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Saturday, 25 February 2017

A Developer's guide to Exploring and Visualizing IoT Data

The value of IoT can be found within the analysis of data gathered from the system under observation, where insights gained can have direct impact on business and operational transformation. Through analysis data correlation, patterns, trends, and other insight are discovered. Insight leads to better communication between stakeholders, or actionable insights, which can be used to raise alerts or send commands, back to IoT devices.

A good course to get good grip on Exploring and Visualizing IoT Data would be Coursera's A developer's guide to Exploring and Visualizing IoT Data  

See Also

While the course suggested previously would be a good introduction to Apache Spark. It would be worth checking out Romeo Kienzler's book on Mastering Apache Spark which gives a more detailed and in depth knowledge on Apache Spark

What this Course is about?


With a focus on the topic of Exploratory Data Analysis, the course provides an in-depth look at mathematical foundations of basic statistical measures, and how they can be used in conjunction with advanced charting libraries to make use of the world’s best pattern recognition system – the human brain. Learn how to work with the data, and depict it in ways that support visual inspections, and derive to inferences about the data. Identify interesting characteristics, patterns, trends, deviations or inconsistencies, and potential outliers. The goal is that you are able to implement end-to-end analytic workflows at scale, from data acquisition to actionable insights. Through a series of lectures and exercises students get the needed skills to perform such analysis on any data, although we clearly focus on IoT Sensor Event Data.

What this Course will do for you?

 After completing this course, you will be able to:
 • Describe how basic statistical measures, are used to reveal patterns within the data 
• Recognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers. • Identify useful techniques for working with big data such as dimension reduction and feature selection methods 
 • Use advanced tools and charting libraries to: o Automatically store data from IoT device(s) o improve efficiency of analysis of big-data with partitioning and parallel analysis o Visualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling)

Pre-requisites

In order to complete this course, the following technologies will be used: (These technologies are introduced in the course as necessary so no previous knowledge is required.) 
• IBM Watson IoT Platform (MQTT Message Broker as a Service, Device Management and Operational Rule Engine)
 • IBM Bluemix (Open Standard Platform Cloud) 
• Node-Red 
• Cloudant NoSQL (Apache CouchDB) 
• ApacheSpark 
• Languages: R, Scala and Python (focus on Python) This course takes four weeks, 4-6h per week

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