Data science and data analytics have become two of the most sought-after career paths in recent years. However, many people are still confused about the differences between these two roles and which one they should choose.
In today’s data-driven world, there’s a growing demand for skilled professionals who can extract meaningful insights from complex data sets.
What is the difference between data science and data analytics?
Data science involves using various statistical and machine-learning techniques to analyse complex data sets and derive insights to help businesses make informed decisions. Data scientists are responsible for developing algorithms, building predictive models, and identifying patterns and trends in data.
On the other hand, data analysts focus more on collecting, processing, and visualising data to help organisations make sense of it. They use tools like SQL, Excel, and Tableau to analyse data and present their findings to stakeholders.
While both roles involve working with data, the two fields require different skill sets and approaches to problem-solving. With data science, you need a strong foundation in mathematics, statistics, and programming, while data analysis focuses more on data visualisation and communication skills.
Infographic Designed By UNSW Online
What salary can I expect?
However, there is also some overlap between the two roles, and many organisations use the terms interchangeably. There’s also not much to choose between both careers regarding salary. Online platforms like UNSW Online have identified the difference between careers and their average salary. A data scientist’s average salary is about $103,117, while a data analyst may earn $116,018.
If you’re considering a career in data science or data analysis, it is important to understand the differences between the two roles and which one would be a better fit for your skills and interests.
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