Students are required to complete minimum four graduate courses (min 12 units), relevant to the Designated Emphasis. This course work needs to be completed before the Qualifying Exam (QE). These include:
- CMN 275Y: Hybrid online course with lecture recordings and weekly face-to-face discussion sessions with participating faculty members (offered once a year). Since contributing faculty members rotate each year, the precise content and order will evolve in each offering. The general outline can be found in this generic syllabus here.
- At least one course from each of the three categories of Elective Courses (see right). At least one of these three courses needs to be taken from a program other than the students PhD program.
Courses used to fulfill the DE requirements can be S/U graded
. However, please note that courses that overlap between the DE and the Ph.D. degree requirement coursework need to be letter-graded (as of Ph.D. requirements).
The student’s Qualifying Examination Committee must include at least one member of the DE. The Chair of the DE and the student’s Ph.D. program Graduate Adviser must co-sign the Qualifying Examination Committee form, which is submitted to Graduate Studies for approval by the Dean of Graduate Studies. When sending the form for signature, please specify which course work you have done to fulfill the DE requirements. The Qualifying Examination will assess the student’s depth and breadth of knowledge within the area of the DE, as well as the Ph.D. program area. Satisfactory performance on the Qualifying Examination for the Ph.D. will be judged independently from performance on the DE. Thus, an allowable outcome of the Qualifying Examination is that the student’s performance may be “passing” for the Ph.D. but “not passing” for the DE. In this event, the Executive Committee of the DE will define a plan for remediation. The plan may include, but is not limited to re-examination by the DE Executive Committee, coursework, teaching, or preparation of a paper. If the student is re-examined, the outcome is limited to “pass” or “fail”. If the student receives a “fail”, the student is disqualified from the DE.
The dissertation shall contain original research connected with the DE and its relation to the student’s research in the home program, commonly by applying or developing computational social science methods. The student’s Dissertation Committee shall be selected in accordance with the regulations of the Ph.D. program, but must include at least one member of the DE. The DE member may be the Dissertation Committee Chair.
The DE will appoint a faculty advisor for each student admitted, in agreement with the student’s main advisor. This advisor may be different from the advisor appointed by the student’s Ph.D. program, and if so will serve the candidate only in matters relating to the DE.
Degree Conferral Process
The Designated Emphasis will be awarded solely in conjunction with the Ph.D. and will be signified by the degree designation “Ph.D. in X with Emphasis in Computational Social Science” where X is the Ph.D. program.
Students must take three Elective Courses, totaling 12 units. Besides the courses listed below, students can petition for ad-hoc approval of other elective courses in each category from the Executive Committee of the DE
(especially 280s and 290s Special Topics and Seminar courses from the affiliated PhD programs or appropriate undergraduate courses, or the popular ECS 253). In this sense, the courses listed below aim at communicating the nature of the three categories, while specific courses can be approved by the Executive Committee in an ad hoc manner (see *footnote).
Computational & Mathematical & Statistical Foundations
- PHY 256A&B. Physics of Information
- ECS 222A. Design and Analysis of Algorithms
- ECS 235A. Computer and Information Security
- ECS270. Artificial Intelligence – 3 units
- ECS271. Machine Learning and Discovery
- ECS272. Information Visualization
- ECN 240A. Econometric methods
- GEO 200CN. Quantitative Geography
- LIN 277. Computational Linguistics
- POL 281. Statistical Computing Issues in Political Science
- STA 243. Computational Statistics
- STA 250. Special Topic: Data, Computing and Science
- STA 260. Statistical Practice and Data Analysis – 3 units
Social Science Theory and Substance
- CMN 233. Persuasive Technologies for Health
- CMN 251. Digital Technology and Social Change
- CMN 252. Computer-Mediated Communication
- CMN 255. Social Media
- LIN231. Syntactic Theory
- POL 215. Introduction to Modeling Political Behavior
- POL282. Advanced Modeling of Political Behavior
- PSC 241. Attitudes and Social Influence
- SOC 220. Deviance, Law, and Social Control
- SOC 226. Sociological Social Psychology
- SOC 265B. Theory in Contemporary Sociology
- SOC 280. Organizations and Institutions
Applied Computational Social Science
- CMN 212. Web Science Research Methods
- CMN 213. Simulation Methods in Communication Research
- CMN 214. Analysis of Communication Networks
- ECN 203B. Game Theory
- ECN 230A. Public Economics
- LIN 227. Text Processing and Corpus Linguistics
- POL 279. Political Networks: Methods and Applications
- POL 284. Advanced Network Analysis
- PSC 211. Advanced Topics in Neuroimaging – 2 units
- PSC 209A Introduction to Programming: Matlab
- SOC 206. Quantitative Analysis in Sociology
- SOC 207A. Methods of Quantitative Research
- SOC 208. Topics in Advanced Quantitative Methods in Social Science