What is Data Science? Applications, Opportunities, and Career Paths
What is data science? An interdisciplinary field, data science merges the realms of statistics, mathematics, programming, big data technologies, and more. Its aim? To decipher, interpret, and extract actionable insights from data. In an era of exploding data volumes—rising from 12.5 zettabytes in 2014 to an anticipated 147 zettabytes in 2024—data has become a key driver of decision-making, operational efficiency, and even the innovation propelling Industry 4.0 (1, 2). Hence, it’s no wonder that data science is indispensable in today’s global landscape.
Now, if you’re pondering whether to pursue this field, questions naturally arise: What roles can you aim for? What salary prospects await? And most importantly, how do you embark on this career journey? Let’s find out.
What is Data Science? 
Data science, as already mentioned, is a multidisciplinary domain that has come to reshape how we understand, interpret, and work with data. Though somewhat close to it, what is data science transcends classical statistical analysis by bringing advanced programming, big data technologies, advanced analytics, and artificial intelligence into play. Its potential lies in its ability to transform raw data into strategic decisions that drive innovation and efficiency.
Today, data science fuels change by enhancing productivity, predicting trends, and uncovering opportunities. For instance, governments use it to optimize public services and make informed policy decisions, whereas businesses harness it to gain a competitive edge. Furthermore, when it comes to STEM fields (i.e., healthcare, industrial technology, etc.), the use of data science results in increased efficiency and innovation. In short, its application reaches nearly every facet of modern life, underscoring its indispensable nature.
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Understanding the Data Science Ecosystem
The essence of what is data science lies in its ecosystem, a well-structured interplay of various elements that collaborate to achieve data-driven insights. Each element contributes uniquely, forming the backbone of this transformative field. Let’s explore the key components that define what is data science.
1. Programming
Programming is one of the key components when it comes to considering what is data science ecosystem. In essence, programming languages are essential for manipulating, cleaning, and analyzing vast datasets effectively. By writing precise scripts and algorithms, data scientists automate tasks, extract patterns, and draw conclusions from intricate data structures. Furthermore, programming enables the creation of custom solutions tailored to specific problems, fostering innovation and precision in data analysis.
2. Big Data
Data science leverages big data to perform high-speed analysis that traditional methods cannot achieve. Consequently, it empowers sectors like finance, marketing, and logistics to process real-time information efficiently. By transforming overwhelming data streams into comprehensible insights, data science maximizes the value extracted from these vast repositories. This illustrates the significance of big data in understanding what is data science.
3. Database Management
Database management ensures that data is stored, organized, and retrieved systematically. In the data science ecosystem, this component maintains the integrity and accessibility of information, forming the backbone of analysis. Furthermore, it establishes a reliable framework that supports the smooth functioning of data-driven projects.
4. Mathematics
Mathematics remains a fundamental pillar of what is data science, guiding every step of the analytical journey. Through mathematical modeling, data science simulates real-world scenarios to provide valuable predictions and insights. For example, it deciphers patterns hidden within complex datasets, offering clarity in ambiguity and enabling businesses to forecast trends, mitigate risks, and make informed decisions.
5. Data Visualization
Data visualization bridges the gap between raw information and human comprehension. It translates intricate datasets into intuitive visual representations, making it easier for stakeholders to grasp complex ideas. By employing charts, graphs, and infographics, data visualization conveys the essence of insights effectively.
6. Artificial Intelligence & Machine Learning
In the data science ecosystem, various cutting-edge artificial intelligence and advanced machine learning technologies are used to analyze complex patterns and build predictive models. These technologies, coupled with data science, play crucial roles in various tasks like recommendation engines, fraud detection, and personalized user experiences.
Highest-Paying Data Science Skills in Singapore 
The Asia-Pacific region has emerged as a global data hub, with Singapore at the forefront. Currently ranking fourth in APAC for data center capacity, Singapore continues to attract major investments (3). In 2011, it became the first Southeast Asian country to host a Google data hub, and in 2018, Facebook announced a $1.4 billion investment to establish a massive data center in the region (4,5). Consequently, with such developments, the demand for skilled data professionals is skyrocketing. So, now that you have a clear understanding of what is data science and what its uses are, it’s time for you to explore some of the highest-paying data science skills in Singapore.
1. Mathematics and Statistical Analysis
Mathematics and statistical analysis are crucial for understanding data patterns and making informed predictions. Why? Because they enable data scientists to apply models that validate hypotheses and quantify relationships, ensuring accurate and actionable insights. To succeed as a data scientist in a competitive data market that Singapore boasts of, you need to be skilled in:
- Probability and Statistics
- Linear Algebra
- Hypothesis Testing
- Time Series Analysis
2. Programming Languages
In what is data science, programming is essential for building algorithms and handling data efficiently. In essence, programming language plays a pivotal role in creating tailored solutions, automating workflows, and analyzing datasets with precision and scalability. Hence, as a data scientist, you need to be skilled in programming languages. Some of the most popular languages that data scientists use includes Python, R, MATLAB C/C++, Java, Scala, and Julia.
3. Database Management
Database management ensures data organization, security, and accessibility. It provides the foundation for storing and retrieving information reliably, which is critical for efficient data analysis and modeling. Hence, being skilled in database management is a non-negotiable quality for data scientists. In this context, some of the most popular database management tools for data scientists are MySQL, PostgreSQL, MongoDB, and Oracle Database.
