Employability in general, and in data science in particular, means your skills must adapt to business needs.
Thomas Davenport and D.J. Patil described data scientist as “The Sexiest Job of the 21st Century” in 2012. Since then, understanding has grown that businesses across industries need employees who can work with and understand large amounts of data. The data science concept has been widely discussed, and new terms have emerged to discuss people working in data science, including unicorn (= a mysterious and rare creature) for those who combine the skills of statisticians, programmers and business experts.
But what is actually required of a data scientist? And where can we find these ‘unicorns’? At SAS Institute, we focus on issues like this in our work on the latest in Analytics and Data Management. We also attend events like SAS UK’s Data Science and Advanced Analytics Forum, which this year focused on training future data scientists.
50% mathematician, 50% data log and 50% business expert
Data science is about confidence with data, both collecting and processing, but also in daring to challenge and ask questions about data. At the SAS Data Science and Advanced Analytics Forum, which brings together universities and businesses, universities highlighted how they focused on providing students with mathematical and statistical competencies. They spiced this with an understanding of how data scientists integrate and use software to provide answers. From industry, however, the focus was on insights into business areas, the ability to communicate ideas and solutions to a wide audience, and collaboration across different business departments. The match between the three areas results in a profile consisting of 50% mathematician, 50% data log and 50% business expert. There is clearly a challenge facing both universities and businesses in finding candidates who fit this bill.
Not a single ‘unicorn’, but a “whole field full of ponies”
Practical experience from organisations like HSBC, ShopDirect and Capgemini suggests that there is unlikely to be just one data scientist doing everything, but instead a whole team who bring together different skills to solve complex issues. In other words, rather than wasting time searching for unicorns, businesses should build a ‘herd of ponies’ who together will provide data science solutions. It is, however, important that data scientists understand all three main areas, and are able to work across disciplines in a non-siloed way.Rather than searching for unicorns, businesses should build a team who bring together different skills #DataScience #sasacademic Click To Tweet
Where can we find them?
Data science attracts graduates from a wide range of fields, including engineering, economics and business education. Our Data Science Graduate program at SAS Institute Denmark welcomed nine new graduates in February 2016, with backgrounds in particle physics, chemistry, nanotechnology, civil engineering, mathematical modelling, political science and economics. Some were directly from university, while others already had a couple of years of professional experience. All of them, however, are linked by their interest in data and analysis and the skills to work analytically and mathematically with numbers and models.
The first few months of the programme at SAS Institute are about methods and data-handling, and particularly suitable tools. The students also work with real life data cases and practice their communication skills, so that they can explain the complex solutions and approaches that are part of data science.
Data Scientists and the future
The lively debate at events like the SAS Data Science and Advanced Analytics Forum show that there is a growing need for people who can process, work with and understand data. Companies and universities now face an exciting and relevant challenge in identifying and developing individuals with the skills and experience to handle data and analytics tasks. The SAS Institute in Denmark continues to place new graduates in different departments in SAS, enabling them to contribute fully. We look forward to following them and learning from their way of working.
Are #datascience education programs keeping pace with business needs?
We hosted a digital panel discussion on Twitter covering this theme. Read the highlights as a Storify: Are #datascience education programs keeping pace with business needs?