According to Indeed’s Annual Report, the number of searches for data science jobs increased 14% last year, and postings on the platform for data science jobs grew 29% year-over-year. These statistics highlight how data has become a lucrative field to pursue, and the demand for those with related skills has risen considerably over the past couple of years.
With that in mind, we asked the judges of the 2018 DatSci Awards to walk us through some of the major challenges and innovations they have noticed in data science over the past year.
Alessandra Sala – Nokia Bell Labs
So gender imbalance in data science is a very important problem. I’m seeing a good trend. Many new students, female students, data scientists that are entering the workplace. We can still do a lot. We can still help them to be more confident and to reach out, to be helped to be more supported in the way they apply for awards and they are mentored in their career.
So from our side, it is important to be a good role model and to reach out into the community.
Andrew Mullaney – NewsWhip
The most surprising fact from our data? Definitely when we could see that Donald Trump looked like he was gonna win the US presidential election about six months in advance.
He was about 2x the engagement of Hillary Clinton in the run-up to the election.
Deepanand Saha – Pramerica
So this is something that I came across maybe a couple of years back at this stage. It goes like, there’s just .5% of all the data that we analyze, the rest is just discarded. Now things have changed, we’ve generated a lot more data in the last two years. We’ve also had a lot more data scientists come in the last two years. But that piece is not really catching up.
So I feel really excited about the opportunities that we have in terms of analyzing data and the future for data science.
I think the biggest challenge facing data scientists these days is understanding what the role of the data scientist will be in the future.
As more and more automation rolls out with the likes of Amazon Sagemaker and Google really bringing automation to bear on data science, more and more emphasis will be placed on data scientists understanding their business and understanding their business challenges. And I think adapting and moving and learning new technologies is just going to become more and more important.
Mick Kerrigan – RecommenderX
The last few months have shown us GDPR, the privacy of individuals, which is having a huge impact on how we do data science.
The sorts of considerations that we need to take into account. And it’s challenging companies both large and small to really think about how they use data.
Noelle Doody – SSE Airtricity
It’s important to close the gap on diversity imbalance in data science roles, because the more diverse a group you have, the better a solution to a particular problem you’re likely to get.
I think that the key is to try and get people involved and interested as young as possible, so that’s where groups such as CoderDojo and other community meetups and groups are really, really important.
When I left my secondary school I had a reference and it said, “Young man of intelligence, industry, and integrity.” And I think that’s probably a good way to be a data scientist as well.
Sheamus Causer – Deutsche Bank
For me, the most exciting trends, really, are not just about the technology and the tools but social impact itself. It’s hard not to feel inspired when you see the use cases that are coming through that have a very tangible impact on both our society and our environment.