Overview
The 21st-century knowledge society is rapidly evolving around a new core resource: data. Innovations such as artificial intelligence, cloud computing, and the Internet of Things are all driven by data, which has moved beyond its role as a mere tool to become a vital source of new knowledge and value creation. In this context, the ability to understand and effectively utilize data has emerged as an essential competency in modern society. The Graduate School of Data Science at Ewha Womans University responds to these changing demands by defining data science as a convergent discipline that leads the future society. Through a comprehensive and structured curriculum—spanning fundamental theories, advanced technologies, and diverse applications across industries and society—the program supports students in developing both academic depth and practical expertise in a balanced manner. Furthermore, it aims to cultivate future leaders in data science who create new value through data-driven inquiry and practice, while demonstrating a global perspective and a strong sense of ethical responsibility.
History
| 2022.03 | The Graduate School of Data Science established |
|---|---|
| 2026.03 | 100% Online Degree Program Launched |
Vision, Educational Philosophy and Objectives
- Vision
- Cultivating action-oriented data scientists who go beyond data interpretation to drive real-world change through data
- Education Philosophy
- 6 Pillars “ACTION”
The Six Pillars represent the core competencies that are consistently reinforced across all courses and stages of learning.
- 6 Pillars “ACTION”
| A | Agile Leadership | Leadership that enables data-driven decision-making and adaptive direction-setting in uncertain environments |
|---|---|---|
| C | Cross-Disciplinary Education | Integrative thinking that connects statistics, computer science, and domain knowledge |
| T | Theory to Practice | The ability to translate theory into practical problem-solving and effective execution |
| I | Innovative Learning Methods | A learning approach centered on projects, hands-on practice, and discussion |
| O | Open Collaboration | Data utilization capabilities that support collaboration across teams, organizations, and society |
| N | Networking Excellence | Continuous growth through networks spanning industry, academia, and alumni |
- 4 Stacks
The curriculum is designed as a staged learning framework that reflects the developmental trajectory of students.
- 4 Stacks
| Foundation Stack | Establishing foundational concepts in data science and a shared analytical language |
|---|---|
| Data Analytics & Modeling Stack | Advancing competencies in analytical methods and AI modeling |
| Management & Appplication Stack | Strengthening capabilities in strategy, decision-making, and real-world application |
| Capstone & Professional Development Stack | Integrating prior learning through a capstone project or research experience |
- Educational Objectives
- Developing a systematic understanding of the fundamental theories and analytical methods in data science
- Building the ability to structure problems and develop models using diverse forms of data
- Cultivating strategic thinking that connects analytical results to decision-making at organizational, societal, and policy levels
- Strengthening practical execution skills through hands-on training and project-based problem solving of real-world data challenges
- Fostering sustained professional growth through collaboration and effective communication
Departments
- Department of Data Science
- Department of Interdisciplinary Data Science