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 [email protected]

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 [email protected] 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 [email protected]

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.