Program Director (Data Science): Yolanda Gil, PhD
Program Co-Director (Public Policy): T.J. McCarthy, PhD
The Master of Science in Public Policy Data Science is a cross-disciplinary joint degree program offered jointly between the Viterbi School of Engineering and the Sol Price School of Public Policy. Students complete a core set of courses to provide a foundation in data science and public policy and choose electives to optimize their preparation for their preferred career path and unique professional opportunities. This program blends fundamentals of data science with public policy and uniquely prepares students for careers in government agencies to leverage new data technologies to inform public policy decision-making. The increasing availability of data is revolutionizing the way many agencies operate, particularly with respect to governance transparency and accountability, law enforcement, transportation and housing policy. This is causing profound changes in strategies for crime-fighting, defense, national intelligence, social programs, and finance and operations of agencies. The effective use of data science holds the potential to revolutionize how public policy is created.
The curriculum is designed to be accessible to students with any background, including students with a public policy background and no computer science knowledge as well as students with a computer science background and no public policy knowledge. Students with undergraduate degrees in computer science, engineering, science or mathematics will acquire the necessary knowledge to analyze health data with diverse sources and purposes and can request to replace introductory data science courses with more advanced ones. Students with undergraduate degrees in public policy, political science, economics, and other social sciences will acquire formal and practical data science skills and can request to substitute introductory courses in public policy with more advanced ones. There is no requirement of prior knowledge of programming or computer science, as the curriculum is designed with special introductory courses that are accessible to students with diverse backgrounds.
Students will learn a range of data science skills such as developing scalable data systems, using state-of-the-art software and infrastructure for data science, designing data analyses with statistical methods, applying machine learning and data mining techniques, designing effective visualizations, and working in multi-disciplinary data science teams. They will also learn the foundations of economics, cost-benefit analysis, and statistical and econometric methods that should be coupled to effectively analyze public policy challenges, and inform and guide public policy decisions. Through a capstone project, students will work in groups to produce a consulting-style report, which will demonstrate their capabilities and skills for the job market.
Prospective students can refer to the Price website for more information about the program.
Throughout their course work, students will assemble a digital portfolio that will allow help them demonstrate their capabilities and skills for the job market. Students will also have ample opportunities to be involved in practical public policy data science projects on campus. Upon graduation, students will have both foundational and practical skills for a successful career in public policy data science.
Students can pursue careers in a variety of sectors. Many government agencies now have a dedicated data science and/or innovation unit, while several leading technology companies have created specialized divisions and products aimed at leveraging these tools in the public policy realm. Understanding and knowing how to use effectively data science is crucial for work in nongovernmental organizations and nonprofits as well. Application areas for public policy data science include healthcare access, fair housing practices, crime pattern identification, and education financing.
Applications will be reviewed by both the Price School of Public Policy and the Viterbi School of Engineering.
Applicants should have a bachelor’s degree in statistics, economics, mathematics, sociology, public administration, accounting, or finance, a strong background in math and analytical courses, and a satisfactory cumulative undergraduate GPA (grade point average).
Prospective students will need to complete and submit:
- The USC Graduate Admission Application, along with:
- Electronic Transcripts
- Personal Statement
- Letters of Recommendation (3, including at least one academic reference)
- English Proficiency Requirement (International Students Only)
- Financial Documentation (On-Campus International Students Only)
The most up to date description of the program is in the USC Catalogue. Course descriptions are available here.
Total Units: 36
All entering students must have a bachelor’s degree from an accredited institution and are required to demonstrate proficiency in foundational statistical methods. The statistics prerequisite can be satisfied in one of two ways:
- Statistics or Econometrics Course Taken Prior to Enrollment
Completion of a college-level statistics or econometrics course with a grade of “B” or better within three years of matriculation. At a minimum, prior coursework must have included essential topics in descriptive and inferential statistics such as measures of central tendency and dispersion, confidence intervals, and hypothesis testing. If relevant statistical coursework was completed more than three years prior, a waiver may be granted based on the level of statistical training completed and the degree to which currency with this material was maintained through subsequent professional use.
- PPD 504 Essential Statistics for Public Management
If a student has not completed a college-level statistics course with a grade of “B” or better within three years of matriculation, they will need to complete a summer course prior to starting the MPP program. USC offers PPD 504 Essential Statistics for Public Management each summer; a grade of “C” or better in PPD 504 is sufficient to satisfy the statistics prerequisite. Students may instead choose to satisfy the requirement by completing a qualifying statistics course at another institution prior to enrolling at USC. If the course is completed outside of USC, it must be taken for a grade and the student must earn a grade of “B” or better. Regardless of which course is used to meet the prerequisite requirement, the units associated with the course will not be used toward the 36 required degree units.
Pre-Semester MPPDS Labs
The Professional Fundamentals Lab provides an introduction to the program, acclimates students to skills that will be further developed in their first-semester courses, and help to create a genuine camaraderie within the first-year student cohort. Entering MPPDS students are required to participate in the Professional Fundamentals Lab. The lab takes place the week prior to the start of the fall semester. The Professional Fundamentals Lab will lead directly into PPD 554 Foundations for Policy Analysis. Entering MPPDS students are also required to participate in the Statistics/STATA Lab in January. The Statistics/STATA Lab meets the week prior to the start of the spring semester. The Statistics/STATA Lab will provide initial exposure to the statistical software that will be utilized extensively in PPD 558 Multivariate Statistical Analysis.
Core Courses (24 units):
- DSCI 510 Principles of Programming for Data Science (4 Units)
- DSCI 549 Introduction to Computational Thinking and Data Science (4 Units)
- DSCI 550 Data Science at Scale (4 Units)
- PPD 503 Economics for Public Policy (4 Units)
- PPD 554 Foundations of Policy Analysis (4 Units)
- PPD 558 Multivariate Statistical Analysis (4 Units)
Data Science Elective - Choose One Course (4 units):
- DSCI 551 Foundations of Data Management (4 Units)
- DSCI 552 Machine Learning for Data Science (4 Units)
- DSCI 553 Foundations and Applications of Data Mining (4 Units)
- DSCI 554 Data Visualization (4 Units)
- DSCI 555 Interaction Design and Usability Testing (4 Units)
- DSCI 556 User Experience Design and Strategy (4 Units)
Public Policy Elective - Choose One Course (4 units):
- PPD 555 Public Policy Formulation and Implementation (4 Units)
- PPD 560 Methods for Policy Analysis (4 Units)
- PPDE 668 Applied Econometrics for Program Evaluation (4 Units)
Capstone (4 units):
- DSCI 560 Data Science Professional Practicum (4 units)
- Students with a computer science background will have the option of replacing DSCI 510, 549 and DSCI 550 with DSCI 551, DDSCI 552 and DSCI 553.
For prospective students, visit the Viterbi Graduate Admission website for more information.
For current students, please contact Suzanne Alexander (Program Administrator, Price Student Affairs) for questions and advisement regarding the M.S. in Public Policy Data Science.