How Data School Australia is balancing technical and soft skills development

A true data professional is often described as someone who can balance technical competency with business know-how and the softer skills or being able to listen and communicate effectively.

It’s a rare requirement, but one the UK-based Data School is striving to instil in its graduates, including the first cohort who were recently welcomed into Data School Australia.

Founder, Tom Brown, told CMO Data School was created as an initiative of the data specialist company, MIP, in response to a huge data skills shortage, particularly with the software products, Tableau and Alteryx.

To date, it has graduated eight cohorts of students in the UK, with two more progressing through the program, and an 11^th group currently being recruited. With more than 100 applications for every available place, Brown said Data School is highly selective in terms of who it brings on board.

“It doesn’t just find the typical people you might expect on a graduate training program,” he said. “We are looking for people who are passionately interested in working with data.”

One of the requirements for admission is for would-be students to submit an example of something they have worked on in the data space that interested them, presented in Tableau.

“That tends to filter out a bunch of people who think they can just send us their CV,” Brown said.

Many of the accepted candidates come from maths and science backgrounds, with a surprisingly high representation of chemistry graduates. Many have completed a Masters degree or some other form of postgraduate education, and most have accumulated at least a few years of work experience.

“That tends to give us people who have already got a few of the softer skills that we are turning to enhance, so they are not frightened by the workplace,” Brown said. This is particularly useful given students are set real-world challenges to work on.

“Client bring in the data, set us a challenge, and we spend the week working on it and then play it back to them,” he said. “There have been some late nights and stressful nights. They have to work in a team with all eight of them. And somehow they pull it together every week.”

Finding the right mix of skills

Some of the most critical attributes of a good data professional, however, are ones Brown said are the most difficult to test for – adaptability and flexibility.

“It is something we have found, but not by looking for it,” Brown said. "My favourite way of interviewing people is to ask them what other things they are good at, such as are they good at tennis, or a chess champion. And I am hoping those things are more difficult than the thing we are trying to teach them.”

It’s not surprising then that one of Data Schools graduates was a Hungarian chess and pool champion.

“To become a national chess champion requires a great deal of problem solving, and an understanding that failure is a good way of learning,” Brown said. “They are really learning how to learn something. If you are in the technology world and you aren’t really adopt at learning new technology then you won’t be in the technology world for very long. A couple of years is perhaps the best you can hope for.”

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While the ability to learn is a key requirement for students, Brown is not satisfied with Data School just reaching the students it works with directly. Hence he is also keen to see his graduates master the softer skill of being able to take their knowledge and train others within the organisations where they find work.

“Part of the soft skill is about becoming a champion and evangelist for these products, and going out and training others by running interest groups and doing things, even on the weekend, to support others who might want to get into this,” he said.

For example, it was a Data School graduate that set up the Data Plus Women initiative in the UK, which now has 1000 members globally who are supporting women in learning data skills and getting into a career.

The ability to teach is also reflected in the tasks that many graduates are set to once they leave the Data School. In the case of client, Jaguar Land Rover, Brown said it had a desire to create a team of 40 data professionals, but could not find anyone who could create it. The carmaker took four Data School graduates and set them on to the task.

“Within six months our four guys trained their 40,” Brown says. “And we left them with a strong analytics team that now provide services across the whole business.”

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