Your money-making expert. Financial journalist, TV and radio personality.
The advent of big data has transformed the world, and companies across all industries have recognised the value of data-driven insights. As a result, data science and analytics have become some of the most in-demand skills in today’s job market. However, the roles and responsibilities of a data scientist and a data analyst can confuse some. Granted, they may seem similar at first glance, but the two roles have distinct differences, and employers have different expectations of each.
A data scientist analyses complex data sets to identify patterns, insights, and trends. They utilise statistical and machine learning techniques to develop models and algorithms that can help solve business problems. They also work closely with other teams to develop and implement data-driven strategies. A data scientist should have a strong foundation in statistics, programming, and data visualisation for employers. They should be able to communicate their findings effectively to stakeholders and have a good understanding of business operations.
On the other hand, a data analyst’s primary responsibility is to analyse data and extract actionable insights from it. They are expected to have strong skills in data cleaning, data visualisation, and data analysis techniques. Their main goal is to help businesses make data-driven decisions by providing insights from the data. A data analyst must have a strong statistics, management, and analysis foundation. Online platforms like UNSW Online have identified the difference between both careers to give aspirants a good idea of what potential employers expect.
In this context, job seekers need to understand the differences between a data scientist and a data analyst and the unique skill sets and knowledge required for each role. Employers have different expectations for these two roles, and understanding them can help job seekers better prepare for and succeed in their chosen career path.
Infographic Designed By UNSW Online