Developing the data science community: generating a sense of support by Hugo D'Ulisse of SAS, DataFest Premium Sponsor
Students live and thrive in a community: the student body and its associations form a relatively tight-knit group that provides—or should provide—support and encouragement on an ongoing basis. For those away from home for the first time, this community can be a life-saver, a sounding board for ideas and a space to help each other learn. For the most part, and for most people, student years are a time of close friendships and reliable support networks.
Moving into the world of work
These strong student communities, however, can make moving into the world of work more challenging. It is, perhaps, one reason why the big graduate employers are so popular: the large number of ‘trainees’ or graduate recruits provide a ready-made community of peers and near-peers. Company mentors have often been through the system themselves, and understand some of the pitfalls and challenges.
It can be more difficult though in specialty areas like data science. There is general agreement that data scientists are in short supply, and few organisations employ many. Some of the big consulting and software houses may have more, but few would have what anyone would describe as a community. Yet a community of data specialists is essential for success. Data Science is a team game. Learning from each other is the only way to solve real business problems.
Many graduates find the jump into work challenging anyway. Employers often find that school leavers and graduates alike are ‘not quite ready’ for work: they may struggle with the time constraints, or expectations about behaviour, dress, or hours. They may also lack some of the soft skills that are so essential for working with others—this may be especially true of science, mathematics or computing graduates, where the focus of their studies has been on technical skills and individual excellence.
Making the move into work easier
Fortunately, many employers and potential employers, together with universities, are recognising that they can, and should, take some action. If students are not ready for work, then there are things that can be done to prepare them. So, while many data science courses focus on technical skills, a new Masters course at the University of Stirling, however, provides data science skills for business.
It has been developed in partnership with SAS, HSBC, the global bank, and The Data Lab, an innovation centre focusing on data science. The course, part of The Data Lab MSc programme, provides high quality teaching on analytics, using SAS software, but it also requires students to take modules in strategy and business consultancy. The students undertake a consultancy project with an external organisation, to help them develop a better understanding of how data science is used in business. This type of course ensures that students are better able to step seamlessly into work, and contribute from the first day.
It is not, however, enough to simply equip students with better ‘work skills’. Building and supporting a data science community is also essential. Collaboration and partnership have become more and more important in the world, and data scientists need to be able to share ideas, tools and techniques with their peers, and learn from others. There are a number of ways in which this can be done.
Networks, communities, and meetings
Data scientists may meet and network at a wide range of events, including hackathons and meetups. Many meetups and hackathons are sponsored by companies, and student attendance is encouraged. This enables students to start to build their networks long before graduation. Meetups often happen regularly, and are relatively local, so it is easy to meet people with similar interests in your vicinity—provided that there is already a budding community.
Other events may be bigger, set-piece events, such as DataFest in Scotland. DataFest 2018 will again be run by The Data Lab, and premier sponsored by SAS. Activities at last year’s festival included central events in Edinburgh and Glasgow and many more local ‘fringe’ events across Scotland, so it can truly be considered a national event. The event included a hackathon run by Deloitte, using data from the World Bank on financial inclusion. There was also a Data Summit with keynote speeches and workshops. With over 1000 attendees, it was a fantastic opportunity to help develop a real sense of community among data scientists.
Data science is still a relatively ‘young’ specialty. It is therefore inevitable that the data science community is not yet fully developed. However, it is important for businesses and organisations that employ data scientists to help a strong community to develop, both virtually and face-to-face in particular localities. Supporting collaboration and cooperation is likely to be key for future development of the profession in Scotland.
Words by Hugo D'Ulisse, Technical Director - Public Sector, SAS