Edge Analytics

Introduction to Data Science

June 3, 2017 Analytics, Big Data, Big Data Analytics, Big Data Management, Cloud Computing, Cold Path Analytics, Data Analytics, Data Collection, Data Hubs, Data Science, Data Scientist, Edge Analytics, Emerging Technologies, Hot Path Analytics, Human Computer Interation, Hype vs. reality, Industrial Automation, Internet of Nano Things, Internet of Things, IoT, IoT Devices, Keyword Analysis, KnowledgeBase, Machine Learning(ML), machine-to-machine (M2M), Machines, Predictive Analytics, Predictive Maintenance, Realtime Analytics, Robotics, Sentiment Analytics, Stream Analytics No comments

We all have been hearing the term Data Science and Data Scientist occupation become more popular these days. I thought of sharing some light into this specific area of science, that may seem interesting for rightly skilled readers of my blog. 

Data Science is one of the hottest topics on the Computer and Internet  nowadays. People/Corporations have gathered data from applications and systems/devices until today and now is the time to analyze them. The world wide adoption of Internet of Things has also added more scope analyzing and operating on the huge data being accumulated from these devices near real-time.

As per the standard Wikipedia definition goes Data science, also known as data-driven science, is an interdisciplinary field about scientific methods, processes and systems to extract knowledge or insights from data in various forms, either structured or unstructured, similar to data mining.”.

Data Science requires the following skillset:

  • Hacking Skills
  • Mathematics and Statistical Knowledge
  • Substantive Scientific Expertise

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[Image Source: From this article by Berkeley Science Review.]

Data Science Process:

Data Science process involves collecting row data, processing data, cleaning data, data analysis using models/algorithms and visualizes them for presentational approaches.  This process is explained through a visual diagram from Wikipedia.

Data_visualization_process_v1

[Data science process flowchart, source wikipedia]

Who are Data Scientist?

Data scientists use their data and analytical ability to find and interpret rich data sources; manage large amounts of data despite hardware, software, and bandwidth constraints; merge data sources; ensure consistency of datasets; create visualizations to aid in understanding data; build mathematical models using the data; and present and communicate the data insights/findings.

They are often expected to produce answers in days rather than months, work by exploratory analysis and rapid iteration, and to produce and present results with dashboards (displays of current values) rather than papers/reports, as statisticians normally do.

Importance of Data Science and Data Scientist:

“This hot new field promises to revolutionize industries from business to government, health care to academia.”

The New York Times

Data Scientist is the sexiest job in the 21st century as per Harward Business Review.

McKinsey & Company projecting a global excess demand of 1.5 million new data scientists.

What are the skills required for a Data Scientist, let me share you a visualization through a Brain dump.

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I thought of sharing an image to take you through the essential skill requirements for a Modern Data Scientist.

So what are you waiting for?, if you are rightly skilled get yourselves an Data Science Course.

Informational  Sources:

Introducing Azure IoT Edge

May 13, 2017 .NET, Analytics, Artificial Intelligence(AI), Augmented Reality, Azure, Azure IoT Suite, Cloud Computing, Data Analytics, Edge Analytics, Embedded, Emerging Technologies, Event Hubs, Industrial Automation, Intelligent Cloud, Intelligent Edge, IoT, IoT Edge, IoT Hub, Linux, Mac OSX, Machine Learning(ML), Microsoft, Robotics, Self Driven Cars, Stream Analytics, Windows, Windowz Azure No comments

During Build! 2017 Microsoft has announced the availability of Azure IoT Edge, which would bring in some of the cloud capabilities to edge devices/networks within your Enterprise. This would enable industrial devices to utilize the capabilities of IoT in Azure within their constrained resources . 

With this Microsoft now makes it easier for developers to move some of their computing needs to these devices.  Edge devices are mostly having small foot print based to high end machines within your company network.

The essential capabilities to be supported by Azure IoT edge  include:

  • Perform Edge Analytics (a cut down version of Azure Stream Analytics)- Instead of doing analytics in cloud developer/implementer can move the basic cloud data processing and analytical capabilities to Edge Device. Run your machine learning algorithms in Edge device and take predictive analytics steps.
  • Perform Artificial Intelligence processing at edge device itself. Availability of Microsoft Cognitive Service on edge device would bring in whole lot of automation capabilities. Imagine Alexa/Siri working without internet connection, it should be able to provide you reminders etc.
  • Perform RealTime Decision making locally based on predefined rules.
  • Reduce bandwidth costs
  • Connect to other Edge devices and legacy devices within the constrained/corporate network.
  • Deploy IoT solutions to Edge Device from Cloud and provide updates as needed.
  • Operate offline without the need of real-time internet connectivity or intermittent connectivity. Doesn’t have to rely on Cloud to provide commands for processing, can do offline data capture and processing of information from other devices connected and take decisions without the need to rely on a connected cloud service.

Azure IoT Edge enables seamless deployment of cloud services such as:

Along with sharing the image represents Azure’s Enterprise Digital Vision, we will discuss about the same in later sessions:

Digital-Enterprise-Vision_png

Getting Started & More information: