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Curriculum

MS, Applied Statistics for Social Science Research

How to Apply

In this 34-to-44-credit program, you will learn the foundations and machinery underlying advanced statistics and data science techniques, apply these methods to critical applications across the social, behavioral, and health sciences, and translate research findings and their implications to outside audiences. This will prepare you for a career as an applied statistician or data scientist or for doctoral study in a range of fields.

Core Course Sequence

The MS in Applied Statistics for Social Science Research (A3SR) core curriculum features coursework in foundational material in probability, inference, and programming as well as experience in using key tools such as regression modeling, machine learning, causal inference, survey research methods, and multilevel modeling. There is also a focus on understanding when these methods are appropriate and the assumptions that are required for valid inference. You can also explore related topics such as ethics, translation, and measurement. If you have prior advanced course work we offer an accelerated option allowing you to place out of some introductory courses. 

Concentrations

The A3SR program allows you to choose from three different concentrations.

The Computational Methods concentration provides rigorous training in methodological theory, development of methods, algorithms, and designs, and evaluation of the efficacy of those research strategies. It is particularly appropriate if you plan to pursue a PhD in statistics, economics, or computer science.

The Data Science for Social Impact concentration focuses on ethical concerns surrounding data collection and use, collaborations between researchers and practitioners, and challenges involved in succinctly and effectively communicating research findings and their implications. Required classes cover machine learning, the ethics of data science, and translation of data science to non-technical audiences. This concentration will position you to work in a wide variety of careers at the intersection of data and society or a social science doctoral program.

The General Applied Statistics concentration is our most versatile concentration, allowing you to customize the program by selecting from a large set of classes in statistics and related fields. Graduates from this track have pursued careers in industry and research, and doctoral programs that are consistent with their course work and internship experiences.

Culminating Experience

Practical experience in applying statistical and data science strategies to address active empirical research projects in academia or beyond is a cornerstone of the program. We work to ensure that you are prepared for the demands of your new career by providing two culminating experiences that serve as training grounds for this work. 

Consulting 

It is critical that all students have a comprehensive statistical and data science tool kit.  You will complete a statistical consulting seminar that provides a structured approach to understanding which methods are the optimal tool for any given task. This involves discussions of passive and active data collection, cleaning/munging data, designing research studies, fitting complicated models or algorithms, and interpreting results. 

Internship

The best way to understand what skills are needed as an applied statistician or data scientist is to spend time in an organization where those skills are needed. Internships provide a practical learning experience where you will apply the skills learned in your coursework to real world data analysis problems. The internship also provides a chance to understand whether a particular organization or industry is a good fit for you. Students in our program have completed internships with companies in the private sector, governmental and non-governmental organizations, or with research groups within and outside of NYU. Students select their internship based on their personal interests.

Program Requirements

This variable-credit program (33–44 credits) offers an accelerated option for students entering with prior statistical training. 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.

All students must select one of three concentrations: General Applied Statistics, Computational Methods, or Data Science for Social Impact. The concentrations allow students to tailor their studies and focus more specifically on training and preparation for their career or future research. Data Science for Social Impact prepares students to build research–practice partnerships, become knowledgeable of ethical concerns surrounding data, and effectively communicate research findings and their implications. Computational Methods provides more rigorous training in methodological theory and development, and is particularly appropriate for students who wish to progress to PhD programs. General Applied Statistics offers maximal flexibility, allowing students to customize their programs of study by selecting from a broad set of statistics and related courses. Applied statistics electives must be taken, selected from among the topics offered in the program. Finally, a small number of unrestricted electives may be taken from departments across the entire university.

Course Title Credits
Major Requirements
Core Requirements
APSTA-GE 2003Interm Quantitative Methods: General Linear Model 13
or STAT-GB 2301 Regression and Multivariate Data Analysis
APSTA-GE 2004Introductory Statistical Inference in R2
or APSTA-GE 2122 Frequentist Inference
APSTA-GE 2331Data Science for Social Impact3
APSTA-GE 2012Causal Inference3
APSTA-GE 2352Practicum in Applied Statistics: Statistical Computing 21-3
APSTA-GE 2042Multi-Level Modeling: Nested Data/Longitudinal Data2
or APSTA-GE 2040 Multi-Level Modeling Growth Curve
APSTA-GE 2139Survey Research Methods3
or APSTA-GE 2134 Experimental & Quasi Experimental Design
APSTA-GE 2044Generalized Linear Models and Extensions2
APSTA-GE 2351Practicum in Applied Statistics: Applied Probability 13
Electives
Each student must have at least 7 elective credits, 3 of which must be APSTA-GE. Minimum of 9 elective credits for those that take APSTA-GE 2310 for 0 credits.7-9
Program Electives
By adivsement. Students complete 3 credits of APSTA-GE courses.
Unrestricted Electives
6 credits of any NYU graduate level courses, including APSTA-GE electives.
Concentrations
Select one of the following concentrations (8 units minimum):8
General Applied Statistics Concentration:
APSTA-GE courses not taken to fulfill another requirement totaling at least 8 credits
Computational Methods Concentration:
APSTA-GE 2011
Supervised and Unsupervised Machine Learning
APSTA-GE 2122
Frequentist Inference
Select two of the following:
APSTA-GE 2123
Bayesian Inference
APSTA-GE 2013
Missing Data
APSTA-GE 2017
Educational Data Science Practicum
Data Science for Social Impact Concentration:
APSTA-GE 2011
Supervised and Unsupervised Machine Learning
APSTA-GE 2062
Ethics of Data Science
APSTA-GE 2355
Data Science Translation: Writing and Visualization
Culminating Experience
APSTA-GE 2310Internship 30-2
APSTA-GE 2401Statistical Consulting Research Seminar3
Total Credits44
1

Indicates that the student with equivalent prior coursework may place out of this course.

