Political Economy with Data Analytics, MA
The Master of Arts in Political Economy with a Data Analytics Emphasis is a three-semester interdisciplinary program. The major goal of the program curriculum is to train public policy specialists using advanced technologies and data analytics. This program is available for Tulane undergraduate students as well as external candidates.
A 4+1 option is available for Tulane undergraduate students.
The M.A. in Political Economy with Data Analytics requires a minimum of 30 credit hours and a cumulative grade point average of 3.000. Details about program requirements can be found below.
See Recommended Plans of Study below.
Curriculum Requirements
External M.A. Curriculum
While the program is designed for a three-semester timeline, there is flexibility in course selection and credit hour distribution each term. Additionally, students may opt for a fourth semester if necessary to accommodate their academic or professional goals.
4+1 M.A. Curriculum
The 4+1 program offers a streamlined path to earning an MA in Political Economy with Data Analytics and is open to Tulane undergraduate students of all academic backgrounds. While all students must meet the program prerequisites before beginning the +1 portion of their studies, the program is designed to accommodate those with or without prior coursework in related fields. Students must apply through the School of Liberal Art’s Graduate Admissions Office (https://liberalarts.tulane.edu/academics/graduate-studies/prospective-students) and be accepted into the program before taking any graduate-level classes.
Course Requirements
| Course ID | Title | Credits |
|---|---|---|
| Core Courses | ||
| PECN 6100 | Empirical Approaches to Political Economy | 3 |
| PECN 6200 | Advanced MA Seminar for Political Economy | 3 |
| PECN 6970 | Mathematics for Data Analysis | 3 |
| Methods-Based Electives (select five from the list) | 15 | |
| Econometrics | ||
| Topics-Mathematical Econ | ||
| Special Topics in Economics | ||
| Econometrics III | ||
| Elements in Biomedical Informatics | ||
| Introduction to Data Science for Biomedical Informatics | ||
| Fundamentals of Data Analytics | ||
| Statistical Machine and Deep Learning in Biomedical Practice | ||
| Intermediate Biostatistics | ||
| Database Management | ||
| Data Management and Statistical Computing | ||
| Introduction To ArcGIS | ||
| Introduction to Methods in Data Science | ||
| Public Health GIS II | ||
| Statistical Inference I | ||
| Regression Analysis | ||
| Design of Experiments | ||
| Categorical Data Analysis | ||
| Nonparametric Statistics | ||
| Research Design | ||
| Introduction to Computer Science | ||
| Intro Artificial Intelligence | ||
| Introduction to Data Science | ||
| Intro to Machine Learning | ||
| Introduction to Deep Learning | ||
| Data Visualization | ||
| Algorithms | ||
| Machine Learning | ||
| Natural Language Processing | ||
| Data Science | ||
| Advanced GIS | ||
| Intro to GIS | ||
| Intro Remote Sensing | ||
| Remote Sensing for Env Anlys | ||
| Geospatial and Numerical Methods | ||
| Mathematical Statistics | ||
| Linear Models | ||
| Intro to Statistical Inference | ||
| Scientific Computing I | ||
| Time Series Analysis | ||
| Analy Methods Appl Math | ||
| Applied Mathematics I | ||
| Data Analysis | ||
| Modeling and Analytics | ||
| Web Analytics | ||
| Advanced Modeling and Analytics | ||
| Special Topics | ||
| Spec offerings Pol Sci | ||
| Quantitative Methods I | ||
| Biostatistics for Public Health | ||
| Introduction to GIS for Public Health | ||
Additional Electives may be approved by the Program Director | ||
| Two Topics-Based Electives from the Following | 6 | |
| Regulation | ||
| Intl Trading Relations | ||
| Health Econ & Policy | ||
| Econ Public Expenditures | ||
| Economics of Taxation | ||
| Public Finance & Public Policy | ||
| Comparative Economic Systems | ||
| Labor & Pop In L.A. | ||
| Econ Devel of Latin America | ||
| Inequality and Poverty in Latin America | ||
| Labor & Population in Lat Amer | ||
| Seminar On Latin American Countries | ||
| Economics of Poverty | ||
| Economics of Education Policy and Reform | ||
| Special Topics In Econ | ||
| U S Labor and Migration | ||
| Theories of Economic Justice | ||
| Dissertation Prospectus Seminar | ||
| Issues In Soc of Gender | ||
| Race, Crime and Control | ||
| Crime and Human Development | ||
| Sociology of Fraud & White-Collar Crime | ||
| Social Movements and Collective Behavior | ||
| Sustainable Development in Latin America | ||
| Health Systems Policy and Management | ||
| Health Equity | ||
Additional Electives may be approved by the Program Director | ||
| Total Credit Hours | 30 | |
Recommended Plans of Study
Three Semester Plan of Study
This is an example plan of study for the three-semester MA in Political Economy with Data Analytics encompassing all requirements for the program. Students are responsible for reviewing university, school, and program requirements, along with prerequisites and the sequencing of courses in coordination with their program advisor.
| Year 1 | ||
|---|---|---|
| Fall | Credit Hours | |
| PECN 6100 | Empirical Approaches to Political Economy | 3 |
| PECN 6970 | Mathematics for Data Analysis | 3 |
| Methods-Based Electives | 3 | |
| Credit Hours | 9 | |
| Spring | ||
| Methods-Based Electives | 12 | |
| Credit Hours | 12 | |
| Year 2 | ||
| Fall | ||
| PECN 6200 | Advanced MA Seminar for Political Economy | 3 |
| Topics-Based Electives | 6 | |
| Credit Hours | 9 | |
| Total Credit Hours | 30 | |
4+1 Plan of Study
This is an example plan of study for the 4+1 MA in Political Economy with Data Analytics encompassing all requirements for the program. Students are responsible for reviewing university, school, and program requirements, along with prerequisites and the sequencing of courses in coordination with their program advisor. Undergraduate students are permitted to apply up to six credits toward both their undergraduate and graduate degrees. Remaining graduate coursework completed during the fourth year of undergraduate study, typically up to 6 additional credit hours, applies only to the graduate degree.
Senior/Final Undergraduate Year
| Course ID | Title | Credits |
|---|---|---|
| FALL | ||
| PECN 6100 | Empirical Approaches to Political Economy (Graduate Credit Only) | 3 |
| PECN 6970 | Mathematics for Data Analysis (Graduate Credit Only) | 3 |
| Credit Hours | 6 | |
| SPRING | ||
| Methods-Based Electives (Graduate & Undergraduate Credit) | 6 | |
| Credit Hours | 6 | |
Graduate Year
| Course ID | Title | Credits |
|---|---|---|
| FALL | ||
| PECN 6200 | Advanced MA Seminar for Political Economy | 3 |
| Methods-Based Electives | 6 | |
| Credit Hours | 9 | |
| SPRING | ||
| Methods-Based Electives | 3 | |
| Topics-Based Electives | 6 | |
| Credit Hours | 9 | |
| Total Credit Hours | 30 | |
Program String and Field of Study: LAMA_GR, PEDA
For more information, contact the School of Liberal Arts.