Data Scientist vs Data Analyst vs Data Engineer

Data science is rapidly emerging as a key area of growth in Australia. In a 2018 study by Deloitte, the data science workforce was shown to have expanded to over 300,000 while maintaining an annual growth rate of 2.4%. Data has become such a valuable corporate currency that those with formal recognition of their data science skills, like through an online Graduate Certificate in Data Science (Applied), have a choice of exciting career opportunities.      

Within the field of data science, there are several specialisations to choose from, each with unique opportunities and benefits. Explore the key differences between some of the most promising career pathways in the industry.
 

Data Scientist 

A data scientist is an analytical problem solver who is responsible for developing, implementing, and testing hypotheses in order to devise and present data-led solutions for business challenges to key stakeholders.

To work as a data scientist, individuals need to possess strong technical and mathematical abilities such as machine learning, statistical modelling, and big data, and as well as advanced programming skills. In addition to this, strong communication and presentation skills make data scientists great storytellers as they are able to break down complex findings and present them from a business perspective. 

Data Analyst 

Data analysis is a discipline within the broader field of data science. A data analyst’s responsibilities can vary across different industries, but their primary task is to gather, review, and analyse data to identify data-led insights which guide and inform critical business decisions.

According to Forbes, data analysts need both soft skills such as business acumen, communication skills, stakeholder management skills and presentation skills, as well as hard skills like technical know-how, critical thinking and data visualisation skills.

Data Engineer 

Focused primarily on the infrastructure and architecture used in data generation, data engineering is also considered a specialty within the field of data science. Data engineers are essentially the backbone of data science, as they’re responsible for designing and developing information systems.

Research into the required technical skills for data engineers outlines key skills such as statistical analysis and modelling, Data warehousing solutions and predictive modelling, machine learning, and data mining.
 

Of course, you’ll also need the right skills and qualifications to pursue a career in these fields. The University of Adelaide’s online Graduate Certificate in Data Science (Applied) can help you formalise your experience and advance your career, giving you an advantage in the increasingly competitive data science industry. For those who do not hold a bachelor’s degree or meet the Mathematical Methods entry requirements for the online Master of Data Science (Applied), the Graduate Certificate in Data Science allows students to commence the first four courses of the Masters while completing MathTrackX simultaneously. If you have any questions about our Data Science programs, make an appointment to speak with an advisor today.

*QS Graduate Employability Ranking 2020.