critical-skills-for-future-data-scientists
Data Mining

Critical Skills for Future Data Scientists

With the continued explosion of data, the proliferation of complexity throughout the information management lifecycle, and the pressing need for organizations to derive revenue-generating insights from their data and information, the role of the data scientist has been thrust into the spotlight, fast becoming one of the most talked about and sought after roles in all of IT.

At the same time, the industry wide shortage of data scientists, combined with reported six-figure salaries, is creating a ripple through existing IT ranks as programmers, application developers and engineers look to climb the corporate ladder by becoming data scientists. They’re part technologist, scientist, investigative researcher, business analyst, mathematician, statistician, economist, and engineer.

Yet, despite the increasing awareness around the need for more data scientists, and despite increasing desire among existing IT personnel to develop into data scientists, there remains a dearth of individuals truly capable of filling this critical role.

So, what critical skills do IT professionals need to develop if they are going to grow into future data scientists? What type of mindset, educational background and career experience makes someone ideally suited for this burgeoning career path?

Curiosity

Data scientists must combine technical savvy with an innate curiosity to analyze huge volumes of data, and then deliver insights and answers that help solve real-world business problems. That sounds like a heavy emphasis on technical skills, and, right now, that’s true. The data scientist of today needs more of a technical bent because this is all so new, and the software is not yet at the point where those technical skills can be fully automated. But, the role will evolve as software and technology eventually catch up and sophisticated algorithms can be automated.  The intuitive and curious side of the data scientist will never be able to be automated – this is where the data scientist can add value now and increasingly in years to come.

Business Acumen

A good data scientist needs business acumen to analyze patterns in data, and draw conclusions that answer business questions. This is why mindset is just as important as skill set. Today’s data scientists can’t just be stat heads. They need to be as familiar with the business engine as they are with the data center. From profit and loss to sales force dynamics, the data scientist must have her finger on the pulse of any and all business metrics, and must be skilled at pulling actionable insights out of the patterns found within them.

Applied Science

The science community has birthed some great data scientists, as scientists are trained to theorize and then test – in contrast to the linear testing methods of IT and engineering problem solving. Data scientists should know how to search, experiment and test, which is quite a different approach from that typically taken by someone who engineers or maintains systems.

Multi-Lingual

The languages of data and application development are many; being able to speak and collaborate in many of them will help data scientists as they explore new fields and frontiers in data mining. Java, R, Scala, Pig Latin, Python and SQL can be very applicable to the data scientist role. Learning SQL and other application development languages opens doors to more opportunities for cross-functional collaboration and growth.

Access & Autonomy

Ironically, the most important things a data scientist needs can only be provided by others. Data scientists need access to the required data and the autonomy to derive insights from that data. They need freedom to think, analyze and think some more, before making recommendations based on observations and research. The data scientist has to be empowered to openly communicate with business leaders and provide the tough answers they may not always want to hear. In other words, for data scientists to truly thrive, organizational leaders must first cultivate a data-driven culture where information is shared and insights are valued.  

It’s also important to remember that the role of the data scientist represents a “right now” need. As software and technology catch up, the role will continue to evolve. The bottom line lesson for today’s data scientist is to make sure you have a broad, well-rounded set of skills so that your career can evolve too. Putting in the work to become a data scientist today will only serve you well in the future.

 

 

Mark Davis, Distinguished Engineer at Dell

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Mark Davis

Mark Davis, Distinguished Engineer at Dell

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