| Course Level: | Master's | |
|---|---|---|
| Course Duration: | 2 Years | |
| Course Language | English | |
| Required Degree | 4 Year Bachelor’s Degree | |
Choosing M.Tech in Data Science at Seoul National University (SNU) offers a unique opportunity to study at one of Asia's top universities. SNU provides a rigorous, cutting-edge curriculum with access to world-class faculty and research resources. The program emphasizes practical skills, equipping students with advanced knowledge in data analytics, machine learning, and big data technologies. Located in Seoul, a global tech hub, students gain exposure to thriving industries, networking opportunities, and internships with leading companies. SNU's reputation, combined with its strong industry connections, ensures graduates are well-prepared for successful careers in data science worldwide.
Category |
Details |
|---|---|
|
Program Name |
M.Tech in Data Science |
|
Degree Awarded |
Master of Technology (M.Tech) in Data Science |
|
Course Duration |
2 years (typically 4 semesters) |
|
Language of Instruction |
English |
|
Yearly Tuition Fees |
Approx. KRW 6,000,000 – 7,000,000 (~USD 5,000–6,000) |
|
Total Tuition Fees |
Approx. KRW 12,000,000 – 14,000,000 (~USD 10,000–12,000) |
|
Total Program Cost |
Varies based on living expenses; estimated around USD 20,000–25,000 for the entire program, including tuition and living costs |
|
Eligibility |
Bachelor's degree in Computer Science, Engineering, or related fields; strong background in mathematics, statistics, and programming |
|
Admission Requirements |
Academic transcripts, recommendation letters, Statement of Purpose (SOP), English proficiency test scores (TOEFL/IELTS), GRE (optional but recommended), resume/CV |
|
Application Intake |
Typically in March and September (check SNU website for exact dates) |
|
Scholarship |
Various scholarships available, including merit-based and need-based options (partial/full tuition, living stipends) |
|
Career Prospects |
Graduates can pursue roles in data science, machine learning, AI, analytics, research, and business intelligence in various industries (technology, finance, healthcare, etc.) |
|
Curriculum Structure |
Includes core courses (Data Mining, Machine Learning, Big Data Technologies), electives (AI, Data Visualization, Deep Learning), and a capstone project or thesis |
