Now running in it’s third batch ..
Welcome to the world’s first course that helps you to become a Data Scientist for the Internet Of Things ..
For the latest batch of this course see Data Science for Internet of things #DataScience #IoT – Aug – Sep 2016 start now in its fourth batch
The course starts on March 22 – 2016 -
Please contact email@example.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
Duration: The course starts from March 2016 and extends to July 2016. 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, Deeplearning4j) 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
Offline (London): £1,200 GBP + VAT
Online: Yes. Please contact us at firstname.lastname@example.org
Contact us at email@example.com to signup
- 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, role, 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
- The course content can be personalized (see 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 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
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|
|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.|
|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 over the duration of the course(including the Project) to get a new role in Data Science/IoT|
|Created by professionals||See our profiles 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). 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 see Spark SQL real time analytics by Sumit Pal(published on kdnuggets)The evolution of Deep learning models by Ajit Jaokar|
|Duration||The course starts from March 2016 and extends to July 2016. 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.|
|Delivery of content||via video. You do not have to be online at specific times|
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)
Approach to Programming
The main Programming focus is on Python, R , Spark (Scala, SQL and R). We also use Deeplearning4j and Theano(for Deep learning). We will also use an ioT platform (like Thingworx) but we will emphasize IoT analytics. 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 Python and 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 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.
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.
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.
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.
We have limited spaces. Please contact us at firstname.lastname@example.org if you want to take the next steps!
See video below
|Week 0 March 15||Orientation, introductions, Personal learning plans, Platform signup|
|Week 1 mar 21||Foundations:An analytics Driven Organization – IoT and Machine Learning - Data Science for IoT – Unique characteristics – Data Science for IoT – why now?|
|Mar 28||Machine Learning concepts Deep Learning concepts|
|Apr 4||An introduction to IoT (Internet of Things)|
|Apr 11||IoT platforms – From sensor to Cloud|
|Apr 18||Concepts of Big Data Part One|
|Apr 25||Concepts of Big Data Part Two|
|May 2||Market drivers for IoT|
|May 9||Choosing a model – what technique to Use?|
|May 16||Use Cases and IoT datasets (these will continue throughout the course)|
|May 23||Time series and NoSQL databases|
|May 30||Streaming analytics part One|
|June 6||Streaming analytics part two|
|June 13||Deep learning part one|
|June 20||Deep learning part two|
|June 2 7||Machine learning algorithms – part one|
|July 4||Machine learning algorithms – part two|
|July 11||Mathematical foundations – part one|
|July 18||Mathematical foundations – part two|
|July To Dec 31||Project|
|Week 0 Mar 15||Orientation, introductions, Personal learning plans, Platform signup|
|Week 1 mar 21|
|Apr 4||Intro to R, Installations, Basics of R|
|Apr 18||Data Frames in R & Tabular Data|
|May 2||Data Processing & Data Visualization in R|
|May 16||Scala basics|
|May 30||Spark batch processing I|
|June 13||Spark Batch Processing II|
|June 2 7||Spark SQL|
|July 11||Spark Streaming|
|July To Dec 31||Projects|
Contact us at email@example.com to signup