Data Science – Let the Data Sing
The hype is real. But let’s get past it. What exactly is Data Science? And why is it the next big thing.
Massive amounts of data are being generated every sec.
The total amount of data in the world is 4.4 zetabytes.
And this is not just the internet data. We are talking about finance, the medical industry, bioinformatics, government, pharmaceuticals, education, social welfare, retail, and the list goes on. But what’s interesting is that this data itself becomes the building blocks of data products. Your Amazon suggestions, friend recommendations on Facebook, trading algorithms in Finance and policies on Data are just a few indications of this paradigm shift in data processing.
In short, Data Science is all about taking all the aspects of life and turning them into data. Twitter datafies stray thoughts. LinkedIn datafies professional networks and so on. We have reached that point where our behavior changes the product and the product changes our behavior.
When asked ‘What is Data Science?’, Mike Driscoll, the CEO of Metamarket, answered:
Data Science, as it’s practiced, is a blend of Red Bull fueled hacking and espresso-inspired statistics.
Or as Drew Conway’s Venn diagram depicts,
Data Science is a mix of Math and Statistics, Hacking Skills and Substantive Expertise.
The term ‘Data Scientist’ was first coined by DJ Patil and Jeff Hammerbacher back in 2008. And this is when Data Scientist emerged as a job title. Anyone who possessed the hybrid skills of stats and computer science paired with personal characteristics such as curiosity and persistence was worthy of being called a Data Scientist. It is of no surprise that Harvard Business Review has declared data scientist as the Sexiest Job of the 21st Century.
In the Industry, a Chief Data Scientist sets up the data strategy for the company. And this involves setting everything up from the engineering and infrastructure for logging and collecting data, to privacy concerns, to deciding what data will be user- facing, how data is going to be used to make decisions, and how it’s going to be built back into the product.
So what domain expertise do you need to have to become a data scientist? Computer Science, Math, Statistics, Machine Learning, Domain Expertise, Communication and Presentation Skills, and Data Visualization are a few of the main domains that a data scientist must have at his fingertips. But these are just a few and these domains are constantly evolving. So it has become more important than ever before for a data scientist to constantly innovate himself and perform research simultaneously.
This is one of the major reasons why Busigence Research was established: To cater to the needs of this ever-growing sector and to provide a ‘Learn and Implement’ environment for our Data Scientists.
As exciting as this new ecosystem seems to be, it is equally alarming. Looking at the bigger picture, Data Science is bound to reduce the number of jobs. To know how to survive this paradigm shift have a look at our article: Revaluate Your Career Path.