Artificial Intelligence (AI) and Data Science, recognized as the key driving forces of the Fourth Industrial Revolution, are the most spotlighted areas among STEM majors. These fields are not only leading technological advancement and industrial innovation, but also creating new opportunities across society. AI and Data Science are considered more than just technical tools. They improve human life and solve complex problems. This article will explore the concepts, characteristics, opportunities, and challenges of AI and Data Science.
Around 2010, big data began to boom. Previous to these innovations, there had been no efficient process to manage massive outpours of amounts of data. A considerable amount of data was simply thrown away. But as computers’ processing power made dramatic leaps, suitable mathematical models for handling the data emerged, and the field of data science began. On top of this, AI models represented by LLMs (large language models) that utilize vast amounts of data appeared, bringing about a major transformation.
At UC Berkeley, one of California’s leading public universities, data science was treated as a subfield within applied mathematics until 2017. But starting in 2018, the school created a standalone data science major beginning with juniors declaring their major. Even in its first year, the number of data science graduates reached nearly 100. By 2024, that number rose to 600. Eventually, in the fall semester of 2024, UC Berkeley merged the departments of Data Science, Computer Science, and Statistics to form a new college called the College of Computing, Data Science, and Society (CDSS).
Accordingly, AI and Data Science have now become core technologies for designing the future — ranging from autonomous driving and smart healthcare to climate change response. Those in these fields will not merely be professionals, but key figures who harmonize technology and ethics to change the world.
AI and Data Science
AI is the academic field that enables computer systems to perform learning, reasoning, problem-solving, and decision-making in ways that mimic human intelligence. It encompasses a wide range of subfields including Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), and Computer Vision, and is increasingly applied in real-life areas such as autonomous vehicles, speech recognition, and medical diagnosis.
Data Science is the academic field that collects, analyzes, and interprets large-scale data to derive meaningful insights. By combining statistics, computer science, and domain knowledge, it builds predictive models and solves business problems. For instance, businesses use data science to forecast consumer behavior, while in the medical field, it is used to analyze patient data and devise disease prevention strategies.
AI and Data Science are complementary. AI uses data generated through data science as training resources, while data science contributes to optimizing AI model performance. In both technical foundations and applications, the two fields are closely connected.
Core Curriculum
AI and Data Science are STEM majors with high demand and growth potential. Students who choose this major learn key skills such as computer science, mathematics, statistics, and programming.
- Programming: Languages such as Python, R, and SQL are essential for data processing and model development. Python is especially the most widely used language in AI and data science, offering libraries like TensorFlow, Pandas, and Scikit-learn.
- Math and Statistics: Linear algebra, probability theory, and statistics form the foundations for data analysis and machine learning algorithms. For example, deep learning models heavily depend on matrix operations and optimization theory.
- Data Processing and Visualization: Techniques to efficiently handle and visualize large-scale data are central to data science. Tools like Tableau and Power BI, or libraries like Matplotlib and Seaborn, are used.
- Domain-Specific Knowledge: In data science, understanding of specific industries such as finance, healthcare, and marketing allows for more effective data interpretation. Field knowledge is extremely important.
In the U.S., universities are rapidly expanding departments related to AI and data science. Many are creating new AI or data science majors or strengthening existing computer science curricula. Prestigious universities such as Stanford, MIT, and Carnegie Mellon are offering educational programs. Additionally, platforms like Coursera and edX allow students to take related courses for free or at a low cost, lowering the barrier to enter into the major.
Demand and Opportunities After Graduation
AI and Data Science graduates enjoy high demand across various industries. According to global consulting firm McKinsey, AI-related job demand is expected to increase by more than 20% annually through 2030. Major IT companies are actively hiring AI and data science talent. Key roles include:
- Data Scientist: Analyzes data to extract business insights and designs predictive models. Salaries start at over $60,000 depending on experience. According to Bureau of Labor Statistics data, the average annual salary is $103,500.
- Machine Learning Engineer: Designs, develops, and deploys AI models. Requires deep understanding of deep learning frameworks.
- AI Research: Develops new algorithms or enhances existing models. Usually requires graduate-level education or higher.
