MPA-MS, Applied Statistics

Take action on critical public service issues with a comprehensive understanding of advanced statistical techniques. At NYU, you can earn both an MPA in Public & Nonprofit Management & Policy and an MS in Applied Statistics for Social Science Research within two years. You'll gain practical policy, management, and analysis skills while being immersed in the foundations of probability and statistics—learning how to implement a variety of methods appropriate for evidence-based policy and practice. 

Students in this program enjoy the benefits of being members of two leading professional schools at NYU. Join a vibrant community of students, practitioners, and scholars focused on the foundations of probability and statistics, public policy, and public service

PROGRAM STRUCTURE

Students matriculate in the fall term and enroll full-time, taking courses toward the MPA (30 credits) and MS (34 credits) degrees concurrently. Students who waive a required course will take an advanced elective in its place. NYU Wagner electives may be from the school or from an approved NYU graduate course outside of Wagner. Students graduate and receive a diploma from each school after successful completion of the dual-degree requirements for both degrees.
 

Total credits required: 64 (30 MPA + 34 MS)


Master of Public Administration (MPA) in Public & Nonprofit Management & Policy [30 credits]
Public Policy Analysis Specialization

  • 12 Core credits
    • CORE-GP 1018 Microeconomics (3)
    • CORE-GP 1020 Management & Leadership (3)
    • CORE-GP 1021 Financial Management (3)
    • CORE-GP 1022 Introduction to Public Policy (3)
  • 6 Public Policy Analysis specialization credits
    • PADM-GP 2140 Public Economics (3)
    • PADM-GP 2171 Evaluating Programs and Policies (3)
  • 9 Elective credits
    • The faculty strongly encourage MPA-MS students to take PADM-GP 2411 Policy Formation and Policy Analysis (3 credits) as one of their MPA electives.
  • 3 Capstone credits
    • CAP-GP 3401 (1.5) & CAP-GP 3402 (1.5) Capstone: Advanced Projects in Policy, Management, Finance, and Advocacy I & II; or
    • CAP-GP 3148 (1.5) & CAP-GP 3149 (1.5) Capstone: Advanced Research Projects in Quantitative Analysis I & II
  • In addition to the course and credit requirements above, all MPA students must satisfy Wagner's Professional Experience Requirement (PER).  
     

MS in Applied Statistics for Social Science Research [34 credits]
Data Science for Social Impact Concentration 

  • 24 Core Course credits
    • APSTA-GE 2003 Intermediate Quantitative Methods: General Linear Model (3)
    • APSTA-GE 2004 Introductory Statistical Inference in R or APSTA-GE 2122 Frequentist Inference (2)
    • APSTA-GE 2012 Causal Inference (3)
    • APSTA-GE 2044 Generalized Linear Models and Extensions (2)
    • APSTA-GE 2331 Data Science for Social Impact (3)
    • APSTA-GE 2017 Databases and Data Science Practicum (2)
    • APSTA-GE 2047 Messy Data and Machine Learning (3)
    • APSTA-GE 2351 Practicum in Applied Statistics: Applied Probability (3)
    • APSTA-GE 2352 Practicum in Applied Statistics: Statistical Computing (3)
  • 6 Data Science for Social Impact Concentration credits
    • APSTA-GE 2062 Ethics of Data Science (3)
    • APSTA-GE 2355 Data Science Translation: Writing, Speaking, and Visualization (3)
  • 4 APSTA-GE Elective credits
    • May include APSTA-GE 2310 internship
    • May not include APSTA-GE 2001, 2002, or 2085

Sample Schedule 

The MPA-MS program admits students for full-time study in the fall term. The program can be completed across two academic years.

Key to Course Codes 
-GP = Wagner Graduate School of Public Service course toward MPA (30 credits)
-GE = Steinhardt School of Culture, Education, and Human Development course toward MS (34 credits)
 

Fall, Year 1 [15 credits]

Registration residency: Steinhardt

  1. CORE-GP 1018 Microeconomics (3)
  2. CORE-GP 1022 Intro to Public Policy (3)
  3. APSTA-GE 2003 Intermediate Quantitative Methods (3)
  4. APSTA-GE 2351 Practicum in Applied Probability (3)
  5. APSTA-GE 2352 Practicum in Statistical Computing (3)

January, Year 1 [2 credits]

Registration residency: Steinhardt

  1. APSTA-GE 2011 Supervised and Unsupervised Learning (2)

Spring, Year 1 [15 credits] 

Registration residency: Steinhardt

  1. APSTA-GE 2004 Introductory Statistical Inference in R or APSTA-GE 2122 Frequentist Inference (2)
  2. APSTA-GE 2017 Databases and Data Science Practicum (2)
  3. APSTA-GE 2044 Generalized Linear Models (2)
  4. APSTA-GE 2062 Ethics of Data Science (3)
  5. CORE-GP 1020 Management and Leadership (3)
  6. CORE-GP 1021 Financial Management (3)

Summer, Year 1 [3 credits]

Registration residency: Wagner

  1. PADM-GP 2171 Evaluating Programs and Policies (3 credits)

Fall, Year 2 [13.5 credits]

Registration residency: Wagner

  1. APSTA-GE 2331 Data Science for Social Impact (3)
  2. APSTA-GE 2047 Messy Data and Machine Learning (3)
  3. APSTA-GE 2012 Causal Inference (3)
  4. CAP-GP 3401 Capstone: Advanced Project in Policy or CAP-GP 3148 Capstone: Advanced Research Projects  (1.5)
  5. PADM-GP 2140 Public Economics (3)

January, Year 2 [3 credits]

Registration residency: Wagner

  1. MPA elective coursework (3)

Spring, Year 2 [12.5 credits]

Registration residency: Wagner

  1. APSTA-GE 2355 Data Science Translation: Writing, Speaking, and Visualization (3 credits)
  2. APSTA-GE XXXX Elective (2 credits)
  3. MPA elective coursework (3) [PADM-GP 2411 Policy Formation strongly encouraged]
  4. MPA elective coursework (3)
  5. CAP-GP 3402 Capstone: Advanced Projects in Policy or CAP-GP 3149 Capstone: Advanced Research Projects (1.5)

 

Our Faculty

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Rajeev Dehejia

Professor of Economics and Public Service; Associate Dean, Academic Affairs; Director of Policy Specialization

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Tatiana Homonoff

Assistant Professor of Economics and Public Service

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Ingrid Gould Ellen

Paulette Goddard Professor of Urban Policy and Planning, Director for Furman Center for Real Estate and Urban Policy

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ADMISSIONS

Applicants are required to apply to NYU Wagner and to NYU Steinhardt. Once admitted to both, a student will qualify for the dual-degree program. Students apply separately to each school and join the MPA-MS, Applied Statistics dual-degree program once they are admitted to both schools. Students enrolled in dual master’s degree programs with other NYU schools are not eligible for NYU Wagner Named Fellowships. However, applicants will still be considered for one-time, merit-based scholarships. If awarded, the scholarship will only be applied during the student’s period of residency at NYU Wagner.

For more information about admissions to either school, please contact:

 

ALUMNI IN ACTION

Claudia Solis-Roman (MPA-MS 2020)
Senior Data Science Researcher, Microsoft

"Steinhardt and Wagner prepared me to work at the intersection of data science and policy. The professors were excellent, the training was rigorous, and I draw upon what I learned every day. I would recommend this program to anyone interested in working at the cutting edge of statistics and public affairs."