Academic Policies

Admission Requirements

The requirements for admission is found in the Admission Page.

Retention and Maximum Residence Rule

The program follows the graduate program retention rules of the University for students enrolled in a doctorate program. Students who do not satisfy the grade requirements will be dropped from the Program, unless the Data Science Graduate Committee decides based on justifiable grounds and upon recommendation of the Graduate Committee of the implementing unit and favorable endorsement by the program/research adviser.

The general university rules for maximum residence apply for Option 1 and Option 2. Option 3 will have a maximum residence of five (5) years.

Shifting/Transfer

Graduate students from among the implementing units, or from any UP Constituent University, may apply to shift to the program subject to the admission requirements imposed by the Graduate Committee of the accepting implementing unit.

Graduate students from other higher educational institutions seeking transfer into the University must satisfy the admission requirements of the University and those imposed by the Graduate Committee of the implementing unit as approved by the unit Graduate Council.

Successful transfer into the Program shall follow the admission protocol related to the assignment of a mentor and approval of the Program of Study.

The Graduate Committee may impose additional subjects or waive program courses subject to the approval of the Graduate Committee Chair according to the rules of the University.

A student may shift his/her implementing unit upon approval of the Graduate Committee Chair provided that the shifting application is: (1) duly endorsed by the originating Graduate Committee, and (2) accepted by the receiving Graduate Committee. This change of implementing (home) unit may be primarily out of convenience such as a change of mentor(s) or implementation site of research work. Retention and graduation rules will follow that of the accepting implementing unit.

Graduation Requirements

Requirements per program track is found in the Graduation Requirements Page. Some of the requirements are described below.

Qualifying Examination (Option 1)

A qualifying exam is given to an applicant to be qualified as a PhD student. This is a milestone that ensures the student has mastery of fundamental and applied scientific methods in studying and understanding the field. The Graduate Committee of the implementing institution may choose to implement an oral exam if deemed sufficient. The qualifying examination for those with master's degree are usually waived.

Candidacy Exam

The minimum requirement before an applicant is qualified to take the qualifying exam is either passing the core foundational courses or having a master’s degree. The unit’s Graduate Committee may decide to require a similar examination for Options 2 & 3 if they are coming from a non-thesis graduate degree.

The candidacy examination is an important milestone to ensure that the student has good scientific appraisal of the field (Data Science) and its implications to the domain of expertise of the student. Requirements:

1. Pass Qualifying Exam (Option 1 only); and

2. Pass all required seminar course/s.

Passing the candidacy examination marks the point when the student becomes a PhD candidate.

Dissertation Topic Defense

This milestone ensures that the candidate has acquired an adequate level of expertise in generating a sound proposal to solve a given problem. Requirements:

1. Passing the candidacy exam; and

2. Dissertation topic proposal manuscript endorsed by the Adviser(s), Co-adviser(s) if any, and Reader(s)

The dissertation topic defense may be held right after the candidacy examination as a natural part of the panel examination. In case the Topic Defense is held in succession with the Candidacy Exam, the topic may not be approved even if the student passed the candidacy exam. The examination panel for the Topic Defense may require another topic defense until approved. In another case that the student does not pass the Candidacy Exam, the topic is not to be approved. In both cases, the topic proposal manuscript must first be endorsed by the Readers and the Adviser before the candidate is allowed to present.

The implementing unit’s Graduate Committee may require another Topic Defense only in the case of a significant change in research direction/topic; or change of a Program/Research Adviser.

Publication Requirement

The publication requirement proves a PhD candidate’s ability to satisfactorily contribute novel knowledge to the field. The minimum requirement is proof of acceptance in a highly-reputable refereed journal or conference proceeding (e.g., listed in SCOPUS or the SCIe index) in an approved list of acceptable journals and conferences whitelisted by the Graduate Committee of the implementing institution and approved by the Data Science Graduate Committee*. More stringent requirements may be imposed by the implementing institution provided it is approved by the Data Science Committee . A similar journal indexing system may be proposed provided it has more stringent inclusion criteria and duly approved by the Data Science Committee.

Graduate Colloquium

A public presentation and discussion of latest research results and progress providing a sense of unity in the dissertation work.

Dissertation Defense

This tests the PhD candidate’s ability to satisfactorily defend the novelty and significance of the body of work on Data Science. Requirements: (1) an approved dissertation topic, (2) dissertation manuscript endorsed by adviser and all readers; (3) graduate colloquium, and (3) satisfied publication requirements.

Pickup Degree (Option 1 only)

Depending on the program of study, the student will be awarded an appropriate pickup master’s degree if the student has completed all the core courses and elective courses in the first two years of the proposed program of study for Option 1 (i.e., for a total of at least 33-35 credited units), and in addition, must have either passed the Qualifying Examination or submitted a data science project (in lieu of not successfully passing the Qualifying Examination and so not able to continue further with the PhD program). Depending on the program of study and/or taken courses, students may earn the following pickup degree, provided that the requirements for the pickup degree are met.

1. Professional Master in Data Science (Analytics)

2. Master of Engineering in Industrial Engineering (Analytics Systems Engineering Specialization)

3. An appropriate non-thesis master’s degree as identified by the degree offering unit and endorsed by the Data Science Committee

The implementing unit that is also the degree-offering unit for the master’s program shall be responsible for ensuring that the requirements for the intermediate master’s degree are met. The degree-offering unit is also the one to endorse the conferment of the master’s degree consistent with university rules and existing guidelines for such master’s program.

• * Some of the leading Data Scientists around the world are mostly known to publish in reputable conferences. Identification of such quality conferences may be done by the respective units. A whitelist of such conferences may be proposed by the implementing unit and approved by the Data Science Council. Such whitelists shall be updated and approved by the Data Science Council annually.