The world is filled with a huge amount of data. In the past, data was not seen as something valuable, but today, it is used to make important decisions across all industries.

This is why every company is projected to have a data department of its own in the near future.

Data is used for various purposes. For example, when you download apps to your devices, you agree to terms and conditions where data is collected with consent.

This data is used by the app company to improve customer experience and refine marketing strategies.

Data science is a relatively new term that was introduced in 2001. Many think this profession is only valued and recognised in western countries like the United States, but this is not the case.

Malaysia is in dire need of data-science professionals, one of the reasons being that school leavers are unaware of this profession and its demands.

Here are three myths surrounding data science in this country and its long-term prospects.

Myth #1: Data science graduates are not in demand in Malaysia

According to the Human Resources Development Fund (HRDF), there should be at least 20,000 data professionals and 2,000 data scientists by 2020. There are, however, just over 14,000 data professionals in Malaysia to date.

The country also requires 12,000 experts on big data, which concerns data sets too large or complex to be dealt with by traditional data-processing software.

This is one of the reasons Malaysia has resorted to hiring data-science professionals from the US.

Myth #2: Data science professionals require excellent computing skills

By definition, data science means having computing and statistical skills. But there are other important talents one needs to possess to be a high-quality data-science graduate.

According to Lim Kai Jie, a data scientist at Celcom, one needs to be a good storyteller with a deep understanding of machine learning and statistics to excel in the profession.

“You need to be able to present the data insights with a good storyline to get the message across to the audience,” he says. “Your biggest challenge would be having to explain concepts in a simple way without technical jargon.”

Kai Jie advises aspiring data scientists to keep a creative mind when working on problems. “Sometimes you have to take a step back and rethink your approach when things aren’t working out.”

Myth #3: With a data science degree, you can only become a data scientist

Studying data science does not mean you are obligated to become a data scientist. Depending on your interests and strengths, you can also be a data analyst or a data engineer. Though the terms are used interchangeably even by hiring companies, the roles and job scopes are different.

“A data engineer gathers data from multiple sources and performs ETL – extract, transform, load – before storing it in a database,” Kai Jie explains.

“If you have solid knowledge of database design and management, you can consider becoming a data engineer.

“But if you are analytical and have a good understanding of statistics, being a data analyst or data scientist would work for you.”

He says data visualisation and machine learning are a good start as these are less complex but essential skills for future data scientists.

Students do not necessarily have to enrol in a data-science programme or specifically obtain a degree; other courses like computer science and software engineering also offer pathways into this field.

Afterschool.my is Malaysia’s no 1 higher education website that provides helpful information on courses, institutions and scholarships for students transitioning from secondary school to university level.