Research reveals the skills needed to find work as a data scientist Credit: Craig Anderson via Flickr

Research reveals the skills needed to find work as a data scientist

Data scientists with expertise in Python, R or SQL and a Master’s degree or higher are those most likely to succeed in the profession, according to new research. 

Educational career website 365 Data Science collected data from 1,001 data scientist profiles on LinkedIn to understand the characteristics likely to land what Harvard Business Review once called "the sexiest job of the 21st century".

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The typical data scientist

The typical data scientist on LinkedIn is a male (71 percent of profiles) who speaks at least one foreign language and holds either a Master's degree or a PhD. He has spent two years in the profession, and a total of 4.5 years in the workforce.

Globally, 53 percent of profiles work in Python or R programming languages, but in the UK SQL is slightly more popular than the latter.

"R and Python have been gaining popularity in recent years," says Iliya Valchanov, one of 365 Data Science’s co-founders.

“It may be the case that in the US they're adopting these languages faster than in the UK, because there is a trade-off between stability and innovation with languages."

Read next: How to get a job as a data scientist

Other popular programming languages in the UK include Matlab (8.9 percent of profiles), Java (7.9 percent), C/C++ (7.9 percent) and LaTex (7.2 percent).

Hadoop skills are more prominent (11.7 percent) at Fortune 500 companies processing big data, but not at smaller companies (2.7 percent) working with lesser volumes of data.

Two positions prior to their current role, the average data scientist was an analyst (19 percent), an IT specialist (16 percent), or already a data scientist (14 percent).

Academic qualifications

The typical data scientist studied computer science (20 percent of profiles), statistics and mathematics (19 percent), or economics and social sciences (19 percent). Almost half (40 percent) of them have also taken field-related online courses, with an average of 3.33 certificates on each LinkedIn résumé.

There were fewer profiles with PhDs or specialist data science degrees than 365 Data Science had expected, a finding the company attributes to the recent rise of the field.

"The main issue with data science is that it's so new that universities are not prepared for the demand," says Valchanov.

Read next:  Best data science platforms

PhDs are of more value to data scientists in the UK, 37 percent of whom have the qualification, compared to 27 percent worldwide and 26 percent in the US. Valchanov believes this is due to differences in culture and demand.

"In Europe, PhDs are more highly valued," he says. "In the USA, the demand for data scientists is higher because the country is bigger as a whole, and the population is bigger and more companies are located in the USA.

"So in a way, in the UK the supply is much higher than the demand, so they're looking for the best-qualified people."

The universities that placed highest in The Times' world rankings were well-represented in the profession, but so were those universities that did not rank at all. Around a quarter (28 percent) of data scientists graduated from a university in the top 50, a similar figure to the 25 percent whose universities weren't even in the top 1,000.

Valchanov says that graduates from the lower or unranked universities were taking more online courses to "catch up to those other guys who went to the top 50 schools."

Sector needs

The industrial and technology sectors are the most popular routes for data scientists in the UK and abroad, but the opportunities in other areas differ significantly across countries.

In the UK, the strength of the financial sector makes it an attractive area of employment and the source of work for 21 percent of data scientists, versus just 13.5 percent in the US and 8.3 percent in India.

Read next: How to use data scientists and machine learning in the enterprise

"Based on where a person resides, they can take a look at the industries hiring data scientists in their country, and specialise in languages and skills that are particular for that industry," says Valchanov's fellow co-founder Nedko Krastev.

"For example, if I am in the UK, I would certainly want to learn how to apply Python for finance, and a person should definitely be aware of who the potential employers are."

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