What Is Data Science? Definition, Applications, and Careers

In the digital era, data is often called the “new oil,” but raw data alone is useless without refinement. This is where Data Science comes in. It is the engine that powers everything from your Netflix recommendations to autonomous vehicles. But what is Data Science all about, and why is it considered the “sexiest job of the 21st century”?

This guide explores the definition, workflow, and career potential of this booming field, highlighting how elite institutions like VinUniversity are preparing for the next generation of data leaders.

1. What Is Data Science?

To understand the digital revolution, we must first define the discipline driving it. Data Science is not just about numbers; it is about finding the truth hidden within them.

1.1. Definition of Data Science and its interdisciplinary nature

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It is the convergence of three key domains:

• Computer Science: For building systems to process big data.
• Mathematics & Statistics: For modeling and analysis.
• Domain Expertise: For understanding the specific business or scientific context.

Ultimately, Data Science is not just about mastering these individual skills; it is about the synergy between them. A data scientist must act as a bridge, translating complex mathematical problems into computational code. Finally, into actionable business strategies. This unique combination empowers organizations to predict the future rather than just analyzing the past.

What Is Data Science?

1.2. The role of Data Science in modern enterprises and society

In modern enterprises, Data Science acts as the “brain” of the organization. It allows companies to move from intuition-based decisions to data-driven decisions.

• In Business: It optimizes supply chains (Amazon), personalizes marketing (Spotify), and detects fraud (Visa).
• In Society: It powers precision medicine, predicts climate change patterns, and improves urban planning through “Smart City” initiatives.

From maximizing corporate profits to saving lives, the influence of Data Science is ubiquitous. However, its broad scope often leads to confusion with its parent disciplines. To truly understand the specific boundaries of this field, we must distinguish it from its close cousins: Artificial Intelligence, Computer Science, and Statistics.

1.3. How Data Science differs from related fields (AI, computer science, statistics)

While related, these fields have distinct focuses:

• Computer Science focuses on building software and hardware (the tool).
• Statistics focuses on rigorous mathematical analysis of numerical data (the method).
• Artificial Intelligence (AI) focuses on creating systems that mimic human intelligence (the goal). Data Science uses all of the above to solve specific problems. For example, a Data Scientist uses Computer Science to pull data, Statistics to clean it, and AI to build a predictive model.

How Data Science differs from related fields

Now that we have defined the field, let’s look under the hood to see how a Data Scientist actually works day-to-day.

2. How Data Science Works in Practice

Understanding what is Data Science requires looking at its lifecycle. It is not a linear process, but a continuous loop of discovery and refinement.

2.1. Collecting, processing, and analyzing data

The workflow begins with Data Capture. Data Scientists gather raw data from various sources of databases, social media, and IoT sensors.

• Cleaning (Data Wrangling): Real-world data is messy. Professionals spend up to 80% of their time cleaning “noise” (missing values, duplicates) to ensure accuracy.
• Exploratory Data Analysis (EDA): They use visualization tools like Tableau or Matplotlib to find initial patterns and anomalies.

This rigorous preparation is the unglamorous but essential foundation. Only when the dataset is pristine can we move to the next stage: applying advanced algorithms to generate predictions.

2.2. Using machine learning and analytics to generate insights

Once data is prepared, the “magic” happens.

• Predictive Analytics: Using historical data to forecast future trends (e.g., predicting next month’s sales).
• Machine Learning (ML): Training algorithms to recognize patterns automatically (e.g., training a model to recognize tumors in X-rays).

However, a high-accuracy model is useless if it sits in a vacuum. To transform these raw algorithmic predictions into tangible business value, they must be interpreted and communicated effectively to stakeholders. This bridges the gap between technical complexity and strategic action, leading us to the final stage of the lifecycle.

Using machine learning and analytics to generate insights

2.3. Supporting data-driven decision-making

The final and most critical step is communication.  Insights are useless if they are complex. Data Scientists must act as “Data Storytellers,” translating complex algorithms into clear, actionable strategies for stakeholders (CEOs, Managers) to drive business growth.

