Data Science for the Internet of Things – A Strategic and a Practitioner led approach
The Data Science for Internet of Things course helps you to become a Data scientist with an emphasis on Internet of Things. In addition to the practitioner based course, we now have a new course for Strategists.
See our early testimonials and program below.
The dimensions (core themes) of the course are
 Internet of Things
 Data Science
 Architecture ex Hadoop, Azure etc
 Data
 Algorithms/ Maths
 Programming (Python, SQL and Deep learning)
 Verticals(in our case IoT) but also spanning Telecoms, Supply Chain, Transport, Retail as applications of IoT
First cohort launched. Now accepting applications for the next batch. Limited spaces. To sign up or for any other questions, contact info@futuretext.com
Benefits and Features

Practitioner course 
Strategic course 
Impact on your work 
Designed for developers/ICT contractors who want to transition their career towards Data science roles with an emphasis on IoT 
Designed for strategists who want to understand Data Science for 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. 
Senior decision makers who are not in a direct development role. Roles could include: Big Data practitioner, IoT platform vendor, M2M / Telecoms providers, Product managers, Cloud based service providers, Analytics and reporting vendors/developers, Consulting/strategists 
Personalization? 
Yes. The course is personalized based on a Personal Learning program(PLP). The PLP includes core modules but also charts an individual path for the participant. This includes choice of platforms, Projects, modules etc. 
Yes. The course is personalized based on a Personal Learning program(PLP). The PLP includes core modules but also charts an individual path for the participant. This includes choice of platforms and modules (note that the Strategic version of the course is not project based. 
Community support? 
Yes. Also includes the Alumni network i.e. beyond the duration of the course at no extra cost 
Yes. Also includes the Alumni network i.e. beyond the duration of the course at no extra cost 
Approach to Programming 
Implementing(see scope below) 
Learning but not implementing(see scope below) 
Approach to Algorithms 
Implementing(see scope below) 
Learning but not implementing(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. 
The strategic course allows you to learn the concepts / techniques for Data science but it is not designed for you to undertake a Data Scientist role. 
Investment 
The practitioner course is both online and offline. 
The strategic course is online only. 
Duration 
The course is based on a document called a ‘Personal learning program (PLP)’. The PLP includes core modules but also charts an individual path for the participant. This includes choice of platforms, modules etc. The course is expected to last between 3 and 6 months. If offline in London – it will include 6 sessions. The duration can be mutually amended based on the PLP and could be extended as needed. Due to this approach, the course is limited to a small number of participants. 
The course is based on a document called a ‘Personal learning program (PLP)’. The PLP includes core modules but also charts an individual path for the participant. The course is expected to last between 3 and 6 months. The duration can be mutually amended based on the PLP and could be extended as needed. Due to this approach, the course is limited to a small number of participants. 
Scope and Modules 
Please see below 
Please see below 
Projects 
A major part of the course is based on projects. Projects may be suggested by participants and mutually agreed through PLPs. They may involve a third party (ex an entity who provides datasets, a platform vendor etc). We can also introduce you to companies we already work with. 
The strategic course is not based on working/building specific projects 
Approach to Programming 
See below 
See below 
Approach to Programming
The course covers Data science with an emphasis on IoT. For the practitioner version of the course, the participants need to be able to Code/come from a development background (the Programming language itself does not matter). Although we cover the Mathematics from first principles, a basic understanding of Math and an aptitude for Math is useful. For the strategic version of the course, you would cover the same ideas but would not be expected to write code. Also, depending on your PLP and personalization – you may choose a specific learning path within this context.
Programming scope includes:
 Python: The basic Python ecosystem  from Data wrangling to Visualization.
 Python Time series: Python/ Pandas based code.
 Python scikitlearn: The Machine learning libraries for Python for prediction
 Real time: Spark and Storm
 Microsoft Azure:
 Hadoop, SQL and Visualization: Based on platforms like Cloudera impala and Tableau. Real time, distributed SQL on Hadoop.
 Sensor fusion (Complex event processing)
 Deep learning
What is your approach to working with Algorithms and Maths?
We explain typical Data science algorithms with an emphasis on IoT datasets. Because we follow a context based approach based on IoT, we corelate the maths to specific examples. Algorithms are encapsulated in libraries and APIs such as scikitlearn(for Python)
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.
What is the implication of an emphasis on IoT?
In 2015, 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 (ex from companies like Sigfox).
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 Reimagining of everyday objects.
Unique elements of IoT: 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, Realtime 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)
How does your learning approach differ?
The emphasis on IoT allows us to take a Problem solving approach through Use Cases. Taking a Use case approach allows us to identify as many complex use cases in each vertical (Wearables, Healthcare, Manufacturing, Retail, Supply chain, Smart energy, Smart cities, Smart Home) and drill down from there. We thus take an ‘engineering led approach’ – i.e. start with a problem and work back to understand the implementation. In contrast, many courses start with specific Algorithms. This has two disadvantages: · Firstly, Machine learning algorithms are often unfamiliar and hard for most participants. Secondly, most people are more familiar with Programming than Maths – and the platform often uses libraries which encapsulate the Algorithms. Thus, by taking a problem solving approach and working backwards – you are empowered to work across many verticals and technologies. This approach is similar to the ‘Context based learning’ in education
Venue
If you are participating in the offline course, sessions in London are held on Saturdays. The schedule is based on the personal learning plan for each participant
Fablab London
1 Frederick’s Place
Off Old Jewry
EC2R 8AE
London
UNITED KINGDOM
Modules
The modules below cover both the Strategic and the Practitioner version of the course. For the strategic version of the course, you would cover the same ideas but would not be expected to write code. Also, for both versions of the course  depending on your PLP and personalization – you may choose a specific learning path within this context.
(Note the modules and the sequence are subject to change)
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
The Use Case approach
The Use case approach spans the course. We tie specific concepts to IoT use cases where possible. This includes the mathematical concepts. Each Use case starts with an IoT related problem. There may be more than one use case per concept. Themes for Use cases include (but not confined to): Complex event processing, Real time, Deep learning, Time series, Edge processing, Use cases by IoT verticals (ex healthcare), Spatial processing.
An exploration of the Hadoop ecosystem
An exploration of the Hadoop ecosystem and its evolution including Hadoop Fundamentals  Introduction to the Hadoop ecosystem – SQL, NoSQL, Distributed Real time SQL.
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, realtime 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)
Python for Data analysis
Here we learn Python for Data analysis packages including Numpy, Scipy, Matplotlib, Pandas, Scikitlearn and Bokeh plots. We use the iPython notebook, the Anaconda distribution, scikit learn, Pandas and Wakari.
IoT datasets
An exploration of IoT datasets and APIs by application: Healthcare, Manufacturing, wearables, Energy etc
Developing for IoT devices (Unique considerations)
Mathematical foundations of Machine learning
We cover
Linear Algebra including Matrix algebra,
Bayesian Statistics,
Optimization techniques (Gradient descent) etc.
Machine learning techniques and algorithms
Supervised algorithms, unsupervised algorithms (classification, regression, clustering, dimensionality reduction etc) as applicable to IoT datasets
Unique Elements for IoT
The course emphasises the following unique elements for IoT
 Complex event processing
 Deep Learning and
 Real Time / Time series datasets includes architectures like Spark and Storm
First cohort launched. Now accepting applications for the next batch. Limited spaces. To sign up or for any other questions, contact info@futuretext.com