Many early-career professionals and students in Singapore often blur the lines between data science and artificial intelligence (AI). Though related, they serve different purposes: data science uncovers insights from data, while AI builds systems that mimic or surpass human intelligence. Knowing this distinction is key to making informed career choices in today’s tech-driven world. No matter what, both data science and AI professionals enjoy significant demand in the Southeast Asian country, with AI engineers receiving an average monthly base pay of 5000 Singapore dollars (SGDs) and data scientists earning an average monthly base pay of SGD 7000.
This blog will discuss the most important areas of difference between AI and data science and the common areas in which they overlap. It will also attempt to bust some myths about both data science and AI.
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AI vs. Data Science: Understanding the Core Differences
Many people ask which is better between data science and artificial intelligence, but they often overlook the fact that these two fields are related but distinct. Essentially, data science is about understanding data, while AI focuses on building systems that can act on such understanding.
Objective and Core Focus
The first area of difference between AI, ML, and data science is in their respective core focus areas and objectives. AI emphasises building intelligent systems that can mimic human intelligence, such as problem-solving, decision-making, and learning. They focus on creating machines that are capable of performing tasks independently by using logic, reasoning, and perception.
Data science focuses more on extracting insights from both unstructured and structured data by using statistical methods, predictive modelling, and data visualisation.
Technologies and Tools Used
Data science professionals are substantially dependent on tools such as R, Tableau, Python (NumPy and Pandas), Excel, and Structured Query Language (SQL) for manipulating, analysing, and visualising data. Their primary areas of focus are extract, transform, load (ETL), statistical modelling, and exploratory data analysis.
On the other hand, AI professionals use frameworks such as TensorFlow, Keras, PyTorch, and OpenCV to create deep learning and ML models.
Job Scope and Career Roles
Job scope and career roles are also significant areas of difference between AI and data science. The most prominent roles in data science are data analyst, business intelligence (BI) analyst, data scientist, and data engineer.
On the other hand, the most prominent AI careers are ML engineer, natural language processing (NLP) engineer, AI research scientist, and computer vision specialist.
Educational Background and Skills
Data science professionals are usually from backgrounds such as statistics, computer science, mathematics, or economics. AI professionals typically have strong foundations in the likes of advanced mathematics (calculus and linear algebra), algorithm design, and computer science.
Industry Impact and Applications
AI is making a significant impact in crucial industries such as healthcare (diagnostics and robotic surgeries), retail (personalisation and chatbots), finance (algorithmic trading), and automotive (autonomous vehicles).
On the other hand, data science plays a pivotal role in dashboard generation, customer behaviour analysis, and market insights in sectors such as marketing, logistics, and education.
Also Read: The Role of Data Analytics in Digital Marketing Jobs
Misconceptions about AI and Data Science That Need Clearing Up
Now that we have established the difference between data science and AI, it is time to focus on the misconceptions and myths surrounding such technology. The following are some of the most prominent ones in this context.
| Myth | Reality |
| AI is a magic wand that can solve all problems without much strategic alignment or effort. | AI’s efficacy depends substantially on its workflow integration, clean and high-quality data, and well-defined objectives. |
| Data science is mainly about writing complex code. | Coding is just a part of the process – data science also involves data cleaning, visualisation, analysis, and communication. |
Also Read: How Generative AI Is Transforming the Future of Data Science in Singapore
Advance Your AI or Data Science Career with upGrad Singapore Programs
The colleges and universities offering data science and analytics courses, along with AI and machine learning (ML) certification programs through upGrad, have designed them to help working professionals in Singapore advance their careers and earn the best salaries as data scientists and AI engineers.
- Master of Science in Data Science, Liverpool John Moores University
- Post Graduate Diploma in Data Science (E-Learning), upGrad Institute
- Executive Diploma in Data Science and AI, Indian Institute of Information Technology (IIIT) Bangalore
- Post Graduate Certificate in Data Science & AI (Exclusive), IIIT Bangalore
- Master of Science in Machine Learning & AI, Liverpool John Moores University
- Executive Diploma in Machine Learning and AI, IIIT Bangalore
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FAQs AI vs Data Science Key Differences
Q: What is the difference between AI and data science?
Ans: Data science focuses on extracting knowledge and insights from data. At the same time, AI looks to create systems that can perform tasks that usually need human intelligence, such as learning, decision-making, and problem-solving.
Q: Which field has more career opportunities in Singapore — AI or data science?
Ans: In Singapore, both AI and data science offer considerable career opportunities, but data science presently has a more established and broader presence.
Q: Do I need to learn coding for both AI and data science?
Ans: Yes, candidates need to know coding to work in both data science and AI jobs. They need a strong foundation in programming for deeper understanding, building more complex solutions, and customisation in both these fields.
Q: Can I transition from data science to AI?
Ans: Yes, data scientists can always transition to AI roles – in fact, it is a natural career progression for so many of them!
Q: What are the top tools used in AI vs. data science?
Ans: The top tools of data science are R, Tableau, Python, Excel, and SQL, and for AI, the premier tools are frameworks like TensorFlow, Keras, PyTorch, and OpenCV.
