What is the ideal training and development programme for data scientists? Discuss.
This ‘exam question’ crops up time and time again in discussions with students, academics, data scientists, and across the business community. This is in large part because in a fast-moving field like data science, the quality of training and development is extremely important.
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A wide range of development opportunities
Formal training plays a huge part in supporting the development of data scientists. There is a growing number of Masters courses in data science-related and analytical subjects, including the cross-institutional quantitative techniques for economics and management masters network (QTEM). These courses provide a solid grounding in quantitative and analytical techniques.
There are also plenty of more informal courses and learning opportunities, such as training in particular tools or techniques. These are a useful supplement to formal training, not least because they allow analytics developers to concentrate on the tools that are most useful to them, and learn from experts.
On-the-job training has a role in any discussion about development, and data science is no exception. It is hard to beat practical learning, and being able to put ideas into practice. How else can you explain the popularity of hackathons and internships? For data science students in particular, opportunities to learn on the job are rare, but well worth seeking out.
Learning from others’ experience
There is another important way of supplementing formal learning, and that is mentoring. Mentoring has been around for many years — the original ‘Mentor’ was the tutor of Odysseus’ son Telemachus in Homer’s Odyssey, and the goddess Athena disguised herself as Mentor to provide wise counsel to Telemachus in his father’s absence. It is, however, often underrated as a development tool, even though mentoring relationships can have huge benefits for both parties.
Mentoring is often thought of as a relationship between senior and junior colleagues, designed to enable the more junior person to draw on the other’s experience and knowledge to improve their own skills. Many mentors will also freely admit that they have gained a lot from the relationship, including broadening their outlook, and getting a new ‘take’ on old ideas.
Mentors admit they have broadened their outlook and gained new take on old ideas #sasacademic #DataScience Click To TweetWorkplace mentoring has become more common in the last couple of decades, with many organisations putting in place formal programmes. A slightly newer version of the idea is a cross-over between the world of business and academia, with students being mentored in particular job or skill areas by those already using these skills in the ‘real world’. This helps students to improve their understanding of the jobs available, and see how their skills might be applied in practical situations. It also helps them to build a network that will support them in getting a job later.
Mentoring in data science
Data science is one of the areas where this idea is starting to take off. Data scientists remain rare, and students may find it hard to get access to information ‘from the horse’s mouth’ without help. Mentoring bridges that gap, and enables students to improve their skills and understanding of using data science in business.
As with workplace mentoring, the benefits of student mentoring are not entirely one-sided. It may sound like the students get the best of the deal, but spare a moment to think about it, and you will quickly realise that organisations are not missing out. They get access to intelligent people who are keen to develop their skills. Having a ‘sneak preview’ of the next set of graduates, and being able to build relationships with them, is a strong advantage in a competitive jobs market such as data science.
But there is even more to it than that. Internships offered to students who have built relationships through mentoring can be a way to test their ‘work readiness’ without requiring a formal job offer. Both mentoring and internships can also be a way to gently ‘improve’ students’ work readiness and ensure that they can ‘hit the ground running’ when they start. It is, in other words, a real win–win situation.
SAS University Mentors
I have been part of SAS’ University Mentors scheme for a while now. This makes me part of a network that advises on curriculum and student projects, helps to arrange guest lectures, and encourages students to develop skills in SAS products such as Viya. With over 60 Masters programs using SAS, and more than 3,500 students graduating with SAS skills, it is clear that demand for SAS University Mentors is only likely to grow, and I am very much enjoying being part of this interesting area.
