Hype vs. reality

Introduction to Data Science

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

[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 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.

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:

Internet of Things (IoT)–Introduction

The Internet of things (IoT) is the inter-networking of physical devices, vehicles (also referred to as “connected devices” and “smart devices”), buildings, and other items embedded with electronics, software, sensors, actuators, and network connectivity which enable these objects to collect and exchange data.

The IoT allows objects to be sensed or controlled remotely across existing network infrastructure, creating opportunities for more direct integration of the physical world into computer-based systems, and resulting in improved efficiency, accuracy and economic benefit in addition to reduced human intervention.

“ Forecasts show an expected IoT universe with between 20 and 30 billion connected devices by 2020 “

Image result for Internet of Things

[Image Source: https://www.i-scoop.eu/internet-of-things-guide/]

IoT is expected to offer advanced connectivity of devices, systems, and services that goes beyond machine-to-machine (M2M) communications and covers a variety of protocols, domains, and applications.

Some of the important IoT messaging protocols are:

AMQP(Advanced Message Queuing Protocol) – An open standard application layer protocol for message-oriented middleware. The defining features of AMQP are message orientation, queuing, routing (including point-to-point and publish-and-subscribe), reliability and security.
MQTT (Message Queueing Telemetry Transport)- or MQ Telemetry Transport is a lightweight connectivity protocol geared for IoT applications. It is based on the TCP/IP stack which uses the publish/subscribe method for transportation of data. It is open-ended and supports a high level of scaling, which makes it an ideal platform for development of Internet of Things (IoT) solutions.
HTTP/2 – Enables a more efficient use of network resources and a reduced perception of latency by introducing header field compression and allowing multiple concurrent exchanges on the same connection.
CoAP(Constrained Application Protocol) – CoAP is a web transfer protocol based on the REST model. It is mainly used for lightweight M2M communication owing to its small header size. It is designed especially for constrained networks and systems withing the Internet of Things paradigm, hence the name, Constrained Application Protocol.
CoAP mimics HTTP in terms of user visibility, and from that standpoint, reading sensor values is essentially like making an HTTP request.
XMPP(Extensible Messaging and Presence Protocol) – An open technology for real-time communication, which powers a wide range of applications including instant messaging, presence, multi-party chat, voice and video calls, collaboration, lightweight middleware, content syndication, and generalized routing of XML data.

We will go through about them in detail in later posts.

That’s all for now. Keep reading.

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