assimilate it in more than one way. Why could that not be true of Online Education also? The ‘Educo’ in ‘Education’ is to draw from within. Online learning offers us that unique ability to learn from within – a concept now becoming more common through ideas like ‘Learning agility’ i.e. an individual must the ability to learn, adapt, and apply in quick cycles. By using the constructivist philosophy, we incorporate a ‘structure’ a linear flow of modules but also an ‘unstructure’ through various means – such as Personal learning plans, choice of programming languages etc. This leads to greater learning agility.

 

2)    Implications for Projects in a constructivist sense

Projects are the core of our course – but we use the idea of Projects in a wider learning context. The key idea in constructivism is: The starting point is familiar to the learner. Hence, we spend a lot of  time in the first few months trying to understand the learner’s current state of understanding. After this, the next phase is spent on the core modules which correlates new ideas to the existing understanding thereby building on existing concepts. In turn, these are further expanded in the  project.  The project itself is contrasted against a methodology Creating an Open methodology for Internet of Things – IoT analytics and Data Science (created by me, Jean Jacques and another course participant Shiva Soleimani) which shows the learner how their knowledge fits into the wider context of the problem.

 

3) Acquiring new and complex skills

Constructivism is very suited to acquiring new and complex skills because new knowledge is both hierarchical and also laterally related. For example, PCA  needs understanding of Eigenvalues .. which in turn is based on Matrix multiplication. Similarly, many concepts in Python and R are related(lateral co-relation of new concepts)

 

4) Not Massive (Not a MOOC)

The strategy of Constructivism will not scale – because it needs greater direct engagement with the participants. That may be for the good!  I have never heard a teacher use the word ‘Massive’ in context of teaching.  VCs use ‘Massive’ for MooCs  i.e. Massive Open Online Courses – but then again VCs have been known to search for mythical beasts like Unicorns.  If you really think about it – the word Massive is associated with teaching only as a Business model.  We do not believe that ‘Massive’ helps teaching because it is really not possible to give attention in the massive model

5) Not Free but Affordable

An education system based on Constructivism will be not free but affordable. To create a large/Massive system we need a free service – with minimal personal engagement.  In contrast, everyone remembers from their childhood at least one teacher who took an extra interest in their work. Such personal attention is not possible on a large scale. Instead of free – a more affordable model is possible. Such models are not  likely to be funded by traditional VCs. But, they could be still be viable and niche institutions.

 

In industry – the analogy would be the German concept of Mittelstand companies (common in Germany, Austria and Switzerland) . Mittelstand companies have no real equivalence outside of Germany. It would be wrong to call them an SME (Small to medium enterprise) because they operate on a specific ethos  which includes qualities such as: Long-term focus, Independence, Emotional attachment to the business, Investment into the workforce, Flexibility, Lean hierarchies, Innovation, technical excellence, craftsmanship, Social responsibility etc.  The point here is: It is possible to create a viable venture on the Web which serves a niche customer base – taking a long term view.  This model is not so common – but is still very achievable.  That’s our aspiration.

6) Community and personalization

This strategy makes us rethink the word ‘community’. Everyone on the Web likes to think of themselves as a ‘community’. But this is yet another word that has morphed into a different meaning in a commercial context. Take the case of LinkedIn. Is LinkedIn a ‘community’? Or more many people trying to sell things to others(with very little in common other than that function).  On the Web – communities reduce to forums where involvement of the tutor is minimised.  But with a smaller group, it is possible to have calls/ meetings etc.  So, we encourage lots more communication between the tutor and the participant i.e. actually speaking/Skype calls etc. Again, you cannot do this on a mass scale.

To Conclude ..

To summarize

a)   To acquire new knowledge in complex domains, the context is important. Specifically, the starting point of the Learner.

b)   New concepts and ideas have hierarchical dependencies and also lateral connections. These need to be incorporated into the learning process

c) The process is slow and personalized. It is not possible to use this approach in a mass/massive mode

d)  The Physical context matters. But Projects need to be seen in the wider context. Projects and Methodology go together. Projects provide the Physical context. The methodology provides the panoramic view to the problem in context of the Big Picture

e)  There is a broad structure to acquiring knowledge but not a fixed path

To conclude, all this sounds less like training but more like going back to the first Principles of Teaching or even Coaching for complex topics such as Data Science for IoT. Going back to timeless values(even when they don’t quite scale!) may well be called for in the next generation. Dr Hessa Al Jaber ex Minister of ICT for Qatar (with whom I was honoured to work with at the World Economic Forum) says in an insightful  post Why my daughter’s generation faces challenges mine didn’t   In the midst of this digital age, and global connectivity fueling all of these conflicting ideas, her generation might start to feel lost.  Values that seemed immutable have begun to shift.

