| Course Level: | Master's | |
|---|---|---|
| Course Duration: | 2 Years | |
| Course Language | English | |
| Required Degree | 3 Year Bachelor’s Degree | |
Pursuing an MS in Mathematical Statistics at Korea University offers a strong academic foundation with cutting-edge research opportunities in probability, statistical theory, and data analysis. Korea University, one of South Korea’s top institutions, provides a rigorous curriculum taught by renowned faculty, blending theoretical depth with practical applications in diverse fields such as finance, artificial intelligence, and biomedical sciences. Students benefit from state-of-the-art facilities, interdisciplinary collaborations, and exposure to Korea’s growing role in data-driven industries. With a global outlook and strong alumni network, the program equips graduates with advanced analytical skills, preparing them for impactful careers or doctoral research worldwide.
Category |
Details |
|---|---|
|
Program Name |
MS in Mathematical Statistics |
|
Degree Awarded |
Master of Science (M.S.) |
|
Course Duration |
2 years (4 semesters) |
|
Language of Instruction |
Primarily English & Korean (many graduate courses available in English) |
|
Yearly Tuition Fees |
Approx. USD 6,000 – 7,000 (varies by semester and student status) |
|
Total Tuition Fees |
Approx. USD 12,000 – 14,000 for full program |
|
Total Program Cost |
Around USD 16,000 – 20,000 (including living expenses) |
|
Eligibility |
Bachelor’s degree in Statistics, Mathematics, or related field |
|
Admission Requirements |
Application form, transcripts, degree certificate, study plan, recommendation letters, English proficiency (TOEFL/IELTS) or TOPIK (for Korean track) |
|
Application Intake |
Spring (March) & Fall (September) |
|
Scholarship |
KU Graduate School Scholarship, Global KU Scholarship, government and external scholarships (e.g., KGSP) |
|
Career Prospects |
Data scientist, statistician, quantitative analyst, academic researcher, roles in AI, finance, public policy, and biomedical industries |
|
Curriculum Structure |
Core courses: Probability Theory, Mathematical Statistics, Linear Models, Statistical Computing; Electives: Multivariate Analysis, Bayesian Statistics, Time Series, Machine Learning; Thesis requirement |
