| Course Level: | Master's | |
|---|---|---|
| Course Duration: | 2 Years | |
| Course Language | English | |
| Required Degree | 3 Year Bachelor’s Degree | |
Pursuing an MS in Applied Statistics at Korea University offers a strong blend of academic excellence and practical training. The program emphasizes modern statistical theory, data analysis, and computational methods, preparing students for careers in research, finance, technology, healthcare, and government. With state-of-the-art facilities, experienced faculty, and access to cutting-edge statistical software, students gain both theoretical knowledge and hands-on experience. Korea University’s global reputation, diverse research opportunities, and industry collaborations provide an ideal environment for professional growth. Studying in South Korea also offers exposure to a dynamic economy and culture, enhancing both academic and personal development.
Program Details |
Information |
|---|---|
|
Program Name |
MS in Applied Statistics |
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Degree Awarded |
Master of Science (M.S.) |
|
Course Duration |
2 years (usually 4 semesters) |
|
Language of Instruction |
Primarily English (some courses may be in Korean) |
|
Yearly Tuition Fees |
KRW 6,500,000 – 7,500,000 (approx. USD 5,000 – 6,000) |
|
Total Tuition Fees |
KRW 13,000,000 – 15,000,000 (approx. USD 10,000 – 12,000) |
|
Total Program Cost |
Including living expenses: ~ USD 25,000 – 30,000 |
|
Eligibility |
Bachelor’s degree in Statistics, Mathematics, Computer Science, Economics, or related fields; strong quantitative background |
|
Admission Requirements |
Online application, academic transcripts, bachelor’s degree certificate, proof of English proficiency (TOEFL/IELTS) or Korean (TOPIK), statement of purpose, recommendation letters, CV/resume |
|
Application Intake |
Spring (March) and Fall (September) |
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Scholarship |
Korea University scholarships, Global Korea Scholarship (GKS), departmental research/assistantship grants |
|
Career Prospects |
Data Scientist, Statistician, Biostatistician, Quantitative Analyst, Market Researcher, Risk Analyst, Academic/Research roles |
|
Curriculum Structure |
Core courses (Probability, Statistical Inference, Regression, Multivariate Analysis), Electives (Machine Learning, Data Mining, Biostatistics, Bayesian Methods, Computational Statistics), Thesis/Capstone project |
