Education

Course Curriculum Graduate School of Data Science Curriculum

  • The core of KAIST GSDS's curriculum-based education is to provide knowledge and skills covering the entire value chain of data science.
  • This distinguishes us from graduate programs focused on specific methodologies, such as AI graduate schools or statistics-related graduate programs.
  • Furthermore, our curriculum differentiates itself from methodology-centered graduate programs by including courses that integrate data science with specific professional domains.
  • Pre-Admission Curriculum: KAIST GSDS recruits students with backgrounds in various fields where data science is applied. We provide introductory data science education prior to admission, preparing these students with the necessary mathematical and programming skills to engage effectively in graduate-level education.
  • Data Science Value Chain Courses: Data creates significant value in the real world through multiple stages including collection, management, processing, analysis, modeling, and application. To reflect this process, we offer the following groups of courses.
    • Introduction to Data Science Courses: Courses providing a comprehensive overview of data science
    • Data Foundation Courses: Graduate-level mathematical courses essential for handling data
    • Data Computing Courses: Courses covering data collection, storage, management, and processing for data science
    • Data Analysis Courses: Courses on statistical analysis, artificial intelligence, and machine learning models for data science
    • Data Application Courses: Courses supporting the practical application and value creation of data science in real-world domains

Graduation Requirement for Graduation

GSDS Master's Program Graduation Requirements

At least 33 credits in total

Requirement for Graduation
Mandatory General Course 3 credits and 1AU
  • CC010 Special Lecture on Leadership(non-credit, this applies to students entering KAIST in 2002 and onward; general scholarship students, foreign students are excluded)
  • CC020 Ethics and Safety I(1AU)
  • Select 1 course among the following 10: CC500, CC510, CC511, CC512, CC513, CC522, CC530, CC531, CC532, CC533
Elective Course at least 21 credits
  • Complete at least 21 credits, including courses from other departments. [Up to 9 credits of 400-level or lower courses may be included, regardless of their undergraduate/graduate cross-listing status. (However, 400-level or lower courses that are not cross-listed for both undergraduate and graduate levels require approval from the academic advisor and department head to be included.)]
  • Select at least 15 credits from DS or IE courses. (Students enrolled in an interdisciplinary program are required to select at least 9 credits from DS or IE courses.)
Research Credits at least 9 credits
  • Students admitted before 2022: at least 9 credits including Seminar 2 credits (A foreign student who has taken HS586 course doesn't need to take the seminar course)
  • Students admitted 2022: at least 9 credits including Seminar 1 credit (A foreign student who has taken HS586 course doesn't need to take the seminar course)
  • Students admitted 2023 or later: at least 9 credits including Seminar 1 credit (Foreign students are also required)

Credit Requirement for Graduation (GSDS Doctoral Program)

At least 63 credits in total

Credit Requirement for Graduation(GSDS Doctoral Program)
Mandatory General Course 3 credits and 1AU
  • The course credits earned in the Master's coursework can be used towards the Doctoral degree.
Elective Course at least 30 credits
  • Complete at least 30 credits from DS or IE courses at the 500-level or above, including courses from other departments. [Up to 9 credits of 400-level or lower courses may be included, regardless of their undergraduate/graduate cross-listing status. (However, 400-level or lower courses that are not cross-listed for both undergraduate and graduate levels require approval from the academic advisor and department head to be included.)]
  • Select at least 18 credits from DS or IE courses including credits earned in the Master's coursework. (Students enrolled in an interdisciplinary program are required to select at least 9 credits from DS or IE courses.)
Research Credits at least 30 credits
  • Students admitted before 2021: at least 30 credits including Seminar 2 credits
  • Students admitted 2022 or later: at least 30 credits including Seminar 1 credit
  • A foreign student who has taken HS586 course doesn't need to take the seminar course
  • The course credits earned in the Master's coursework can be used towards the Doctoral degree (except research credits)

Guidelines for Doctoral Dissertation Proposal (Oral Examination)

Timing

  • The proposal may be conducted at any time throughout the year; there is no deadline.
  • It may be held during either the regular semester or vacation period.
  • Please note that it is not permitted to conduct both the proposal and the dissertation defense during the final regular semester prior to graduation.

Composition of the Examination Committee (Total of 5 members)

  1. Chair: The advisor (ex officio)
  2. Two tenured professors from the department
  3. The remaining two members may be selected from among non-tenured faculty of the department (e.g., emeritus or invited professors), professors from other departments, or external experts from outside institutions.

Examples

  • Advisor + 2 tenured department professors + 2 external members (Allowed)
  • Advisor + 4 tenured department professors (Allowed)
  • Advisor + 1 tenured department professor + 1 non-tenured department faculty member + 1 professor from another department + 1 external member (Not allowed)

Administrative Procedures

Path: Academic System > Graduation > Dissertation

  1. Under the “Appointment of Dissertation Committee Members” tab, complete the “Request for Appointment of Dissertation Committee Members” form and submit it to the department office before the examination.
  2. Result Report Form: Under the “Dissertation Proposal Oral Examination” tab, download the “Dissertation Proposal Oral Examination Form” and provide it to your advisor.
  3. The result report is usually submitted directly to the department by the advisor, and the department will update the results in the system.
  • If Step 1 is not completed, the department will not be able to confirm or follow up in cases where the result report has not been submitted.


Curriculum Offered Courses

DS501 Statistical Inference for Data Science3:0:3

This course lays statistical foundation for data scientists. Probability model, random variables and their probability distribution, joint distribution, and conditional distribution are introduced along with data generation from a given distribution via computer exercise. Statistical inference with likelihood principle and Bayesian approach will be covered.

DS503 Machine Learning for Data Science 3:0:3

This is an introductory course on machine learning, which belongs to a broader family of machine learning methods concerned with the development and application of modern neural networks. We will cover a range of topics from basic neural networks, convolutional and recurrent network structures, deep unsupervised learning, attention-based models, and applications to computer vision, language understanding, and other areas. The course will be focused on understanding deep learning methodology, rather than implementing them using modern libraries.

DS504 Programming for Data Science3:0:3

This course introduces the technologies and programming techniques for data science. This course covers from the programming language basics, which is effective in data science, common libraries for analytics, and utility tools and techniques on software for data science.

DS523 Pervasive Computing for Data Science3:0:3

There has been an increasing trend towards integrating sensing, storing, and computing in the real world. This course takes an interdisciplinary look at this pervasive computing from the data science perspective. A huge set of data is being collected, stored, and analyzed via sensors, SNSs, metaverse, etc. Students are expected to understand how to integrate the data science into the sensing, collecting, and analyzing such data in an impactful way.

DS535 Recommender System and Graph Machine Learning3:0:3

This course is a project-based course focused on recommender systems and machine learning on graphs. The first half of the course covers fundamental techniques in recommender systems ranging from content-based approaches, and traditional collaborative filtering, to recent advanced techniques including matrix factorization and deep learning-based collaborative filtering methods. In the second half of the course, we discuss major topics in machine learning on graphs including random walk-based methods

Notes
* Prior contact with a prospective advisor is recommended.
* Advisor assignment will be conducted before enrollment for admitted students.