Common Program Courses

This is a list of the courses common to all Program Tracks. Other pages of interest may be as follows.

• Electives Courses

• Core Area Courses (for Option 1 track only)

DS 301 Foundations of Data Science

Data science history, concepts, and underlying philosophy, data cycle and handling and the associated legal & ethical frameworks

• Prerequisite: None

• Credit: 3 units

• No. of hours: 3 hours

• Meeting type: Lecture

This introductory course provides a general entry point into the data science field. The course covers basic philosophical underpinnings of data science in the context of its historical perspective and the increasing utility in the information age. Other topics relevant to the understanding of the subsequent analytical and computational foundations and tools are also added to increase their readiness and correct scientific perspective. Familiarity with the existing data privacy laws will also be taken up here. Finally, extensive discussions through case studies of data-related situations will be undertaken to identify, debate, and resolve the ethical and moral issues in decisions and policies of individuals, corporations, and/or governments. This last topic is crucial for their future career as data scientists and related professions. Everyone who is in the Ph.D. (Data Science) program is required to take this course.

DS 396 Graduate Seminar

Seminar course on recent work in developing concepts, tools, and methods in Data Science

• Prerequisite: DS 301 Foundations of Data Science

• Credit: 1 unit

• No. of hours: 1 hour

• Meeting type: Seminar

This course may be taken up to three (3) times. Those admitted in the program with bachelor’s degree (Option 1) are required to have finished at least 12 units of course work under the curriculum. This course aims to update the student or candidate in the recent research works regarding the field of Data Science and to help in developing research ideas for dissertation via seminar discussion or reading of latest scientific articles. The stipulation to be taken three times is a condition for the possibility of requiring non-thesis undergraduate and graduate degrees (e.g. professional masters).

DS 397 Special Topics in Data Science (Options 1 & 2)

• Prerequisite: COI

• Credit: 3 units

• No. of hours: 3 hours

• Meeting type: Lecture

This course may be taken up to three (3) times provided the subject titles are different. This course updates the student on the recent trends and advances in Data Science and related fields. The field of data science has many aspects that special topics may arise as a viable research direction.

DS 398 Advanced Studies in Data Science (Option 3 only)

Conduct of directed, specific research on a problem in the field of specialization, preparation and submission of scientific manuscript in a highly reputable refereed journal

• Prerequisite: COI

• Credit: 4 units

• No. of hours: 12 hours

• Meeting type: Laboratory

This course must be taken twice. Each course may be split into two separate semesters with two (2) units each. It allows students to conduct advanced studies related to their dissertation. Expected output is a paper submitted for publication in a highly reputable refereed journal.

DS 399 Research Methods (Options 1 & 2)

Development and discussion of applicable research methods for and consideration of ethics in dissertation topic proposal

• Prerequisite : COI

• Credit : 3 units

• No. of hours : 3 hours

• Meeting type : Seminar

To be enrolled in this course, the student must have already passed the Ph.D. Candidacy Exam and is graduating in status (i.e. only DS 400 is left in the succeeding semester/s). For Option 1 and Option 2, this provides an opportunity for the student to focus on developing the dissertation topic proposal with the research adviser. This course will end up with both a candidacy exam and a dissertation topic proposal. The course is handled by the research adviser. This course institutionalizes the common practice of research meetings.

DS 400 PhD Dissertation

• Credit : 12 units

• No. of hours : 12 hours

• Meeting type : Independent study

To be enrolled in this course, the student must have passed the candidacy exam and completed all other course requirements. It maybe taken in parts, provided a total of 12 units of DS 400 has been taken before being allowed to graduate. Each part may only be broken into 3-unit, 4-unit and 6-unit courses, or their multiples (i.e., 3, 4, 6, 8, 9, or 12 units respectively corresponding to 3, 4, 6, 8, 9, or 12 hours of independent study)

This course tests the PhD candidate’s ability to satisfactorily defend the novelty and significance of his/her body of work on Data Science. The course may be taken multiple times according to the broken down number of units per take. The adviser may impose requirements according to the appropriate units such as submission of a manuscript to a listed journal, presentation in a colloquium, dissertation manuscript endorsed by adviser and all readers, or a PhD dissertation defense. The public presentation and discussion while enrolled in this course will showcase the latest research results and progress and provide a sense of unity in the dissertation work. The submission of a manuscript to a journal is intended to subject the study to examination of other experts in the same fields and have the research published the soonest. The dissertation defense will be required in the last part or final units of the course.

This course is also the culmination of the student’s research work. Requires passing rating in an oral presentation to a panel and submission of the duly endorsed dissertation manuscript bound copies. The dissertation work can also come from a capstone project in connection to an industry partner (private, government or non-government) following standard classification schemes for dissertation manuscript (“F”, “P”, etc.) as recommended by the unit program committee and approved by the Program Committee.