Data shift: How big data continues to impact organisations


  • TECH
  • Thursday, 28 Aug 2014

Insightful: Data analytics continues to grow in importance for organisations as it helps them to unlock useful information from their corporate data.

A viable career 

As data analytics continues to grow in demand within the corporate world, the need for experts within this field will consequently increase as well. Hence, Deepak believes it is a good time to consider a career as a data scientist.

“The salaries that data scientists get nowadays have gone through the roof,” he says. “It’s a very difficult skill set to find, and has extremely high value.” 

Deepak defines a data scientist as a “statistician who is better at software programming than most software programmers or a software programmer who is better at statistics than most statisticians”. 

“It’s a combination of the two,” he says. “One of the skills a data scientist needs to have is the ability to code. By code, I don’t mean write huge programs. It’s more of data management: attaining the data and then running a model on that data.” 

The role of a data scientist, however, isn’t really new. Traditionally, Deepak says those in this role used to be labelled as quantitative analysts, data miners or sometimes even statisticians. 

“The use of the term ‘data scientist’ is a sexier alternative which aims to attract younger people into the profession,” he explains. “I think a lot of universities are also getting in on the act by offering courses on business analytics.” 

For instance, SAS currently has a partnership with Sunway University whereby the institution was given the rights to offer its Bachelor of Information Systems students the opportunity to specialise in the area of business intelligence and data mining.

Deepaks believes that those from the younger generation have great potential to excel as data scientists as most of them are not afraid to fail. 

“While accuracy is important for statisticians, it’s good for analysts to be willing to fail. The idea is to fail faster and then to go on to build new models very quickly,” he says.

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