Are you an aspiring data science professional in Singapore wondering whether to master Python, R, or sharpen your SQL skills? With a boom in technology roles, the demand for data science professionals is surging, making it the right time to pursue programming languages to hone skills for a thriving data-driven career.
As of 2025, the average salary of a Data Scientist in Singapore is SGD 104,421 per annum, which makes it a viable career choice with competitive wages for programming enthusiasts. In this article, let’s explore how upskilling in the best programming languages for data science – Python, R, and SQL – can boost your career in 2025.
Also Read: Data Science Certifications for Professionals in Singapore
Python, R, or SQL – Which Language is the Best for a Data Science Career in 2025?
Whether you pursue Python, R, or SQL, all these languages come with their personal strength and are in demand for a thriving data science career in 2025. These programming languages are used for their performance with analytical tools in data science. Python is popular for its vast framework, R is known for its role in statistics, and SQL is regarded for efficiently handling databases. Let’s take a quick look at the three key programming languages in data science to help you choose the right one for your career.
Python:
Python is one of the powerful and easy-to-learn data science languages with a simple and straightforward syntax. It is widely used for tasks like:
- Data science and analysis
- Building websites and web apps
- Automation and scripting to save time on repetitive tasks
R:
It is the statistical language for data analysis designed to work with data visualization and statistical computing. It is primarily used for:
- Cleaning and organizing data
- Running detailed statistical tests
- Creating clear and powerful visualisations
SQL:
SQL is the language used to manage data. It stores, retrieves, and manages statistics in relational databases. It is used to:
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- Extract specific data from large datasets using queries.
- Update, insert, or delete records in a database.
- Join and combine data from multiple tables for analysis
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Comparison of features: Python vs R vs SQL
The below compares the key features of Python, R, and SQL for your understanding:
| Feature | Python | R | SQL |
| Purpose | Data analysis, ML, automation, scripting. Versatile across domains. | Statistical modeling, research, and data visualization. | Querying, filtering, and managing structured data. |
| Ease of Learning | Beginner-friendly | Moderate | Simple to Moderate |
| Data Handling | Excellent | Strong | Excellent |
| Libraries |
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|
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| Industry Adoption | Very high across technology, finance, healthcare industries, and startups. | High in academia, healthcare, and research institutions. | Universally adopted in all industries using databases. |
| Ecosystem | Rich ecosystem. | Strong ecosystem for data science. | Limited to DBMS with extensions available. |
| Safety & Reliability | Strong testing frameworks. | Moderate; better for exploratory analysis. | Reliable and stable for DB operations. |
| Performance | Fast with optimized libraries. | Slower than Python. | Efficient with large, structured datasets. |
| Best For | – End-to-end projects, ML models, automation, production system.
– Attracts software developers, widely popular |
– Academic research, statistical analysis, and visual exploration.
– Stronghold in academia, data scientists, finance, and pharmaceuticals. |
– Data retrieval, reporting, and ETL in databases.
– Widely used by data analysts, data engineers, business analysts, database administrators, and backend developers. |
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How Recruiters Hire Data Science Professionals: Which Programming Language Skills Matter?
Recruiters prioritize candidates skilled in Python, SQL, and R for different requirements. They generally value a mix of all three or more combinations.
For instance,
- For Data Analysts, SQL is the most in-demand skill, appearing in over half of job postings, followed by Python and R.
- Data Scientists need proficiency in Python, which is mostly featured in job listings. SQL and R are also highly valued.
- For Machine Learning Engineers, proficiency in Python and SQL is essential, with additional demand for languages like Java.
- Data Engineers need robust skills in SQL and Python and experience in cloud platforms like Azure and AWS.
The table below shows the programming language preference for Data Science Roles in Singapore:
| Roles | Preference by Recruiters (From most preferred to least preferred) |
| Data Analyst | SQL → Python → R → Excel → Power BI/Tableau |
| Data Scientist | Python → Machine Learning tools → R → SQL → NLP |
| ML Engineer | SQL → Python → Apache Spark → Azure → AWS |
| Data Engineer | Python → PyTorch/TensorFlow → SQL → Java → R |
Also Read: Top Python & R Courses for Data Science Beginners in Singapore
Future Trends in Data Science: Why You Shouldn’t Limit Yourself to Just One Language
Data science’s rising demand and influence across all domains make it a viable option for career prospects. Limiting your knowledge to one of the data science languages will limit your scope of work across domains and industries.
With proficiency in multiple languages, including Python, R, and SQL, each playing an important role, professionals and aspiring data science graduates can seek immense career growth. Leveraging the strength of each language for specific problems will boost your career with varied prospects.
Also Read: Data Science Master’s Vs. Self-Learning In Singapore
Choose the Right Language for your Career With upGrad’s Cutting-Edge Programming Courses and Certifications
Choosing the correct programming language for data science can make or break your data science career trajectory in 2025. Rather than preparing and specialising in one particular language, moving ahead with a balanced approach that works best for many professionals is better.
Data science is one of the hottest fields in 2025, making it a sought-after career for data enthusiasts in Singapore. If you are planning to plunge into one or multiple programming languages for an industry-ready career, upGrad Singapore can be your most reliable partner in your learning journey.
Explore the courses and certifications for the best programming language for data science offered by upGrad to get the best career prospects:
- Post Graduate Certificate in Data Science & AI (Executive) – IIT Bangalore
- Master of Science in Data Science – Liverpool John Moores University
- Executive Diploma in Data Science and AI -IIT Bangalore
- Post Graduate Diploma in Data Science (E-Learning) – upGrad Institute
For more information, email at query@upgrad.com or call +65-6232-6730.
FAQs on Best Programming Languages for Data Science
Q: Which programming language is best for beginners in data science?
Ans: Beginners in data science generally start their programming journey with Python, as it is beginner-friendly and has a simple and easy-to-learn syntax.
Q: Do I need to learn both Python and SQL for a data science career in 2025?
Ans: Proficiency in both Python and SQL is beneficial for data science roles in 2025. Python helps with data manipulation and analysis, while SQL extracts data from databases.
Q: Is R still relevant for data science in 2025?
Ans: R is used for research and other specialized fields, which makes it relevant for data science in 2025. It is primarily required for statistical computing, data visualization, and advanced analytics.
Q: Can I get a data science job if I only know SQL?
Ans: Depending on your job role, SQL may or may not be sufficient for a data science job. SQL is essential for database querying. However, pairing it with Python or R will significantly improve your job prospects.
Q: How long does learning Python, R, or SQL for data science take?
Ans: With consistent effort, one can learn Python, R, and SQL within 3–6 months. However, mastering the language for advanced data science applications will require a longer duration.
Q: Which language is more in demand by employers in 2025?
Ans: Python, JavaScript, and Java have been among the top languages due to their versatility and job market demand.
