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.
Only last few places left – Sep 2016 – please email
email@example.com if you want to join
Please contact firstname.lastname@example.org
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 Mar 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 (email@example.com)
Contact us at firstname.lastname@example.org 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
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
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
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.”
“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”.
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
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.”
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 email@example.com for details of the September batch (now in it’s fourth batch)
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
- 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|
|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
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 (firstname.lastname@example.org)|
|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 company|
|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 Sep 2016 to late Mar 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
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?
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 email@example.com