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Curriculum

MS, Applied Statistics for Social Science Research

How to Apply
MS, Applied Statistics for Social Science Research

In this 34-to-43-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, cleaning messy data, and databases. 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 four 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 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 Measurement, Methodology, and Design concentration prepares students with the skills to ensure data are meaningful, collected appropriately, and analyzed in a way that yields valid, reliable, and actionable insights. Students in this concentration study survey research methods, experimental and quasi-experimental design, and measurement.

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 a culminating experience that serves as a training ground for this work. 

Consulting 

The aim of the statistical consulting seminar is to reinforce key components of the A3SR curriculum and prepare students for work as applied statisticians. Over the course of the semester, you will complete three exams, each covering different aspects of applied statistics. The first exam focuses on regression modeling, statistical inference, probability, and randomized experiments. The second exam emphasizes statistical computing and R programming. The third and final exam covers general topics in applied statistics. Class time is devoted to discussing strategies for identifying the most appropriate tools for a given problem and achieving mastery in applied statistical methods.

Program Requirements

This variable-credit program (34-43 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, messy data and machine learning, multilevel modeling, applied statistics electives, and unrestricted electives. A statistical consulting research seminar and options for internships provide practical learning experiences.

All students must select one of four concentrations: General Applied Statistics, Computational Methods, Data Science for Social Impact, or Measurement, Methodology, and Design. 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 Ph.D. 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. Measurement, Methodology, and Design allows students to explore the principles and practical applications of creating and evaluating effective research. 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
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 13
APSTA-GE 2044Generalized Linear Models and Extensions2
APSTA-GE 2017Databases and Data Science Practicum2
APSTA-GE 2047Messy Data and Machine Learning3
APSTA-GE 2351Practicum in Applied Statistics: Applied Probability 13
Electives
Each student must have at least 10 elective credits, 4 of which must be APSTA-GE. 10
Program Electives
By advisement. Students complete 4 credits of APSTA-GE courses. Electives may include APSTA-GE 2310 Internship for 1-2 credits.
Concentrations
Select one of the following concentrations (6 units minimum):6
General Applied Statistics Concentration:
By advisement. Students complete 6 credits of APSTA-GE coursework.
Computational Methods Concentration:
APSTA-GE 2123
Bayesian Inference
Choose 4 credits from the following:
APSTA-GE 2013
Missing Data
APSTA-GE 2011
Supervised and Unsupervised Machine Learning
APSTA-GE 2040
Multi-Level Modeling Growth Curve
or APSTA-GE 2042
 Multi-Level Modeling: Nested Data/Longitudinal Data
Data Science for Social Impact Concentration:
APSTA-GE 2062
Ethics of Data Science
APSTA-GE 2355
Data Science Translation: Writing and Visualization
Measurement, Methodology, and Design Concentration:
Select two of the following:
APSTA-GE 2139
Survey Research Methods
APSTA-GE 2094
Modern Approaches in Measurement
APSTA-GE 2134
Experimental & Quasi Experimental Design
Unrestricted Electives
6 credits of any NYU graduate level courses, including APSTA-GE electives.
Culminating Experience
APSTA-GE 2401Statistical Consulting Research Seminar3
Total Credits43
1

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

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 2017 Databases and Data Science Practicum 2
Concentration or APSTA elective 3
 Credits9
3rd Semester/Term
APSTA-GE 2047 Messy Data and Machine Learning 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
Concentration or APSTA elective 3
Concentration or APSTA elective 3
 Credits12
4th Semester/Term
APSTA-GE 2012 Causal Inference 3
Concentration or APSTA elective 2
Unrestricted Elective 3
Unrestricted Elective 2
 Credits10
 Total Credits43
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 2017 Databases and Data Science Practicum 2
APSTA-GE 2062 Ethics of Data Science 3
 Credits9
3rd Semester/Term
APSTA-GE 2047 Messy Data and Machine Learning 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
APSTA or unrestricted elective 2
APSTA or unrestricted elective 3
 Credits11
4th Semester/Term
APSTA-GE 2012 Causal Inference 3
APSTA-GE 2355 Data Science Translation: Writing and Visualization 3
APSTA or unrestricted elective 2
APSTA or unrestricted elective 3
 Credits11
 Total Credits43
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 2017 Databases and Data Science Practicum 2
APSTA-GE 2123 Bayesian Inference 2
APSTA or unrestricted elective 2
 Credits10
3rd Semester/Term
APSTA-GE 2047 Messy Data and Machine Learning 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
APSTA-GE 2042 Multi-Level Modeling: Nested Data/Longitudinal Data 2
APSTA or unrestricted elective 3
 Credits11
4th Semester/Term
APSTA-GE 2012 Causal Inference 3
APSTA-GE 2013 Missing Data 2
APSTA or unrestricted elective 2
APSTA or unrestricted elective 3
 Credits10
 Total Credits43

