An insight into the role of a Data Scientist

Data scientist is the hottest selling controversy in the IT industry right now. This has been one of the most necessary skills, and the sexiest job of the 21st century.

The question to follow is: why?

Given the demand-driven nature of this industry, you need to keep evolving with it. Every day, so much data is being produced, and this data can be used to change the structure of every industry in this world, and this is where they need a data scientist, which thus, makes this the sexiest job of the 21st century.

But, what does a data scientist really do?

With the introduction of today’s world to big data, the focus shifted to its storage and the processing of this data. While the tools like Apache Hadoop, MicrosoftHD insight, etc. solved the problem of storage of the data, the focus shifted to processing and working with this data. And that is what a data scientist has to do- analyse the data and interpret it to produce meaningful insights with it.

Sounds really simple, doesn’t it?

WELL, not so much!

The whole process of collecting the data, cleaning it, applying algorithms for mining of this data, analysis of this data and it’s interpretation, to develop an insight which actually answers the problem- is precisely what data scientists have to do!

Good command over programming and a high data intuition is the primary required weapon. While being acquainted with Hadoop or hive is not a necessary skill, a data scientist, in all probability, will end up acquainted with this skill set.

To put it simply:
“A Data Scientist is better at statistics than any software engineer and better at software engineering than any statistician.” ― Josh Wills, Director of Data Engineering at Slack

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They have to code their way to fetch and visualise the data, and once that is done- they have to do ALL THAT MATH!

To break this whole process into categories, we have:

Data collection: Data collection is the process of gathering and measuring data, information or any variables of interest in a standardised and established manner that enables the collector to answer or test hypothesis and evaluate outcomes of the particular collection

Data Visualization: To communicate information clearly and efficiently, data visualisation uses statistical graphics, plots, information graphics and other tools. Numerical data may be encoded using dots, lines, or bars, to visually communicate a quantitative message. Useful visualisation helps users analyse and reason about data and evidence. It makes complex data more accessible, understandable and usable. This uses tools like Tableau, plotly, RAW etc.

Data Analysis: Data analytics focuses on processing and performing statistical analysis of existing data sets. Analysts concentrate on creating methods to capture, process, and organise data to uncover actionable insights for current problems, and establishing the best way to present this data.

And if this doesn’t explain why data scientists have been labelled as the Unicorns of the IT industry, probably nothing will!

So, that is all you need to know about data science as a career. To put all of this into perspective, Coding ninjas has curated a course which covers the A to Z of data science, starting from the fundamentals, covering up all the concepts involved in visualisation, gathering and analysis of the data.

For further details on this course, you can check out the course curriculum here.

To read more about Data Science, click here.