Data scientist versus data analyst: What’s the difference?
We hear from a data analyst and a data scientist at Aon to learn more about the differences and similarities between the two roles.
The ability to work with data is a highly sought-after and increasingly important skill. Analysing it, understanding it and applying it each requires a unique approach. That’s what sets various data-linked roles apart. But what exactly are the differences between data analysts and data scientists?
To learn more, Siliconrepublic.com spoke to two employees – senior data analyst Beatrice Russell and data scientist Dylan Butler – at Aon’s Centre for Innovation and Analytics (ACIA).
Both Russell and Butler draw on their data expertise to inform software solutions for the insurance industry, but they spend their days on different types of work.
Russell: Data analysts tend to wear different hats and there are particular skillsets attached to each of the specialisms. There’s the statistical analysis and modelling of the data, understanding and solving conceptual and abstract problems, creating data visualisation and telling a story to communicate the finding.
You also have duties such as data manipulation and coding to automate your everyday, regular reporting. If these were the spokes of a wheel, you’d want to have a good grasp of all of these skills and maybe choose to specialise in one or two.
Industry knowledge is very important too. This informs the assumptions, type of analytics employed, isolating the key findings from the noise and quickly identifying issues and errors in the data.
It becomes important to appreciate the nuances and complexity of the problem and be able to clearly communicate that to the technical and less technically inclined stakeholders. Working effectively together in a team and with different stakeholders and specialists is important to ultimately find the simplest and most effective solutions.
Butler: Patience is very important for me; taking time to truly dissect the problem at hand. In addition, a high level of focus is required to ensure that the mind does not drift away from the overall goal.
Always ask yourself why you are doing something and how it contributes to solving the problem at hand. I recommend coupling these skills with an understanding of statistics and a knowledge of Python to form a strong arsenal to combat most problems.
One of the most important tasks for a data scientist is pre-processing the data. We don’t always get lucky with a perfectly structured dataset and most of the time the data is a complete mess. Other interesting duties include building predictive models, training custom named-entity-recognisers and identifying rich data sources, to name but a few.
Russell: Curiosity. This is critical and everything else follows from that.
