| Course Level: | Bachelor's | |
|---|---|---|
| Course Duration: | 4 Years | |
| Course Language | English | |
| Required Degree | Class 12th | |
Choosing a B.Sc in Information Statistics at Gyeongsang National University offers a strong foundation in data analysis, statistical modeling, and information systems—skills highly demanded in today’s digital era. The program emphasizes practical learning, blending theory with hands-on projects to prepare students for careers in data science, business analytics, and research. Located in South Korea, a hub of technological innovation, GNU provides access to modern facilities, experienced faculty, and global opportunities. With a supportive academic environment and diverse student community, this degree equips graduates with critical problem-solving abilities and international perspectives, fostering success in both local and global job markets.
Field |
Details |
|---|---|
|
Program Name |
B.Sc. in Information Statistics |
|
Degree Awarded |
Bachelor of Science (B.Sc.) |
|
Course Duration |
4 years (8 semesters) |
|
Language of Instruction |
Korean (some courses may be offered in English) |
|
Yearly Tuition Fees |
Approx. USD 2,000 – 3,000 (varies by year and scholarships) |
|
Total Tuition Fees |
Approx. USD 8,000 – 12,000 for 4 years |
|
Total Program Cost |
Around USD 20,000 – 25,000 (including living expenses) |
|
Eligibility |
Completion of high school (or equivalent); proficiency in Korean or English |
|
Admission Requirements |
Application form, transcripts, proof of language proficiency (TOPIK/IELTS/TOEFL), statement of purpose, recommendation letters |
|
Application Intake |
Usually March (Spring semester) and September (Fall semester) |
|
Scholarship |
Merit-based and need-based scholarships available for international students |
|
Career Prospects |
Data analyst, statistician, business intelligence specialist, data scientist, researcher, roles in IT, finance, healthcare, and academia |
|
Curriculum Structure |
Core courses: Probability, Statistical Inference, Data Analysis, Regression, Statistical Computing, Big Data Analytics; Electives in AI, Machine Learning, and Applied Statistics |