This rigorous process is applied across every major industry. Let’s examine where these skills are most in demand.

3. Data Science Careers and Industry Applications

The applications of Data Science are limitless, creating a diverse range of high-impact career opportunities

The applications of Data Science are limitless, creating a diverse range of high-impact career opportunities.

• Healthcare: Bioinformatics Scientists use genomic data to develop personalized treatments and discover new drugs faster.
• Finance: Quantitative Analysts (Quants) use complex models to manage risk and automate stock trading.
• E-commerce: Recommendation System Engineers build the algorithms that suggest “Products you might like,” driving billions in revenue for giants like Amazon and Alibaba.
• Automotive: Computer Vision Engineers are crucial for teaching self-driving cars (like those at VinFast) how to “see” and navigate the world.

With such high demand, it is no surprise that Data Science offers some of the most attractive compensation packages in the tech world.

4. Data Science Salary and Career Growth

In 2025, Data Science remains a top-tier career path with lucrative returns for skilled professionals.

According to the 2024-2025 Salary Reports from ITviec and Robert Half, the compensation for data professionals is rising steadily:

• Entry-Level: Fresh graduates can expect $10,000 – $15,000/year in Vietnam, and significantly higher in global markets (~$95,000 in the US).
• Specialists: Roles like AI Engineers or Machine Learning Architects often command $60,000 – $100,000+ annually in Vietnam for senior positions, and up to $200,000+ globally.
• Growth: The Bureau of Labor Statistics projects a 35% growth rate for Data Science jobs through 2032, much faster than the average for all occupations.

Data Science Salary and Career Growth

To access these high-paying roles, you need more than just online courses; you need a rigorous academic foundation. This is where VinUniversity distinguishes itself.

5. Studying Data Science at VinUniversity

For students asking “what is Data Science all about?” in an academic context, VinUniversity offers a world-class answer validated by Ivy League standards.

5.1. Data Science Bachelor’s Degree at VinUniversity

The Bachelor of Science in Data Science at VinUniversity is a comprehensive program designed to mold future tech leaders.

• Curriculum: Validated by Cornell University (a global top-tier university), the curriculum covers critical modules like Big Data, Deep Learning, and Artificial Intelligence.
• Interdisciplinary Approach: Students collaborate with peers from the College of Business and College of Health Sciences, mirroring the real-world cross-functional environment.

The Bachelor of Science in Data Science at VinUniversity

5.2. Advantages of VinUniversity Data Science program

• Cornell Validation: Your degree carries the prestige of a curriculum validated by an Ivy League institution, ensuring global recognition.
• Industry Immersion: Through the unique “Industry Engagement” program, students gain hands-on experience at Vingroup’s ecosystem companies like VinBigData, VinBrain, and VinFast, tackling real challenges like genomic sequencing or autonomous driving.
• Generous Scholarships: VinUniversity offers merit-based scholarships covering 50% to 100% of tuition for talented students, making elite education accessible.

5.3. How VinUniversity prepares students for high-paying roles

VinUniversity focuses on active learning. By graduation, students possess a portfolio of real-world projects, not just textbook knowledge. This practical experience allows graduates to negotiate top tier starting salaries, often bypassing entry-level rates to secure specialist roles immediately.

A degree from VinUniversity is not just a certificate; it is a passport to the global digital economy.

VinUniversity focuses on active learning.

6. Conclusion

So, what is Data Science? It is the compass of the 21st century, guiding businesses and societies through the ocean of information. Whether you are interested in predicting stock markets, curing diseases, or building smart cities, Data Science provides the tools to turn those dreams into reality.

Institutions like VinUniversity are at the forefront of this revolution, offering the perfect blend of academic rigor and industry application. For students ready to shape the future, the journey starts here.

Ready to lead the data revolution? Explore the Data Science Bachelor’s program at VinUniversity today: https://vinuni.edu.vn/