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.
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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