Big Data/Analytics

8 key skills and traits data scientists need

Analytics Amy-jo Crowley

14:08, August 14 2014

Analytics

The demand for data scientists is reaching big data levels.

A job title that barely existed three years ago has now become one of the hottest corners of the hi-tech labour market. With Harvard Business Review calling data science the "sexiest" job of the 21st century, data scientists are set to be high in demand.

So what are the skills required by a data scientist?

1. Programming skills

To work with big data, strong skills in programming and scripting languages are a must. Working with information sets so big, programming skills that are currently in demand include being able to program in statistical languages like the language of SAS and the open source R, as well as computer languages like Java, C# and Python.

2. Database skills

Perhaps less obvious is the need for skills in databases and other data sources, according to Jason Stamper, analyst, Data Platforms & Analytics at 451 Research. This includes data in traditional relational databases from companies like Oracle, IBM and Microsoft, to newer data architectures like Hadoop, and even social media data feeds and their respective application programming interfaces (API's).

He told CBR: "As the range of data sources inside and outside the typical enterprise has grown, good data scientists will have an understanding of the nuances of different data formats."

3. STEM

It's also easier for someone with a degree in STEM (Sceince, Technology, Engineering and Mathematics), preferably with an advanced degree in statistics, mathematics, analytics or machine language. Those with a PHD have further advantage that would help them learn the computation elements of data science.

4. Domain Knowledge

The ability to help business leaders to answer key questions and even help them determine which questions need answering requires domain understanding. Business domain expertise and knowledge helps the data scientist understand the problem at hand and how to measure it.

5. Visual design and communication skills

A data scientist has to be a good storyteller as their goal is to present their findings to a less technical business audience. Visualisations can help with this process as it's easier to persuade someone with an idea when there is a big picture or dashboard.

6. Curiosity

Curiosity, innovative thinking and a willingness to challenge the status quo are essential perquisites to discovering relationships between data and testing solutions.

7. Focus

Data scientists must be able to remain focused for extended periods of time while designing and testing solutions. They must be persistent in testing regardless of the amount of failed procedures or time it takes to come up with the best result.

8. Teamwork

451's Stamper recognises that the above skills and traits are quite varied and few candidates will have more than a few of them.

"For that reason, we think that the best approach today is for companies to assemble teams of data scientists with different areas of expertise," he says.

"But it's also why we are hearing that those who come closest to having all of the above skills are in particular demand today: teams tend only to be as good as their weakest link, and introduce yet another requirement - teamwork!"



Source: Company Press Release

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