Data Science for Internet of Things – practitioner course
Welcome to the world’s first course that helps you to become a Data Scientist for the Internet Of Things.
Only for October – now with a new part payment plan for last few places.
The course starts on Nov 10
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
The course starts from November 2015 and extends to March 2016. We work with you for the next year and a half to transition your career to Data Science.
Created by Data Science and IoT professionals, the course covers infrastructure (Hadoop – Spark), Programming / Modelling(R/Time series) and ioT. We cover unique aspects of Data Science for IoT including Deep Learning, Complex event processing/sensor fusion and Streaming/Real time analytics
Our vision is to create an intellectually elite group world class Data Scientists for IoT
Contact us at email@example.com to signup
- You can transition your career to Data Science for ioT
- You are not alone: Toolkits and community support to start working on real Data science problems for IoT
- You master specific skills: Spark, R, Scala, IoT platforms, Data analysis, SQL among others
- The content can be personalized (see examples of personalization below)
- The Data Science principles can apply to other domains i.e. beyond IoT
(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
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 toolkit
A set of models and techniques in the R programming language to work with IoT based scenarios based on Time series modelling. These include models from Retail, Healthcare, Energy, wearables, Transport etc covering specific examples in these domains. The module provides you a toolkit which you can adapt and use from Day one in your work.
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
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)
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.|
|Community support?||Yes. Also includes the Alumni network i.e. beyond the duration of the course at no extra cost. The course is based on a toolkit which we use to analyze IoT datasets in context of specific problems in industry verticals (ex Retail, Transport etc). You are thus empowered to work with IoT from the outset|
|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.|
|Investment||Offline(London): £1,200 GBP + VAT(if applicable)
Online: Yes. Please contact us at 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 upto a year and a half from the start of the course to get a new role in Data Science/IoT|
|Created by professionals||See our profiles below|
|Personalization||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. If you are interested in this option, please let us know at email@example.comIf you want to see examples of our work and content, please seeSpark SQL real time analytics by Sumit Pal(published on kdnuggets)The evolution of Deep learning models by Ajit Jaokar|
|Duration||The course starts from November 2015 and extends to March 2016. We work with you for the next year and a half to transition your career to Data Science.|
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, Azure BI etc
h) We are exclusively focused on IoT (although the concepts can apply to any other vertical)
Approach to Programming
The main Programming focus is on Spark (Scala, SQL and R). We will also use an ioT platform (like Thingworx) The participants need to be able to Code/come from a development background (the Programming language itself does not matter).
What is your approach to working with Algorithms and Maths?
The course is based on modelling IoT based problems in the R programming language. 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.
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 Re-imagining of everyday objects.
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)
Who is creating/teaching this course?
The course is created by futuretext and conducted by Ajit Jaokar, Dr Paul Katsande and Sumit Pal
Ajit Jaokar – Based in London, Ajit’s research and consulting is based on Data Science and the Internet of Things. His work is based on his teaching at Oxford University and UPM (Technical University of Madrid) and covers IoT, Data Science, Smart cities and Telecoms.
Dr Paul Katsande is a technical architect based in London working with Apache Spark, Scala and Data Science. Paul’s PhD research is based on image processing from the University of Manchester.
Sumit Pal is a big data, visualisation and data science consultant. He is also a software architect and big data enthusiast and builds end-to-end data-driven analytic systems. Sumit has worked for Microsoft (SQL server development team), Oracle (OLAP development team) and Verizon (Big Data analytics team) in a career spanning 22 years. Currently, he works for multiple clients advising them on their data architectures and big data solutions and does hands on coding with Spark, Scala, Java and Python. Sumit is based in Boston.
We have limited spaces. Please contact us at firstname.lastname@example.org if you want to take the next steps!
See testimonials below