Data science and analytics

Data scientists and analysts typically gather and transform raw data into more meaningful information that can be used by organisations to improve and develop business.

Although the roles of data scientists and data analysts may overlap, there are some distinctions between the two. Data scientists develop tools and methods to gather data, creating algorithms and systems to mine data, make predictions and solve complex problems. Data analysts generally analyse existing data, interpreting and communicating the data to facilitate strategic decision making. Both are likely to use programming and database querying languages, such as R, Python and SQL. Data scientists need a higher level of proficiency in programming, together with knowledge of machine learning, AI, data engineering and the ability to tackle more complex problems; they are more likely than data analysts to have a postgraduate qualification. For more on the different roles, see the profiles in Key resources below.

With more businesses relying on data to make decisions, data scientists and analysts are key in helping companies grow and develop. While the sector attracts graduates mainly from STEM subjects including mathematics, computer science, and engineering, there are also openings for those from a non-tech background who have the passion and aptitude to acquire the relevant skills. This career path offers competitive salaries and exciting opportunities for growth, with roles available across a range of industries and organisations.