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Total Points Required: 34 minimum; 34–44 variable

This is a variable-credit program in which you can take a minimum of 34 credits. If you are entering the program with prior statistical training, the accelerated, lower credit option may be ideal for you. Most students with no prior statistical experience will take around 44 credits.  

The program consists of theoretical foundations, statistical inference and generalized linear models, causal inference, survey research methods, multilevel modeling, applied statistics electives, and unrestricted electives. A statistical consulting research seminar and internship provide practical learning experiences.

Required Courses: 23 credits

Core Courses  
APSTA-GE 2003: Intermediate Quantitative Methods*
or
STAT-GB 2301: Regression and Multivariate Data Analysis
3
APSTA-GE 2004: Advanced Modeling I: Topics in Multivariate Analysis 2
APSTA-GE 2331: Data Science for Social Impact 3
APSTA-GE 2012: Causal Inference 3
APSTA-GE 2352: Practicum in Statistical Computing & Simulation* 2
APSTA-GE 2042: Multilevel Models: Nested Data
or 
APSTA-GE 2040: Multilevel Models: Growth Curves
2
APSTA-GE 2139: Survey Research I
or 
APSTA-GE 2134: Experimental Design
3
APSTA-GE 2044: Generalized Linear Models 2
APSTA-GE 2351: Applied Probability* 3
*Indicates that the student with equivalent prior course work may place out of this course.  

Concentrations: 8 credits (choose one)

The Data Science for Social Impact concentration provides intense training on the breadth of skills required today to work effectively with the government, non-for-profits, evaluation firms, and other organizations that use data to impact society. This concentration is one of the first to focus on ethical concerns surrounding data collection and use as well as challenges involved in succinctly and effectively communicating findings and their implications. Students in this concentration would be well positioned to work in a wide variety of careers at the intersection of data and society or a social science doctoral program.

The Computational Methods concentration provides more rigorous training in methodological theory and development, particularly appropriate for students who will progress to PhD programs in statistics, economics, or computer science.

The General Applied Statistics concentration offers maximal flexibility, allowing the student to customize their program by selecting from a large set of statistics and related classes. Students from this track have pursued careers in industry, research, and doctoral programs that are consistent with their course work and internship experiences.

General Applied Statistics
Three APSTA-GE courses totaling at least 8 credits not taken to fulfill another requirement.  
Computational Methods
APSTA-GE 2011: Introduction to Machine Learning 2
APSTA-GE 2122: Statistical Inference (Frequentist) 2
AND two of the following three courses:  
APSTA-GE 2123: Statistical Inference (Bayesian) 2
APSTA-GE 2013: Missing Data 2
APSTA-GE 2017: Education Data Science Practicum 2
Data Science for Social Impact
APSTA-GE 2011: Introduction to Machine Learning 2
APSTA-GE 2062: Ethics of Data Science 3
APSTA-GE 2355 Data Science Translation 3

Culminating Experience: 3-5 credits

Culminating Experience
APSTA-GE 2310: Internship+ 0-2
APSTA-GE 2401: Statistical Consulting Seminar 3
+Students are advised that internships must be approved by a program advisor and will be assessed in terms of content and rigor. Most students can take this course for 0 credits and take 2 credits of unrestricted electives instead. Those with three years or more of professional experience may waive it completely.  

Electives: 8-10 credits

Applied Statistics Electives  
4–10 credits of APSTA-GE courses; not taken to meet any other requirement**  
Unrestricted Electives
0–6 credits of unrestricted electives; may be taken from departments across the entire university according to your own interests, with advisor approval. We recommend EDPLY-GE Education and Social Policy.  
**Note: total for both electives (in program and unrestricted) must be at least 8.  

 

Approved NYU Electives
 
Courant Institute of Mathematical Sciences, School of Medicine, Faculty of Arts and Science, or Tandon School of Engineering:

MA-GY.7763: Topics in Statistics: Data Mining and Machine Learning

MATH-GA 2840: Advanced Topics in Applied Mathematics – Dynamic Computational Statistical Models for Socio-Economic and Geo-Political Systems
CSCI-GA-256: Machine Learning and Pattern Recognition
DS-GA 1002: Statistical and Mathematical Methods
DS-GA 1003: Machine Learning 
DS-GA 1004: Big Data
EHSC-GA 2339: Introduction to Bayesian Modeling 
For more courses, visit the Data Science curriculum website. Please note some of the courses offered through Data Science may have substantial prerequisites in mathematics and computer science; further, open seats in classes offered in that program are limited. 
Department of Psychology, Faculty of Arts and Science:
PSYCH-GA 2243: Psychometric Theory
PSYCH-GA 2247: Structural Equation Methods
PSYCH-GA 2248: Methods for the Analysis of Change
Department of Sociology, Faculty of Arts and Science:
SOC-GA 2314: Longitudinal Statistics
SOC-GA 2306: Event History Analysis
Department of Economics, Stern School of Business:
ECON-GB 3351: Econometrics I
ECON-GB 9912: Econometric Analysis of Panel Data
Stern School of Business:
STAT-GB 2302: Forecasting Time Series Data
STAT-GB 2308: Applied Stochastic Processes For Financial Models
STAT-GB 4310: Statistics for Social Data
INFO-GB 3335: Data Mining for Business Analytics

For all Stern courses: view the cross-registration process and form required. 

For all Wagner courses (PADM-GP): submit a course registration request.