Graduate Program Requirements

Requirements for Doctor of Philosophy (Ph.D.) in Data Science

The goal of the doctoral program is to create leaders in the field of Data Science who will lay the foundation and expand the boundaries of knowledge in the field. The doctoral program aims to provide a research-oriented education to students, teaching them knowledge, skills and awareness required to perform data driven research, and enabling them to, using this shared background, carry out research that expands the boundaries of knowledge in Data Science. The doctoral program spans from foundational aspects, including computational methods, machine learning, mathematical models and statistical analysis, to applications in data science.

There are three categories of courses: Foundation (group A), Core (group B), and Elective and Research requirements (group C) for the graduate program. These course requirements are intended to ensure that students are exposed to (1) fundamental concepts and tools (Foundation), (2) advanced, up-to-date views in topics central to Data Science for all students (the Core requirement), and (3) a deep, current view of their research or application are (the Elective requirement). Courses may not fulfill more than one requirement.

The doctoral program is structured as a total of 52 units in courses from these group A, B, and C as described in detail here. Out of the 52 units, 48 units (or 12 courses) must be taken for letter grade and at least 40 units must be using graduate-level courses.

The remaining 4 (= 52 – 48) units are for professional preparation, consisting of 1 unit of faculty research seminar, 2 units of TA/tutor training and 1 unit of survival skills course taken for a passing (satisfactory) grade. Finally, as mentioned earlier, out of the 12 regular courses, at least 10 must be graduate-level courses; at most two can be upper-level undergraduate courses. 36 units or 9 courses must be completed within six quarters from the start of the degree program.

Research rotations provide the opportunity for first-year PhD students to obtain research experience in data analysis under the guidance of HDSI affiliated faculty members. Through the rotations, students can identify a faculty member under whose supervision their dissertation research will be completed.

A research rotation is a guided research experience lasting one quarter (10 weeks) obtained by registering for DSC 294 with an instructor. All Ph.D. students will participate in a minimum of 2 research rotations during their first year, and with a minimum of two different faculty members, and as much as three rotations including summer quarter. A student may rotate twice under the same faculty member as long as they rotate with at least two faculty members. The goal is to expose students to new methodological approaches or domain knowledge and help them find an advisor for their Ph.D. research.

Please refer to the HDSI Faculty page for a list of HDSI faculty members and their research interests. Students should reach out to faculty members to learn more about potential rotation opportunities.

An important goal of HDSI is to foster interdisciplinary collaboration. Specifically, collaboration between method researchers and domain researchers. Domain researchers specialize in a particular scientific field and collect or generate large datasets. Domain experts can partner with HDSI faculty and jointly advise a PhD. student. The site matchpoint.com was designed to facilitate the creation of such collaborations.

Outcome of each rotation program: A written report, or curated datasets with jupyter notebooks. Students are expected to work 10 hours per week.

Research rotations must be completed by the end of the Fall Quarter of the second year with a signed commitment form from a faculty advisor. Those who fail to identify a research advisor shall be advised to leave the doctoral program with an optional assessment for completion of a terminal MS-DS degree.

The goal of the preliminary assessment examination is to assess students’ preparation for pursuing a PhD in data science, in terms of core knowledge and readiness for conducting research. The preliminary assessment is an advisory examination.

There is an oral presentation that needs to be completed before the end of Fall quarter of the second year. The student will propose the topic of the presentation (e.g., the outcome of a research rotation or a literature survey). The Graduate Program Committee will set up a committee consisting of two members (students can suggest committee members but there is no guarantee that only those suggested faculty members will be chosen for the committee). The oral presentation from the student will be followed by a Q&A session by the committee members. The committee will assess both the oral presentation as well as the students academic performance so far (especially in the required core courses). Students who did not get a satisfactory evaluation will receive a recommendation from the Graduate Program Committee regarding ways to remedy the lacking preparation (e.g, suggestion of courses to be taken), or an opportunity to receive a terminal MS in Data Science degree provided the student can meet the degree requirements of the MS program.

A research qualifying examination (UQE) is conducted by the dissertation committee consisting of four or more members approved by the graduate division as per senate regulation 715(D). One senate faculty member must have a primary appointment in the department outside of HDSI. Faculty with 25% or less partial appointment in HDSI may be considered for meeting this requirement on an exceptional basis upon approval from the graduate division.

The goal of UQE is to assess the ability of the candidate to perform independent critical research as evidenced by a presentation and writing a technical report at the level of a peer-reviewed journal or conference publication. The examination is taken after the student and his or her adviser have identified a topic for the dissertation and an initial demonstration of feasible progress has been made. The candidate is expected to describe his or her accomplishments to date as well as future work. The research qualifying examination must be completed n