With a rapid surge in Singapore’s digital economy, Big Data and Data Science are becoming increasingly popular; however, they are often confused with each other and sometimes used interchangeably.
According to the latest statistics, the digital economy in Singapore is expected to reach a Gross Merchandise Value (GMV) of nearly USD 27 billion by the end of 2025.
This blog provides a closer examination of the key differences between Big Data and Data Science, and explores how each contributes to the growth of Singapore’s digital economy.
Also Read: How To Become A Data Scientist?
Big Data vs. Data Science: Understanding the Key Differences
To better understand the difference between Big Data and Data Science, the table below highlights the various parameters that distinguish the two technologies:
| Parameters | Big Data | Data Science |
| Objective | Adequate storage, processing, and management of massive datasets | Analyze and interpret data to make forecasts |
| Skills | Distributed computing, data infrastructure, and data pipeline design | ML, statistical analysis, data visualisation |
| Tools | Spark, Hadoop, Kafka | R, Python, SQL |
| Job roles | Data Architect, Big Data Engineer, ETL Developer | Data Analyst, Data Scientist, ML Engineer |
| Best suited for | Professionals aspiring to build and maintain data pipelines and systems | Aspiring professionals in predictive model designing and extracting valuable insights from datasets |
Big Data & Data Science
Big Data, as the name suggests, implies massive sets of structured and unstructured data generated at a high speed from several sources.
Data Science refers to the use of various algorithms, scientific methodologies, and tools to evaluate and interpret complex data.
Primary Objective of Big Data & Data Science
While Big Data aims to effectively gather, store, and process large datasets for informed decision-making, data science focuses more on evaluating and interpreting data using computational and ML methods.
Data Types Used in Big Data & Data Science
This is yet another key distinction between Big Data Analytics vs Data Science. Big Data generally handles massive volumes of various types of data; Data Science typically works with semi-structured, structured, and unstructured data.
Key Technologies
Some key tools and technologies also distinguish Big Data from Data Science. For instance, Apache Spark, Hadoop, and Kafka are the primary technologies used in Big Data.
In Data Science, programming languages such as R and Python, data analysis tools like NumPy and Pandas, and AutoML platforms like Google AutoML and DataRobot are utilised.
Common Job Roles
When deciding between Big Data and Data Science: Which is better, it helps to assess the potential career roles that each offers for long-term growth. For instance, professionals in the field of Big Data are eligible to work as Data Architects, Big Data Engineers, and ETL Developers.
Data Science students, on the other hand, find lucrative job opportunities as Data Analysts, Data Scientists, and ML Engineers.
Industries for Each Field
The industries for both domains are also different. For instance, Big Data is commonly used in industries such as finance and banking, telecommunications, e-commerce, and retail.
However, Data Science finds prominence in sectors such as healthcare, advertising, education, and logistics.
Type of Professional or Interest Area Best Suited for Each Field
This is yet another distinguishing factor in the debates over Big Data versus Data Science. Big Data fields are best suited for professionals with a keen interest in systems architecture, data infrastructure, and building backend systems.
Data Science aspirants include professionals from Statistics, Economics, or Mathematics backgrounds, with an interest in data visualisation and unravelling data patterns.
Also Read: Top Data Scientist Interview Questions in Singapore
Choosing Between Big Data and Data Science: What Should You Consider?
When making an informed choice between the two, consider your academic and professional backgrounds, career aspirations, and interests.
- Take into account your background – those from an IT or Computer Science background are ideal for Big Data, and those from a Math or Statistics background are more suited for Data Science.
- Understand your career goals clearly and choose one of the two fields that best aligns with them.
- Evaluate your interests and what you love working with.
Also Read: Data Science vs. Machine Learning Engineer
Learn Big Data or Data Science with upGrad’s Industry-Aligned Programs
Once you have figured out which of the two – Big Data Analytics vs Data Science – is better, consider pursuing upGrad’s globally recognised programs in both fields, such as:
Explore these online data science courses through upGrad!
- 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 (IIT Bangalore)
- Post Graduate Certificate in Data Science and AI -Executive (IIT Bangalore)
FAQs on Big Data vs. Data Science
Q: Which is better, Big Data or Data Science?
Ans: Data science is a more valuable field, as it helps derive useful insights and inform decisions. Big Data, on the other hand, focuses on managing massive volumes of data.
Q: Who earns more, a Data Scientist or a Big Data Engineer?
Ans: The actual compensation for both may vary depending on the industry in which they work and other factors. However, Data Scientists earn relatively higher salaries than Big Data Engineers owing to their skill sets.
Q: Is Big Data in demand in Singapore?
Ans: Of course! Big Data is in high demand in the country, which is rapidly evolving into a digital economy. There is a surge in the need for data professionals across varied sectors and industries.
Q: Are there certifications that cover both Big Data and Data Science?
Ans: Yes, aspiring professionals in both fields can pursue online certification courses from popular e-learning platforms like upGrad. In addition to flexible learning, the platform offers career assistance.
Q: How long does it take to become job-ready in either the Big Data or Data Science field?
Ans: Job readiness in both fields may vary from one individual to another. It typically takes professionals around 6 to 12 months to gain expertise in hands-on projects.
