fbpx
Your well-being matters. In response to COVID-19, all our programmes are now shifted online/remotely.

Just like the title suggests, we are here today to debunk the differences between data analyst and data scientist – two very similar roles but yet so different. Its key difference lies in what they do with their data. A data analyst focuses on answering questions that help businesses in better decision making, while a data scientist focuses on discovering new questions that can help drive innovation for the company. Now you have a brief overview, let’s dive deeper.

Data analyst 

Part of a data analyst job requires them to act as a data translator. They receive and analyse information before communicating it in a digestible format for us to understand.

Besides ensuring effective communication through visuals, they are also in charge of studying the data. They take their existing datasets and perform statistical analysis to find answers to some of these questions. Why did sales increase in quarter 3? Would the marketing campaign perform better in other countries or cities as compared to the current promotion? Which store is performing better and why? 

Before they can arrive at their answers, there are certain skills and knowledge needed. Here are a few examples: SQL / programming knowledge, adobe and google analytics, reporting and data visualisation. 

In summary, a data analyst helps businesses to break down complex numbers and statistics into layman terms to make efficient business decisions while seeking answers to big questions. 

Data Scientist

Now we understand that a data analyst acts as a translator and an answer seeker, how about a data scientist? A scientist is often described as an expert in their area of interest. In this scenario, a data scientist is an expert in seeking and discovering questions which is in contrast to a data analyst who answers them. Their role entails combing through massive datasets to uncover insights and establishing potential trends. 

To be qualified as one, you need to have in-depth programming knowledge of SAS/Python/R as data mining is required in this role. Apart from coding languages, you should be equipped with machine learning and deep learning principles. 

Comparing both roles, a data scientist will encounter much heavier coding due to the specifications of the role. Overall, you can say that a data scientist is a question seeker and a data analyst is an answer seeker. 

So there you have it, the key differences between the two jobs! If you wish to develop your skills further in either of the roles, you can check out our bootcamps on data analytics and data science. See you in class!