Sample Plan of Study

Full-Time General Applied Statistics

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 2017 Databases and Data Science Practicum 2
Concentration or APSTA elective 3
 Credits9
3rd Semester/Term
APSTA-GE 2047 Messy Data and Machine Learning 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
Concentration or APSTA elective 3
Concentration or APSTA elective 3
 Credits12
4th Semester/Term
APSTA-GE 2012 Causal Inference 3
Concentration or APSTA elective 2
Unrestricted Elective 3
Unrestricted Elective 2
 Credits10
 Total Credits43

Full-Time Data Science for Social Impact

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 2017 Databases and Data Science Practicum 2
APSTA-GE 2062 Ethics of Data Science 3
 Credits9
3rd Semester/Term
APSTA-GE 2047 Messy Data and Machine Learning 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
APSTA or unrestricted elective 2
APSTA or unrestricted elective 3
 Credits11
4th Semester/Term
APSTA-GE 2012 Causal Inference 3
APSTA-GE 2355 Data Science Translation: Writing and Visualization 3
APSTA or unrestricted elective 2
APSTA or unrestricted elective 3
 Credits11
 Total Credits43

Full-Time Computational Methods

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 2017 Databases and Data Science Practicum 2
APSTA-GE 2123 Bayesian Inference 2
APSTA or unrestricted elective 2
 Credits10
3rd Semester/Term
APSTA-GE 2047 Messy Data and Machine Learning 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
APSTA-GE 2042 Multi-Level Modeling: Nested Data/Longitudinal Data 2
APSTA or unrestricted elective 3
 Credits11
4th Semester/Term
APSTA-GE 2012 Causal Inference 3
APSTA-GE 2013 Missing Data 2
APSTA or unrestricted elective 2
APSTA or unrestricted elective 3
 Credits10
 Total Credits43

Full-Time Measurement, Methodology, and Design

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
APSTA-GE 2352 Practicum in Applied Statistics: Statistical Computing 3
APSTA-GE 2331 Data Science for Social Impact 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 2017 Databases and Data Science Practicum 2
APSTA-GE 2094 Modern Approaches in Measurement 3
 Credits9
3rd Semester/Term
APSTA-GE 2047 Messy Data and Machine Learning 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
APSTA or unrestricted elective 3
APSTA or unrestricted elective 2
 Credits11
4th Semester/Term
APSTA-GE 2012 Causal Inference 3
APSTA-GE 2134 Experimental & Quasi Experimental Design 3
APSTA or unrestricted elective 3
APSTA or unrestricted elective 2
 Credits11
 Total Credits43

Part-Time Sample Plan

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
APSTA or unrestricted Elective 2
 Credits6
3rd Semester/Term
APSTA or unrestricted elective 3
 Credits3
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
APSTA-GE 2017 Databases and Data Science Practicum 2
Concentration course 3
A3SR or unrestricted elective 3
 Credits8
6th Semester/Term
APSTA or unrestricted elective 2
 Credits2
7th Semester/Term
APSTA-GE 2047 Messy Data and Machine Learning 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
 Credits6
8th Semester/Term
APSTA-GE 2012 Causal Inference 3
Concentration course 3
 Credits6
 Total Credits43
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
APSTA or unrestricted Elective 2
 Credits6
3rd Semester/Term
APSTA or unrestricted elective 3
 Credits3
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
APSTA-GE 2017 Databases and Data Science Practicum 2
Concentration course 3
A3SR or unrestricted elective 3
 Credits8
6th Semester/Term
APSTA or unrestricted elective 2
 Credits2
7th Semester/Term
APSTA-GE 2047 Messy Data and Machine Learning 3
APSTA-GE 2401 Statistical Consulting Research Seminar 3
 Credits6
8th Semester/Term
APSTA-GE 2012 Causal Inference 3
Concentration course 3
 Credits6
 Total Credits43

Summer Stats Bootcamp

This August, ASH will be offering a math, statistics, and programming bootcamp which will run as a two-week long 0 credit course for NYU graduate students.

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