I  welcome comments (ajit.jaokar at futuretext.com ). I believe this is a new approach – and it’s a learning experience for us all!

Image:  Jean Piaget – the founder of Constructivism – source – Wikipedia

MWC 2016: 3 developments to watch which impact IoT analytics Introduction

I have attended the Mobile World Congress for the last 7 years (5 of them as a speaker/panellist).

This year, I did not go. Partly, this is reflective of my change in business focus (to Data Science for Internet of Things course) .

But, also I believe that today, MWC does not reflect disruptive innovation.  The show has more than 100,000 participants but we do not see fundamental game-changing innovations likeDeepmind – Go from Google last week.

Thus, in keeping with my current focus, this blog highlights developments to watch from MWC 2016 which impact IoT analytics. From an IoT / IoT analytics standpoint – the Telecoms industry would only play a major role with  5G(beyond 2020) ex see The rise of 5G: the network for the Internet of Things  and The plans for 5g to power the Internet of Things

Before I list the areas that impact IoT analytics, here is some background. These factors have remained the same for most of the Mobile Data Industry’s lifetime

a)      Telecoms Operators only make money when Data passes through the Cellular network. That is not always the case(ex WiFi, Bluetooth etc).

b)      Telecom Operator business models are  not suited to selling other types of products(ex to Enterprise)

c)       The interface to the customer, once so prized, is now gone to Web and Social media players like Facebook

d)      Telecoms have a long history of successful engineering led standards. But these standards have not been successful at higher layers of the stack. So, again we see many players unite to launch One M2M – but the history of Telecoms standards is poor

e)      Device vendors like Samsung, Apple etc are more successful with innovation but are take an independent approach as best they can

f)       Even when Data passes through the Telecoms network, the Operator may not be able to monetize it for legal reasons(ex just because an Operator knows your location, they do not have the permission to sell you advertising)

Having said that, from an IoT Analytics standpoint, there are some areas which impact IoT data(hence IoT analytics) which are unique to Telecoms. This blog discusses three such developments which I am tracking

 

Analysis and Focus areas

1) LPWA battles

low power wide area networks are a key battleground for the industry at the moment. With the arrival of 5G years away(including 5G devices) is years away, the main players today are Sigfox and LoRa. The Operators have been forced to push LTE-M in a hurry (see Lora vs LTE vs Sigfox). The momentum is certainly with LoRa and Sigfox.

 

2) Platforms

There is no shortage of IoT based platforms including Telecoms oriented platforms like Jasper(now acquired by Cisco).  However, we will see maturity in Platforms and new partnerships. Here are three developments

Analytics platforms spanning multiple network types: Platforms ItalTel launches solutions for healthcare and Infrastructure – this is an example of a more mature platform supporting multiple network types(Sigfox and LoRa ) and even WebRTC. The platform claims to have analytics built into it.

Open Source IoT analytics platforms like Kaa IoT who were at the show and who I am following with interest

Ericsson – Amazon Cloud Deal – A deal which combines the IoT cloud PAAS vendors with the Telecoms network providers.

3) e-SIM

And finally, the eSim. I have been following the work of companies like Gemalto for a while who have long advocated the eSim technology. Today, in the age of IoT, its a technology whose time has come.

The embedded SIM (also called eSIM or eUICC) is a new secure element designed to remotely manage multiple mobile network operator subscriptions and compliant with GSMA specifications

eSim could help customers to set up and manage subscriptions on devices remotely via a single embedded SIM (eSIM) in a process which will be a lot cheaper, easier and faster without sacrificing any levels of security. There were a few key eSim announcements such as Gemalto with Jasper wireless  and also Sierra wireless and valeo on telematics

 

Conclusions

IoT analytics (Data Science for IoT) is still a nascent field and Telecoms will be a key enabler for IoT. I see the initial impact around areas like Security, LPWA and Platforms which facilitate analytics for IoT. If you want to see more of my work, please see Data Science for Internet of Things course