ARTIFICIAL intelligence (AI) and data science are the two main fields in the technology and economic sectors, reshaping how we approach and solve problems.

These interconnected fields leverage vast amounts of data to achieve diverse outcomes, driving the Fourth Industrial Revolution (IR 4.0).

Data science utilises data to develop models and extract insights, while the application of these models is used to automate processes, make decisions, and predict outcomes.

Generative AI (Gen AI), a subset of AI, leverages models to produce new data across various forms such as images, text, audio, and video, expanding AI’s scope from creative arts to scientific research and advancing robotics by enabling more versatile capabilities.

The big data analytics (BDA) landscape is also rapidly evolving, with the integration of AI in data science unlocking unprecedented opportunities and catalysing transformative applications across industries such as healthcare, manufacturing, finance, and retail.

Data science, blending computer science, statistics, mathematics, and domain expertise, extracts insights from vast datasets, using techniques such as data mining and predictive analytics, enhancing business efficiency, growth strategies, and scientific advancements.

According to Malaysia Digital Economy Corporation, the BDA market in Malaysia is expected to reach RM7.85bil by 2025.

This growth means that the field of BDA-related roles – such as data scientists, data engineers, data architects, data analysts, and business analysts – are getting more popular and have the highest rise in vacancies in South-East Asia.

How AI augments data science careers

The integration of AI techniques is transforming traditional data science roles, opening up new avenues in this fast-growing sector.

Asia Pacific University of Technology and Innovation’s (APU) School of Computing (SoC) senior lecturer and corporate trainer Amad Arshad said that fresh graduates now need to have a solid understanding of AI and machine learning (ML) algorithms and neural networks to excel in roles like ML engineers, data engineers, and data scientists.

“Developing a strong knowledge in computing theory with technical skillsets in programming languages, statistical analysis, ML techniques, data visualisation, and domain-specific knowledge is crucial for talents to adapt in this sector,” he said.

Data science-related jobs in demand

A typical data scientist makes somewhere around RM5,000 as a fresh graduate to about RM25,000 per month in Malaysia.

Junior data analysts typically earn between RM3,500 and RM4,500 per month, and are responsible for tasks such as collecting, cleaning, analysing, and visualising data to ensure its quality and derive valuable insights using various analytical tools.

Senior data scientists can expect a salary ranging from RM6,500 to RM15,000, influenced by factors like company size, location, and technical expertise.

Higher compensation is often offered for roles requiring skills in computer vision, natural language processing, and deep learning, where responsibilities include performing statistical analysis, uncovering insights, and developing ML models for predictive and prescriptive analytics.

Data engineers in BDA-related roles typically earn between RM4,500 for fresh graduates and RM12,000 per month, specialising in designing and constructing secure systems for gathering, managing, and storing raw data, and proficiently using scripting languages such as Python and Structured Query Language (SQL).

Gen AI’s impact on data science

The rapid evolution of data science underscores the critical importance of AI skills, enabling data scientists to extract deeper insights, navigate complex challenges, and make impactful decisions.

APU’s SoC lecturer Chong Mien May emphasised that Gen AI is revolutionising data science by automating repetitive tasks like data cleaning and extraction, thereby enhancing efficiency and accuracy while freeing up time for strategic pursuits.

Moreover, she highlighted that Gen AI enhances code quality by detecting errors and optimising algorithms, thereby streamlining the coding process.

“This integration of AI not only enhances productivity but also expands the capability of data science to uncover meaningful insights that drive innovation and decision-making across various industries,” she said.

Preparing students for the AI-driven future

APU ensures its data science students are thoroughly prepared to meet the evolving demands of AI in modern workplaces with a robust curriculum that includes core specialised modules within the data analytics degree programme.

These modules include Probability and Statistical Modelling, Text Analytics and Sentiment Analysis, Data Mining and Predictive Modelling, and Optimisation and Deep Learning.

Additionally, APU enriches its curriculum by hosting seminars and events where industry experts share insights on current AI trends and real-world challenges, ensuring students not only grasp cutting-edge technologies but are also prepared to tackle industry-specific problems effectively.

APU’s SoC senior lecturer Dr Adeline Sneha John Chrisastum highlighted that the institution actively collaborates with industry partners to deliver comprehensive real-world AI experiences for data science students.

“Through strategic collaborations, robust industry partnerships, immersive workshops, and specialised training programmes, APU sets a benchmark in BDA education, enriching student learning experiences and empowering educators to effectively cultivate the future generation of data science and AI talents,” she said.

Beyond traditional coursework, APU actively fosters practical learning through internal and external competitions, and hackathons, offering students a platform to refine their skills and test their AI and data analytic capabilities in an industry-centric environment.

Besides technical skills, students also learn critical 21st-century soft skills intended to help them keep up with the lightning pace of today’s modern markets.

According to APU’s SoC head Assoc Prof Dr Tan Chin Ike, most tech companies are looking for talents not only with strong critical thinking, problem-solving, adaptability, and presentation skills but also those with emotional and social skills, like conflict management and working well in a team environment.

“These soft skills are seen as pivotal by hiring companies, distinguishing top talents from average graduates.

“By blending theoretical knowledge, hands-on experience, industry perspectives, and essential 21st-century soft skills into its curriculum, APU equips its graduates to drive creativity and innovation, and succeed in diverse tech-centric roles,” he said.

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