MS & PhD Programs

Master of Science in Data Science & Artificial Intelligence

The Master of Science Program in Data Science and Artificial Intelligence is designed to prepare students for a professional career in artificial intelligence and/or data science in applied settings, as well as providing a solid basis for continued advanced research. Introductory level computer programming, probability, statistics, and mathematics skills are required for a student to start the program. It provides basic knowledge about the field through the mandatory courses: Data Science, Machine Learning, and Statistical Inference. While the program offers a thorough grounding in various aspects of data science and artificial intelligence, it also offers scientific excellence in highly interdisciplinary fields that aim to solve important societal problems.

The MS Program in Data Science and Artificial Intelligence is an interdisciplinary program. Therefore, people from diverse backgrounds like engineering, law, medicine, life, and social sciences are all candidates for the program. The program includes courses from different research fields of data science and artificial intelligence which can address needs of a wide spectrum of students.

This is a two-year program with the first year devoted to completing all the course requirements, and the second year devoted to preparation of a thesis based on authentic research. For students lacking the required scientific background, an additional one-year remedial program is offered.

Table 1: The Master of Science Curriculum in Data Science and Artificial Intelligence

First Semester		Credit	ECTS
DSAI 510	Data Science	4	8
DSAI 512	Machine Learning	4	8
DSAI 514	Statistical Inference	4	8
— —	Complementary Elective	3	6
Total		15	30

Second Semester		Credit	ECTS
DSAI —	Area Elective	3	7
DSAI —	Area Elective	3	7
— —	Complementary Elective	3	6
DSAI 579	Graduate Seminar	0	2
DSAI 599	Guided Research	0	8
Total		9	30

		Credit	ECTS
DSAI 690	Master's Thesis	0	60
Total		0	60

Total Credits: 24
Total ECTS: 120
* All area elective courses must be chosen from the courses listed in Table 4.
** Credit and ECTS specified are the minimum required values.

Table 2: Remedial courses

First Semester		Credit	ECTS
DSAI 301	Introduction to Programming with Python	4	8
DSAI 303	Probability and Statistics for Data Science and AI	4	8

Second Semester		Credit	ECTS
DSAI 302	Python for Data Science and AI	4	8
DSAI 304	Mathematics for Data Science and AI	4	8

Candidates with gaps in their background may be required to complete a remedial program before they start the program. Remedial courses are listed in Table 2, nevertheless; each candidate is responsible for a specific subset of courses based on their scientific background. The remedial program must be completed within one or two semesters depending on the admission requirements.

Data Science and Artificial Intelligence M.S. Program includes three compulsory courses: DSAI510 Data Science, DSAI512 Machine Learning, and DSAI514 Statistical Inference. Each student is also required to take at least two area elective courses, two complementary elective courses, a graduate seminar course, and a guided research course.

Each registered student is required to choose a specific area associated with their studies. The area elective courses of the program are provided in Table 4.  Complementary elective courses may be chosen from graduate courses offered by the Institute or the graduate level courses of other programs based on the student’s research field. Complementary elective courses may also be chosen from senior-level undergraduate courses offered by other programs in exceptional circumstances. These courses must be approved by the student’s academic or thesis advisor. The seminar course is designed to expand the research perspective of the students. The guided research course is given by the thesis advisors to specify student’s research direction and thesis proposal.

Individuals registered for the program are required to choose their thesis advisor and research field until the end of the first semester. They must prepare a thesis proposal and must submit the thesis title to the Institute until the end of the second semester.

Doctor of Philosophy in Data Science & Artificial Intelligence

Doctor of Philosophy program in Data Science and Artificial Intelligence provides a regular PhD program for candidates who have a master’s degree in a program related to data science or artificial intelligence and an integrated PhD program for candidates who have only a bachelor’s degree or a master’s degree from other programs.

The students in the integrated PhD program are required to accumulate a minimum of 42 credits by completing at least 14 graduate courses three of which must be the compulsory courses of the MS and the regular PhD program in Data Science and Artificial Intelligence. They may also be asked, based on their background, to complete the remedial course work prior to starting the integrated PhD program. The remedial courses are given in Table 2. The remedial program must be completed within one or two semesters depending on the admission requirements.

The PhD program is given in Table 3. In the program, individuals are required to determine a specific curriculum with their advisors to guide them for their research interests. Each student is required to take a graduate seminar course in which all research fields of the program are introduced, a guided research course, at least four Area Elective Courses selected from Table 4 and at least three complementary elective courses which may be chosen from graduate courses offered by the Institute or the graduate level courses of other programs based on the student’s research field. The guided research course is given by the thesis advisors to specify student’s research direction and thesis proposal prior to the qualifying exam.

Individuals must prepare a thesis proposal while they are taking the guided research course. The PhD thesis is required to be completed in the legal term for every individual registered in the program following the approval of the thesis proposal.

Table 3: The Doctor of Philosophy Curriculum in Data Science and Artificial Intelligence

First Semester		Credit	ECTS
DSAI —	Area Elective	3	8
DSAI —	Area Elective	3	8
— —	Complementary Elective	3	7
— —	Complementary Elective	3	7
Total		12	30

Second Semester		Credit	ECTS
DSAI —	Area Elective	3	7
DSAI —	Area Elective	3	7
— —	Complementary Elective	3	6
DSAI 700	Graduate Seminar	0	2
DSAI 699	Guided Research	0	8
Total		9	30

		ECTS
—	Qualifying Exam	30
—	Thesis Proposal Defense	30
DSAI 790	PhD Thesis	120
Total		180

Total Credits: 21
Total ECTS: 240
* All area elective courses must be chosen from the courses listed in Table 4.
** Credit and ECTS specified are the minimum required values.