School of Economic, Political and Policy Sciences
Master of Science in Social Data Analytics and Research
36 semester credit hours minimum
Faculty
Professors: Kurt J. Beron, Patrick T. Brandt, Euel W. Elliott, Daniel A. Griffith, Dohyeong Kim, Dong Li, Robert C. Lowry, Fang Qiu, Meghna Sabharwal, Donggyu Sul
Associate Professors: Rodney Andrews, Simon M. Fass, Seth Giertz, Evgenia Gorina, Tomislav Kovandzic, Michael Tiefelsdorf
Associate Professor of Instruction: Karl K. Ho
Associate Professor of Practice: Timothy M. Bray
Mission
The mission of the Master of Science (MS) in Social Data Analytics and Research (SDAR) is to equip individuals with rigorous multi-disciplinary proficiency in methods of social data production, collection, investigation and visualization using the big data approach for which there is a strong and increasing demand in the public, nonprofit, and private sectors, as well as in doctoral programs and other advanced research organizations.
The MS in SDAR degree program builds on faculty expertise in criminology, economics, geospatial information sciences, political science, public and nonprofit management, public policy, political economy and sociology to equip students with advanced data science training that is widely applicable in a variety of fields using big data and social data.
The MS in Social Data Analytics and Research endows students with a clear understanding of algorithmic modeling and collection of social big data using modern methods such as API and search-based web scrapping. It encourages reflection not only on core methods, theories and philosophical dimensions of social science practice, but also emerging data analytic methods such as social media data collection, social network analysis, machine learning, spatiotemporal mapping, data mining and visualization, and big data management. It also fosters an appreciation of the role of social data analytic methods in helping to shaping public policy and action through participation in the evaluation of policies and programs in place as well as through the formulation of new ones based on the outcomes of data analysis.
Objectives
Taught by internationally recognized faculty at the University of Texas at Dallas, graduates of the Social Data Analytics and Research program develop advanced expertise in:
- big data analytic skills and capabilities in practice to sustain public, nonprofit, and private sector organizations as they address pressing societal issues on both local and global scales;
- Quantitative and qualitative data discovery, analysis and visualization;
- Data science and social science methodologies, theories as well as the ethics of data science practice in the areas of public policy and services.
Responding to an ever-increasing need across public and private sectors for social data analytics and research, the program equips students with rigorous and versatile multidisciplinary proficiencies in data collection, management and investigation. Students completing the degree will justify the importance of social big data analytics and research in helping to shape public policy and action and successfully build career paths in fields of employing social data.
Facilities
Students have full access to four state-of-the-art computer laboratories housed in the School of Economic, Political and Policy Sciences. Open for extended hours including evenings and weekends, each laboratory is equipped with full multimedia systems and contains 24 to 30 computers. All computers are network linked and hold full suites of leading survey, qualitative, spatial and statistical analysis software, including R, Python, JAVA, Qualtrics, NVivo, ArcGis, ENVI, EViews, STATA, and SAS. The University's computer labs also provide desktop computers and UNIX workstations for student use throughout the campus. These include computing facilities in the Erik Jonsson School of Engineering and Computer Science and in the NASA Center for Excellence in Remote Sensing in the Department of Geosciences. Key data sources and reference materials are readily available online through the University library and the School's memberships in various professional organizations.
Admissions Requirement
The University's general admission requirements are discussed on the Graduate Admission page.
The MS in Social Data Analytics and Research invites applications from students with a baccalaureate degree from an accredited higher education institution. Every application receives an all-inclusive review. In general, entering students should have earned a minimum 3.0 undergraduate Grade Point Average (GPA) on a 4.0-point scale, a verbal score of 150 and a quantitative score of 150 on the Graduate Record Examination (GRE). In addition to standardized test scores, which are only one of several factors taken into account in determining admission, students should submit all transcripts, three letters of recommendation, and a one-page essay outlining the applicant's background, education, and professional objectives.
Prerequisites
There are no specific prerequisites for admission to the MS in Social Data Analytics and Research. Several required courses, however, demand satisfactory prior completion of undergraduate college algebra and/or calculus.
Grading Policy
In order to qualify for graduation, students must maintain a minimum 3.0 grade point average in their degree program's core courses plus an aggregate grade point average of 3.0 for all graduate courses taken in the student's degree program at UT Dallas.
Degree Requirements
The University's general degree requirements are discussed on the Graduate Policies and Procedures page.
Students seeking the MS in Social Data Analytics and Research must complete at least 36 semester credit hours of graduate coursework in the program and maintain at least a 3.0 (B) grade point average in order to graduate.
The program has three components: Required Core Courses (15 semester credit hours), Prescribed Analytical Electives (12 semester credit hours) and Prescribed Disciplinary Electives (9 semester credit hours), as follows:
I. Required Core Courses: 15 semester credit hours
EPPS 6302 Methods of Data Collection and Production
PPPE 6310 Research Design I
EPPS 6313 Introduction to Quantitative Methods1
or EPPS 7313 Descriptive and Inferential Statistics2
or EPPS 7316 Regression and Multivariate Analysis2
EPPS 6356 Data Visualization
or GISC 6363 Internet Mapping and Information Processing
In special circumstances, students may substitute alternative equivalent courses in the core with prior approval of the Program Director or the Associate Dean of Graduate Studies.
II. Prescribed Analytical Electives: 12 semester credit hours
Students complete twelve semester credit hours
EPPS 6317 (GISC 6317) Python Programming for Social Science
EPPS 6323 Knowledge Mining
EPPS 6326 (GISC 6323) Machine Learning for Socio-Economic and Geo-Referenced Data
EPPS 6346 Qualitative Research Orientation
EPPS 6352 Evaluation Research Methods in the Economic, Political and Policy Sciences
EPPS 6354 Information Management
EPPS 6355 Machine Learning for Social Text Data
EPPS 6356 Data Visualization
EPPS 7304 Cost-Benefit Analysis
EPPS 7318 Structural Equation and Multilevel (Hierarchical) Modeling
EPPS 7344 Categorical and Limited Dependent Variables
EPPS 7370 Time Series Analysis I
EPPS 7371 Time Series Analysis II
EPPS 7386 Survey Research
EPPS 7390 Bayesian Analysis for Social and Behavioral Sciences
EPPS 7V81 Special Topics in Social Science Research Methodology
ECON 6305 Mathematical Economics
ECON 6306 Applied Econometrics
GISC 6301 GIS Data Analysis Fundamentals
GISC 6321 Spatial Data Science
GISC 6363 Internet Mapping and Information Processing
GISC 6381 Geographic Information Systems Fundamentals
GISC 6384 Advanced Geographic Information Systems
GISC 7310 Advanced GIS Data Analysis
GISC 7360 GIS Pattern Analysis
GISC 7361 Spatial Statistics
Other analytical courses proposed by the student and approved by the Program Director or the Associate Dean of Graduate Studies are possible as long as student meets prerequisites or receives program approval.
III. Disciplinary Electives: 9 semester credit hours
Students complete nine semester credit hours in the following disciplinary domains (Criminology, Geospatial Information Sciences, Economics, Political Science, Public/Nonprofit Management, Public Policy/Political Economy, or Sociology) with courses prescribed by the respective EPPS Programs. The Program Director or the Associate Dean for Graduate Studies must approve course selection.
1. Prerequisite is College Algebra.
2. Prerequisite is Calculus.