DACSS Courses and Requirements

The Masters in Data Analytics and Computational Social Sciences degree requires satisfactory completion of 10 courses, or 30 credits. Coursework will consist of four required core courses, three technical electives, and three additional substantive or technical electives. Specialization tracks will provide students with suggested combinations of substantive and technical electives that are best suited to their career objectives.

Degree curriculum has been carefully designed to prepare students for the workforce and reflects current industry standards for data science professionals. The four core courses will ensure students are able to meet industry standards in all three areas, while an additional three technical electives will provide advanced training in programming and statistics to ensure graduates have cutting edge training in one or more methods such as survey research, network analysis or computational text analysis.

Core Courses (Four)

The four required courses will provide students with a solid grounding in data collection, programming and data management, statistical data analysis, data visualization and communication, and effective evidence-based decision-making. The four courses are: 1) Research Design for Social Scientists, 2) Data Science Fundamentals, 3) Introduction to Quantitative Analysis, and 4) Data Communication and Visualization.

CSS404/604 Research Design for Social Scientists
This course will provide an introduction to fundamentals of behavioral research including hypothesis testing, measurement, internal and external validation, along with an introduction to
a range of data collection and analysis methods used in social research (e.g., experiments, surveys, text analysis, econometrics). Students will learn how to design research that can address specific issues encountered in business and policy settings, and how to effectively use data analysis to address those questions and support effective managerial decision-making.
Faculty:
CSS402/602 Data Science Fundamentals
This course will introduce fundamental data science concepts and the world of big data, while continuing to help students develop their programming skills in R. It will also provide students with a solid grounding in general data management and data wrangling skills that are required in all advanced quantitative and data analysis courses.
Faculty: 
CSS405/605 Introduction to Quantitative Analysis
This course will provide students with an introduction to quantitative analysis in the social science, often referred to as econometrics or statistical analysis. The course will include a brief review of college-level statistics (required for all students prior to degree matriculation), and will then introduce students to stastical techniques including ordinary least squares (OLS), limited dependent variable analysis, and other regression analysis models. There will be two tracks through this requirement, one of which will more heavily emphasize the computational and interpretive aspects of quantitative analysis, while the other will require a stronger mathematical background and is a prerequisite for taking advanced econometrics courses in Resource Economics.
Note: students may substitute courses that are the disciplinary equivalent of CSS405/605 taught regularly in SBS departments: PS797, SOC711, ResEc701, Econ751/753, or Anthro597.
Faculty:
CSS403/603 Data Communication and Visualization
CSS403/603 will provide students with hands-on experience writing about and visualizing a range of data types with different communication goals. Data Visualization components will include the theory/concepts of visualization and hands-on work with ggplot2 package in R. The course is expected to be organized as a workshop type experience and may include one or more live client project experiences in the future.
Faculty: 

Technical Electives (3 or more)

Students will be required to take a minimum of three courses (nine credits) of advanced technical training in special data analytic methods to ensure that all graduates have cutting edge training in at least one specialized data analytic method. Examples of courses include: survey research, empirical text analysis, advanced quantitative methods in anthropology, geospatial analysis, modeling emergence and social simulation, experimental economics, political experiments, special topics in forecasting, panel data econometrics, social and political network analysis, and applied time series econometrics.

Network Analysis
  • POLSCI 753 Political & Social Network Analysis (Faculty: Rolfe)
  • SOCIOL 794N – Seminar – Social Network
  • SOCIOL 797NE – Special Topics – Network & Health

Survey Research

  • POLSCI 797R Survey Research Methods
  • SOCIOL 714/15 Survey Research Methods

Text and Content Analysis

  • POLSCI 791EA Empirical Analysis Ideologies
  • COMM 794A – S – Content Analysis (Faculty: Erica)

GIS & Spatial Analysis

  • REGIONPL 625 – Intro to Geographic Information Systems
  • RP 673 Spatial Analysis and Regional Development
  • Other GIS (Forrest Bollick or SPP)
Formal Models
  • SOCIOL 795E – S – Modeling Emergence
  • Econ 702 Game Theory and Strategic Interaction
Experiments
  • POLSCI 523 Experiments in Media, Politics, and Power
  • RES-ECON 797B – ST – Experimental Economics (Faculty: Oliveira)
Time Series Analysis
  • RES-ECON 797A – ST – Time Series & Forecasting  (Faculty: Morzuch)
  • ECON 797W Time Series Econometrics
Statistics & Econometrics
  • POLSCI 7XX Bayesian Statistics
  • RES-ECON 701 – Quantitative Methods (Faculty: Rojas)
  • RES-ECON 702 – Econometric Methods (Faculty: Bauner)
  • RES-ECON 703 – Topics In Advanced Econometrics (Faculty: Wang)
  • RES-ECON 797D – ST – Panel Data Econometrics (Faculty: Morzuch)
  • SOCIOL 712 – Grad Stat Soc Sci II
  • ECON 797B Labor Econometrics
  • ANTHRO 597AQ – Advanced Quantitative Analysis in Anthropology
  • COMM 621 Quantitative Methods in Research
  • RP 620 Quantitative Methods in Planning
Data Communication and Visualization
  • JOURNAL 397DJ Data-Driven Storytelling (Faculty: Zamith)
  • JOURNAL 392P Writing for Public Relations
  • JOURNAL 435 Web Design for Journalists (Faculty: Braun)

Substantive Electives (Up to 3)

Students may take up to three courses that provide substantive background in a range of topics (such as health, race, inequality, social media, and/or immigration) and analytical approaches (such as public policy, public opinion, organizational theory, cultural theory, and/or economics). Specific courses will be identified as part of pre-defined specialization tracks and be listed as permanently approved electives for this degree. Students may also petition for additional courses to fulfill the substantive elective requirements. Examples of substantive courses that may interest degree students include: policy evaluation, demography, networks and health, political behavior, and industrial organization.

  • COMM 497DB Survey of Digital Behavioral Data – syllabus (Xu)
  • SPP597 Internet Governance & Information Policy – syllabus (Bautista)
  • SPP651 Social Inequalities, Technology and Public Policy – syllabus (Bautista)
  • JOURNAL 333 Introduction to Visual Storytelling
  • JOURNAL 310 International Journalism (Faculty: Zamith)
  • JOURNAL 335 Public Relations
  • JOURNAL 490B Public Relations & Integrated Communication Cases
  • RES-ECON 732 – Indus Org I – Res Ec
  • RES-ECON 797M (733) Industrial Organization
  • POLSCI 791PP Political Psychology (Faculty: Nteta)?
  • POLSCI 791V Political Behavior Prosem (Faculty: Nteta)
  • POLSCI 780 Public Policy Prosem
  • POLSCI American Political Institutions Prosem

Specialization Tracks

In order to help guide students through coursework that best prepares them for their workforce requirements, degree faculty will develop several specialization tracks through required and optional coursework that reflect future career goals. Examples of specialization tracks include: Data Science (Technical); Population and Policy Analysis; Behavioral Analysis; Organizational and Market Analysis; Culture, Communication and Media Analysis; and Economic and Financial Analysis.