It’s easy to feel a little lost in the vast world of data where roles are intertwined and differences are marginal. Before embarking on a career in data science and data analytics, it’s important to first be familiar with what each role entails; they may sound and even seem similar on the surface, but both possess nuanced differences that may or may not suit your interests or career aspirations. If you’re planning on enrolling in data science or data analytics training in Singapore, here are the key differences between those two paths to keep in mind.

Job Scope

Although both data scientists and data analysts deal with big data, the key difference lies in how each occupation utilises that data. Data analysts examine large amounts of data to identify critical trends and develop charts and presentations that translate data into information that organisations can understand. On the other hand, data scientists are responsible for designing and constructing new data modelling and production processes using prototypes, algorithms, predictive models, and custom analysis. In short, data analysts use data to solve problems while data scientists create new ways to collect accurate data. If being a data “detective” is something you prefer, then a career in data analytics in Singapore could suit you better.

Interests

Data analytics is a profession steeped in programming, numbers, and statistics, so it certainly is a nudge in that direction if your interests lie in these fields. Data analysts function as the focal point of their organisations’ data, and therefore work almost exclusively in databases to extract relevant data from different sources in order to make sense of it. Additionally, you should also have a strong understanding of the industry you’re in, as every sector requires data analysts, from gaming to travel, to healthcare. Nevertheless, all data analytical roles require at least the same base set of skills across industries, such as SQL, that will be taught in data analytics training in Singapore.

Data scientists are more involved in data on a macro level, laying down the groundwork for more specific analytical work by asking the right questions and building the appropriate algorithms to solve complex business problems. This is also why data scientists are required to have a keen interest in business.

Required Skills

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As data analysts study data more intently and in greater detail, and are also responsible for presenting their findings, they are required to possess skills like data mining/warehouse, R or SAS, SQL, database management and reporting, and statistical analysis. You will be fine-tuning these skills during your data analytics training in Singapore.

On the other hand, data scientists work more closely with algorithms and predictive modelling, which is more programming-heavy. This is why they are also required to be proficient in a number of programming languages, such as Java and Python, on top of standard data mining and analytical skills. Data scientists should also be familiar with machine learning and software development.

Career Path

Ultimately, data science is an umbrella term that encompasses data analytics amongst other roles. This is also reflected in both career paths, with data analysts often making the transition into data science as their trajectory typically plateaus after 10 years. This is when they pick up an advanced degree and additional programming skills in order to take on a developer or data scientist position, as the latter is regarded as a more senior role and therefore pays better. However, the seemingly more long-winded career progression does come with a huge advantage, as the additional experience in data analytics could shape you into a more versatile and multi-faceted data scientist in the long run. 

To get started on data analytics training in Singapore, do check out our website for more information.