The undergraduate major in data science is designed to develop students’ broad knowledge in emerging theories and methods of computational statistics in academic fields within the humanities, social sciences, and sciences.
Students who complete the major in data science will be exposed to diverse ways of knowing, research and critical thinking skills, and communication and inference techniques. This exposure will equip them to be both ethically and scientifically responsible stewards of data, as well as rigorous scientists.
Knowing what questions to ask, how they connect to deep issues in a particular field, and how to judge the reliability of your results is just as important as (if not more than) knowing how to execute your statistical tests.
While students will necessarily gain skills in programming due to the computational nature of the field, this major is not centered on professional or vocational training. Instead, the skill-building in computational applications and database management that takes place within our data science courses and curriculum unfolds within a broader context of scientific and theoretical frameworks. These frameworks are essential for understanding and pursuing deeper objectives, novel knowledge generation, and robust discovery.
To develop an even stronger link between data science and the liberal arts tradition, all students who major in data science must also have a second major or a CAS minor. Information regarding minors that will fulfill this requirement can be found on the NYU College of Arts & Sciences Majors and Minors page.
Please note: Due to substantial overlap, data science majors are not allowed to pursue the computer science minor. The undergraduate major in data science requires the completion of 13 four-point courses (52 credits) and one liberal arts minor. Please see the NYU Bulletin for the full major requirements, and don’t hesitate to get in touch with any questions.