BDSS-IGERT is a collaboration of the social, computational, statistical, and visual sciences. For more information or to talk with someone about the program, connect here.
As part of the BDSS-IGERT traineeship, IGERT Trainees will engage in a full disciplinary PhD program in one of the areas listed above, as well as in multidisciplinary curriculum that spans these areas.
In additional to curriculum requirements, BDSS-IGERT students are engaged in a wide variety of noncurricular training opportunities, including workshops, conferences, and multiple short-term and long-term research challenges, interdisciplinary research rotations on campus, and summer externships away from campus in private industry, government, non-profit groups/labs or in academic research settings.
Social Data Analytics at Penn State University
The curricular requirements of BDSS-IGERT correspond to those of the new Social Data Analytics dual-title PhD. It is briefly summarized below, with greater detail provided here.
The curriculum includes:
Core seminars for all IGERT Students.
- Approaches and Issues in Big Social Data
- Approaches and Issues in Social Data Analytics
Analytics distribution, one course in a core approach to analytics:
statistical / machine learning or visual analytics. Examples:
- Data Mining (STAT/IST)
- Machine Learning (CSE)
- Visual Analytics: Leveraging Geosocial Data (GEOG)
Social Data Analytics approved electives, collectively meeting minimum cross-disciplinary distribution requirements in social science, computational science, informational science, statistical science, visual analytics and ethics and scientific responsibility; examples:
- Vision-Based Tracking (CSE)
- Computational Regularity on Interdisciplinary, Large Data Sets (CSE)
- Pattern Recognition (CSE)
- Information Retrieval and Organization (IST)
- Web Analytics (IST)
- Spatial Analysis (GEOG)
- Most graduate courses in Statistics
- Political Event Data and Forecasting (PLSC)
- Social Network Analysis (SOC)
- Causal Inference (PLSC)
- Democratic Representation: Big Data Approaches (PLSC)
- Multilevel Modeling (SOC)
- Spatial Demography (SOC)
- Intensive Longitudinal Data (HDFS)
- Privacy in Statistical Databases (STAT/CSE)
- Data Privacy, Learning, and Games (CSE)
- Big Social Data and the Law (PLSC/CLJ)
- The Information Environment (IST)
BDSS-IGERT students must be admitted to a participating Penn State PhD program:
Statistics, Computer Science & Engineering, Information Sciences & Technology, Geography, a participating social science department in the College of Liberal Arts (Political Science, Sociology, Anthropology, Economics, Communication Arts & Sciences) or the College of Health & Human Development (Human Development & Family Studies, Health Policy & Administration). Students in the dual-title degree in Demography, regardless of home department, are also eligible. BDSS-IGERT is, for most departments, a program taken up in the second and third year of the PhD program. (For programs that admit directly to a Masters, they might take up IGERT in years one and two of the PhD; there may be other exceptions.)
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