- Data Engineer: Builds data pipelines and manages large-scale data.
AI and Data Science are breaking down boundaries between industries. In the financial sector, AI is used for fraud detection and risk management. In healthcare, AI-powered diagnostic tools are gaining attention. E-commerce companies maximize revenue through recommendation systems, and in the public sector, experts are being hired to assist with data-driven policy decisions.
Challenges AI and Data Science Majors Should Know
While AI and data science majors offer high salaries, the challenges are not trivial.
First, the fast pace of technological change requires constant learning. For example, the Transformer model, introduced in 2017, completely transformed the NLP field. New algorithms and frameworks appear every year. Experts must stay up to date through platforms like GitHub and ArXiv, as well as conferences such as NeurIPS and ICML. Active engagement with open-source projects have become crucial.
Second, the mathematical and technical difficulty presents an initial barrier. Linear algebra, calculus, and probability theory are essential to understanding how machine learning models work. For instance, optimization methods like Gradient Descent, which are central to deep learning, require knowledge of multivariable calculus and matrix operations. Beginners may need time to build this foundational knowledge.
Third, ethical issues are emerging as increasingly significant challenges. AI models can reflect biases embedded in training data. For example, in 2018, Amazon discontinued its AI-based hiring system due to gender bias. Data privacy is also a serious concern. As of 2023, regulations like the General Data Protection Regulation (GDPR) impose strict limitations on data use, and violations can result in heavy fines. Experts must not only focus on technology development, but also follow ethical guidelines.
How Students Can Prepare for Careers
Students who wish to major in this field should take the following steps:
- High School Preparation: Take AP Computer Science, AP Statistics, and AP Calculus AB/BC
- Strengthen Fundamentals: In freshman and sophomore years of college, focus on linear algebra, probability theory, statistics, and programming (Python, SQL). For example, data preprocessing with Pandas and matrix operations with NumPy are frequently used in the field.
- Gain Practical Project Experience: Participate in data analysis competitions on Kaggle or contribute to open-source projects on GitHub. For example, building a survival prediction model using Kaggle’s Titanic dataset can help develop practical skills.
- Internships and Networking: Summer internships or participation in AI-related academic conferences are highly beneficial. Internships at companies like Google Research offer both hands-on experience and networking opportunities.
- Ethics Education: Take AI ethics courses, such as AI Ethics on Coursera or related seminars, to learn about bias, privacy, and accountability. These are increasingly valued as much as technical skills.
- Build a Portfolio: Upload real-world data projects to GitHub — like a recommendation system or image classification model — and visualize the results for presentation. A solid portfolio becomes a strong asset when applying for jobs.
Future Outlook and Social Impact
AI and Data Science are likely to remain leading STEM fields going forward. The global market size for autonomous vehicles is projected to reach $1 trillion by 2030. In smart healthcare, AI-driven disease prediction and personalized treatment are driving medical innovation. For instance, AlphaFold by DeepMind solved the problem of predicting protein structures, making a huge impact in life sciences. Smart cities analyze data on traffic, energy, and environment to boost efficiency — such as Songdo in South Korea, one of the world’s earliest planned smart cities.
Beyond industry applications, these technologies also help tackle global issues. In addressing climate change, data analysis is used to predict carbon emissions and suggest optimized energy usage strategies. For example, Google improved its data center energy efficiency by 40% using AI. In education, AI-powered personalized learning platforms like Khan Academy’s AI-based recommendation system are enhancing learning outcomes.
However, this progress comes with threats. First, job automation caused by AI poses a serious challenge. According to a study by Oxford University, by 2035, 47% of global jobs could be replaced by automation –especially in manufacturing, logistics, and customer service. Second, AI malfunction risks are a concern. A 2023 incident involving Tesla’s autonomous vehicle malfunction highlighted the reliability issues of AI systems.Third, data misuse is another danger. The Cambridge Analytica scandal in 2018 showed how data privacy breaches can erode public trust.
To address these problems, experts emphasize strengthening ethical and legal frameworks alongside technological development. For example, AI developers should use diverse datasets to reduce bias and aim for transparent algorithm design.
BY BYUNGHEE JANG, ANGELINA CHO(Intern Reporter)
