Glasgow local group meeting on professional perspectives around data science and statistics - report

On 1 December 2020, the RSS Glasgow Local Group hosted its Annual General Meeting. The event was divided into two sections. In the first one, the chair of the group, Dr Kate Pyper provided the attendees with important information regarding the management of the group. This included the number of events that took place during the year, the number of attendees each event had, and a general overview of our finances.

The second and main section of the event was a conversation between Robin Higgins and Dr Vinny Davies around professional perspectives around data science and statistics.

Robin gave an overview of the different kinds of job advertisements available for careers in data science. His talk focused on distinguishing the kinds of skills needed for each kind of these. In general terms, there are five kinds of data science jobs:

  • Data science
  • Data engineering
  • Data management
  • Data analytics
  • Reporting analytics

From the applicants’ side, knowing what is required from each of these positions allows them to determine whether they should follow through with an application and pursue a career in a particular field. However, from the company side, it is important to fine-tune the description so that the right candidates can apply. In some situations, large companies will require very specific individuals to fulfil the open roles. In others, smaller companies (start-ups, for example), will need individuals who can perform many of these roles at the same time.

In his talk, Vinny Davies talked about the specific skills needed to perform a job as a data scientist or analyst (using the above distinctions). In particular, he mentioned how much of the machine learning architecture needed for jobs in data science, comes and is very much embedded in more traditional statistical methodologies that have been in use for longer than most people are aware of. However, he made clear that, although the tools are very similar, there are different use cases and knowing when to apply 'statistics' and when to apply 'machine learning' is key for success both in the industry and in academia. In both talks, the speakers mentioned the need for strong quantitative skills as well as programming skills to succeed.

The discussion that came after the two talks was particularly enriching, since it gave the opportunity to further see the clear parallels between data science and statistics, but also where academia works in a different way to industry jobs. Robin provide some insight into the fact that a lot of companies are riding the current data-science-machine-learning hype, but do not really know what they need to meet their particular needs. This only to say that the distinction is not necessarily clear for the people who are already performing these roles in the real world.

About the author
Sebastián Martínez is a PhD student of Statistics at the University of Glasgow and secretary of the RSS Glasgow local group committee. Before coming to Scotland, Sebastián lived in Berlin where he did a master's degree in public policy at the Hertie School of Governance and worked researching global and public health financing architecture. He has a master's degree in economics, a bachelor's degree in economics, and a bachelor's degree in mathematics from the Universidad de Los Andes in Colombia, where he is originally from.
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