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4. Big Data Technologies
Big data technologies enable the processing of massive datasets at high speed. In short, they play a vital role in uncovering insights from unstructured data and supporting real-time decision-making processes. Hence, succeeding as a data scientist requires that you are well-versed in big data technologies, which include frameworks such as Hadoop, Apache Spark, Hive, NoSQL, etc.
5. Data Visualization
In essence, data visualization simplifies complex datasets, turning insights into visually compelling stories that drive strategic actions. Some of the most common platforms and libraries used for data visualization include Tableau, Power BI, D3.js, and Matplotlib.
6. Artificial Intelligence
Artificial intelligence has become an integral part of data science. Why? Because it proves extremely effective when it comes to operations like creating predictive systems or automating data-driven decisions. Hence, a data scientist must be adept at working at the intersection of data technology and artificial intelligence. For this particular purpose, one must be skilled in frameworks and libraries like TensorFlow, Keras, PyTorch, Scikit-learn, etc.
What Roles Can a Data Scientist Do?
Data scientists possess versatile skill sets that enable them to take on various roles across industries, and each position leverages different aspects of data science, providing unique opportunities to apply expertise and make a tangible impact. Below is an overview of the key roles a data scientist can pursue, along with their primary responsibilities.
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| Role | Responsibilities |
| Data Scientist |
|
| Data Architect |
|
| Data Engineer |
|
| Machine Learning Engineer |
|
| Statistical Analyst |
|
| Database Administrator |
|
Is Data Scientist a Good Career Path?
Would it be a wise decision to choose data science as your career? Yes. Unequivocally, yes, and primarily for three reasons.
Firstly, the appeal of a career in data science lies in its versatility and relevance. For instance, professionals in this field tackle diverse problems, from optimizing supply chains to predicting healthcare outcomes, enabling significant advancements across sectors. Furthermore, data scientists also have the opportunity to work with cutting-edge technologies like artificial intelligence, machine learning, and big data, making this career both dynamic and future-proof. Thus, the ability to shape industries and solve real-world challenges makes it a fulfilling profession for those passionate about innovation.
Secondly, the financial rewards are equally compelling. Data science roles consistently rank among the highest-paying jobs globally and in the Asia Pacific region. Let’s look at how much a data scientist earns in some of the countries in this region.
- A data scientist in Singapore earns around $84,000 in Singapore. (6)
- A data scientist in India earns ₹13,51,000 per annum. (7)
- The salary of a data scientist working in China is approximately CN¥4,13,500 per annum. (8)
- A data scientist in New Zealand earns around NZ$90,000 per year. (9)
So, it’s clear from the data above that data science is a highly lucrative job. With the increased momentum of this discipline in the Asia Pacific region, the number of job openings and the average yearly salary for various positions are expected to rise. Hence, choosing what is data science as a career path is highly rewarding in terms of financial gains, beyond doubt.
Last but not least, beyond monetary benefits, data science offers excellent job security, given the increasing reliance on data for strategic decisions. Thus, whether in startups or multinational corporations, data scientists enjoy the flexibility to explore roles tailored to their skills and interests.
It’s for such reasons that the Harvard Business Review famously declared data science the “sexiest job of the 21st century (10).” Together, the combination of high demand, impactful work, and substantial compensation ensures that a career in data science remains not only viable but also exceptionally rewarding for aspiring professionals.
How to Become a Data Scientist
Becoming a data scientist requires a structured approach that combines formal education, specialized training, and hands-on experience. Let’s delve into the step-by-step journey toward mastering what is data science and making a mark in this domain
1. School Education (10+2)
The journey to becoming a data scientist begins at the school level, where subjects like mathematics, statistics, and computer science lay a strong foundation. Mathematics introduces concepts like calculus, linear algebra, and probability. They are crucial for understanding machine learning algorithms and data analysis techniques. Meanwhile, exposure to computer science develops problem-solving skills and introduces basic programming, which are vital for handling datasets and building analytical models.
2. Bachelor’s Degree
Earning a bachelor’s degree in a related field is the next logical step. Degrees in computer science, mathematics, or statistics offer foundational knowledge that supports data science skills. For instance, a degree in computer science emphasizes programming, algorithm development, and database management. On the other hand, mathematics programs delve deeper into quantitative techniques and modeling, which are indispensable for creating predictive systems. Similarly, statistics degrees focus on methods for collecting, organizing, and interpreting data, directly aligning with the goals of data science. In addition, specialized undergraduate data science courses provide early exposure to machine learning and big data technologies, accelerating the learning curve.
3. Master’s Degree
Pursuing a master’s degree refines and expands expertise, focusing on advanced techniques and practical applications. Master’s programs in data science, machine learning, or analytics offer in-depth training in areas like big data processing, deep learning, and predictive modeling. These programs also emphasize using real-world datasets, ensuring students are industry-ready. For aspiring data scientists targeting leadership roles or specialized positions, a master’s degree significantly boosts employability and equips them to tackle complex challenges with confidence.
4. PhD (Optional)
While not mandatory, a PhD opens doors to research-intensive roles and academia. This level of education equips individuals with a profound understanding of the theoretical underpinnings of data science, enabling them to develop groundbreaking methodologies or contribute to cutting-edge innovations. PhD holders often work in top-tier research labs, academic institutions, or think tanks, making significant contributions to the advancement of what is data science.
Data science, with its unique blend of challenges and opportunities, continues to intrigue and inspire. If understanding what is data science excites you, and you’re eager to build a thriving career in this transformative field, explore Emeritus’s data science courses. Delivered by top-ranking institutes in Asia and brought to you by Emeritus, these programs combine global insights with regional relevance, offering cutting-edge knowledge and skills to help you excel in this dynamic domain.
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