2

Indicates that a student with significant experience may qualify for reduced credit or may place out of this course.

3

If Internship is taken for 0 credits, then the two remaining credits must be made up via electives; may be waived if student has significant professional experience in the field.

Plan of Study Grid
1st Semester/TermCredits
APSTA-GE 2003 Interm Quantitative Methods: General Linear Model 3
APSTA-GE 2331 Data Science for Social Impact 3
APSTA-GE 2351 Practicum in Applied Statistics: Applied Probability 3
APSTA-GE 2352 Practicum in Applied Statistics: Statistical Computing 3
 Credits12
2nd Semester/Term
APSTA-GE 2004 Introductory Statistical Inference in R 2
APSTA-GE 2044 Generalized Linear Models and Extensions 2
Concentration or APSTA elective 3
Concentration or APSTA elective 3
 Credits10
3rd Semester/Term
APSTA-GE 2012 Causal Inference 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
APSTA-GE 2042 Multi-Level Modeling: Nested Data/Longitudinal Data 2
APSTA-GE 2139
or APSTA-GE 2134
 Survey Research Methods
or Experimental & Quasi Experimental Design
 3
 Credits11
4th Semester/Term
APSTA-GE 2310 Internship 2
Concentration or APSTA elective 2
Concentration or APSTA elective 2
Unrestricted Elective 3
Unrestricted Elective 2
 Credits11
 Total Credits44
Plan of Study Grid
1st Semester/TermCredits
APSTA-GE 2003 Interm Quantitative Methods: General Linear Model 3
APSTA-GE 2331 Data Science for Social Impact 3
APSTA-GE 2351 Practicum in Applied Statistics: Applied Probability 3
APSTA-GE 2352 Practicum in Applied Statistics: Statistical Computing 3
 Credits12
2nd Semester/Term
APSTA-GE 2004 Introductory Statistical Inference in R 2
APSTA-GE 2044 Generalized Linear Models and Extensions 2
APSTA-GE 2062 Ethics of Data Science 3
APSTA-GE 2355 Data Science Translation: Writing and Visualization 3
APSTA-GE 2011 Supervised and Unsupervised Machine Learning 2
 Credits12
3rd Semester/Term
APSTA-GE 2012 Causal Inference 3
APSTA-GE 2042 Multi-Level Modeling: Nested Data/Longitudinal Data 2
APSTA-GE 2401 Statistical Consulting Research Seminar 3
APSTA-GE 2139
or APSTA-GE 2134
 Survey Research Methods
or Experimental & Quasi Experimental Design
 3
APSTA elective 2
 Credits13
4th Semester/Term
APSTA-GE 2310 Internship 2
APSTA elective 2
Unrestricted elective 3
 Credits7
 Total Credits44
Plan of Study Grid
1st Semester/TermCredits
APSTA-GE 2003 Interm Quantitative Methods: General Linear Model 3
APSTA-GE 2331 Data Science for Social Impact 3
APSTA-GE 2351 Practicum in Applied Statistics: Applied Probability 3
APSTA-GE 2352 Practicum in Applied Statistics: Statistical Computing 3
 Credits12
2nd Semester/Term
APSTA-GE 2004 Introductory Statistical Inference in R 2
APSTA-GE 2044 Generalized Linear Models and Extensions 2
APSTA-GE 2122 Frequentist Inference 2
APSTA-GE 2011 Supervised and Unsupervised Machine Learning 2
APSTA-GE 2123 Bayesian Inference 2
APSTA-GE 2013 Missing Data 2
 Credits12
3rd Semester/Term
APSTA-GE 2012 Causal Inference 3
APSTA-GE 2042 Multi-Level Modeling: Nested Data/Longitudinal Data 2
APSTA-GE 2139
or APSTA-GE 2134
 Survey Research Methods
or Experimental & Quasi Experimental Design
 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
 Credits11
4th Semester/Term
APSTA-GE 2310 Internship 2
APSTA Elective 2
Unrestricted Elective 3
Unrestricted Elective 2
 Credits9
 Total Credits44
Plan of Study Grid
1st Semester/TermCredits
APSTA-GE 2003 Interm Quantitative Methods: General Linear Model 3
APSTA-GE 2351 Practicum in Applied Statistics: Applied Probability 3
 Credits6
2nd Semester/Term
APSTA-GE 2004 Introductory Statistical Inference in R 2
APSTA-GE 2044 Generalized Linear Models and Extensions 2
Unrestricted Elective 2-3
 Credits6
3rd Semester/Term
Concentration Course 2
Unrestricted Elective 2-3
 Credits4
4th Semester/Term
APSTA-GE 2352 Practicum in Applied Statistics: Statistical Computing 3
APSTA-GE 2331 Data Science for Social Impact 3
 Credits6
5th Semester/Term
A3SR Elective 2
 Credits2
6th Semester/Term
APSTA-GE 2134 Experimental & Quasi Experimental Design 3
APSTA-GE 2042 Multi-Level Modeling: Nested Data/Longitudinal Data 2
Concentration Course 2
 Credits7
7th Semester/Term
Concentration Course 2
 Credits2
8th Semester/Term
APSTA-GE 2012 Causal Inference 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
 Credits6
9th Semester/Term
APSTA-GE 2310 Internship 2
A3SR Elective 1
Concentration Course 2
 Credits5
 Total Credits44

Summer Stats Bootcamp

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