The PhD in Data Science at the University of the Philippines aims to produce PhD graduates equipped with a good scientific mindset, with adequate technical skills, and a professional perspective of expanding the science of data, with data as carriers of information. Distinct from an MS degree holder, the PhD (Data Science) degree holder is expected to exceed the skills of those with a master’s degree in science (research).
The PhD graduate will therefore have a better integrative view of the field of data science beyond mastery of a specific chosen subfield. It is therefore typical of a PhD graduate to be able to defend the significance of a given research problem and the implications of a possible solution to other fields beyond its own. Results of a scientific study may also be found by a PhD to have entanglement with philosophy and our understanding of the true nature of things in a more holistic view.
At the end of the program, graduates are expected to:
1. Analyze the nature of data throughout its life cycle including the mechanism behind data generation and storage (domain knowledge base);
2. Apply multi-disciplinary theories and methods with their underlying philosophies for data analysis resulting from various phenomena and processes (data science application to domain);
3. Generate new data science theories, data models, architecture, and/or algorithms that can adapt to the evolving nature of data throughout its life cycle including generation, information representation, storage, and/or extraction (generation of new knowledge);
4. Apply data science solutions to real-world problems in collaboration with and/or among academic, industrial and public governmental institutions (collaboration in the application of data science); and
5. Practice ethical and lawful guidelines and policies related to the application of data science processes, methods and techniques, especially in the Philippine context (ethical practice).
This option is for those with a bachelor’s degree in the sciences and engineering, or in other fields relevant or data science. Under this program track, students are required to take graduate courses in mathematical, computational, and statistical methods. These courses, together with DS 301 (Foundations of Data Science), serve as foundational courses so students acquire the necessary minimum competencies to do advanced studies in data science. Compared to Option 2, this program track has more units required for elective courses. These additional units for electives allow the students to appreciate the many applications of data science and further their areas of interest (domain).
This option is intended for students with a master’s degree. Students applying for the program are expected to already have the necessary competencies to do advance studies in data science as well as the necessary background and skills in the practice of data science particularly in their domain expertise. Thus, relative to Option 1, this program track has fewer electives and does not require the foundational courses in mathematical, computational, and statistical methods. Applicants with master’s degree without the appropriate level of mathematical, computational, and statistical background may either opt to take Option 1 or enroll in non-degree course/s to attain the required level of competencies.
This one is for those with a graduate degree and are proven capable of doing independent research in data science or related disciplines. The student is required to have a research plan prior to enrollment into the program. Under this program track, the student is not required to take any elective course; instead, they shall be enrolled in laboratory and seminar courses to focus their work on research studies and/or publication requirements.
Notes:
*Master’s degree refers to Master’s degree in science or engineering with at least 24 units of graduate courses from a recognized institution of higher learning. Additional requirements will be imposed for those with professional master’s degree and/or may be required to take relevant credit courses before as non-degree being admitted to the program.
Table 1: Total number of units of the courses to be taken in the PhD DS program for different track options: Straight PhD (Option 1); Regular PhD (Option 2); and PhD by Research (Option 3). The requirements are also added for completeness of comparison. The Qualifying exam for Option 2 and Option 3 is waived due to MS degree.
Course Grouping/Requirements | Option 1 | Option 2 | Option 3 |
|---|---|---|---|
Core: DS 301: Foundations of Data Science | 3 | 3 | 3 |
Mathematical Methods | 3 -5 | ||
Computational Methods | 3 | ||
Statistical Methods | 3 | ||
Elective Courses | 21 | 9 | |
Special Topcs in Data Science (DS 397) | 9 | 9 | |
Advanced Studies in Data Science (DS 398) | 8 | ||
Research Methods (DS 399) | 3 | 3 | |
Seminar Course (DS 396) | 1 | 1 | 3 |
Qualifying Examination | Required | ||
Colloquium | Required | Required | Required |
Candidacy Examination | Required | Required | Required |
Dissertation Proposal | Required | Required | Required |
Dissertation (DS 400) | 12 | 12 | 12 |
TOTALS | 58-60 units | 37 units | 26 units |
To apply to the PhD Data Science program, please check the Application and Admission section of the Graduate Student Guide of the College of Science: https://science.upd.edu.ph/graduate-student-guide/