The Center for Data Science offers a suite of undergraduate courses and programs designed to equip and empower students of all backgrounds and with any level of prior programming & statistics experience to become practitioners of rigorous, thoughtful, and ethical data science.
Data science is the new language of the 21st century and has become a cornerstone of a liberal arts education. Data science skills are also increasingly a requirement for students entering the workforce, government, or research after graduation. As more and more academic disciplines, industries, and media outlets rely on data-driven decision making, research, and evidence, being a sophisticated consumer of data and visualizations, as well as being empowered to analyze and generate discoveries, is naturally becoming a prerequisite for being a global citizen, scientist, and leader.
We are pleased to announce our new innovative and interdisciplinary major and minor in Data Science. Both programs of study are open to all undergraduate students from across the humanities, social sciences, and sciences, and our courses are open to all students. Students who complete the major in data science will be equipped to apply existing data science theories and technologies to problems in many liberal arts domains across the humanities, social sciences, and sciences. They will also acquire a thorough knowledge base in computer science and statistics, as well as a deep understanding of current theories, research methodologies, and tools needed to analyze big data, make inferences, explore data-driven decision making, and engage in ethical and privacy considerations.
We invite you to explore the above menu for more on our major, minor, course offerings, and contact information. Please don’t hesitate to get in touch with any questions about our programs, courses, or anything else.
Major in Data Science
MAJOR IN DATA SCIENCE
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 to make them both ethically and scientifically responsible stewards of data, as well as, of course, rigorous scientists.
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 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 minor in another liberal arts discipline (other than computer science).
The undergraduate major in data science requires the completion of 13 four-point courses (48 credits) and one liberal arts minor. Please see the College of Arts & Sciences Bulletin for the full major requirements, and don’t hesitate to get in touch with any questions.
Minor in Data Science
MINOR IN DATA SCIENCE
The minor in data science allows students to learn foundational computational analysis concepts and use data science methods and tools to answer important questions. Students will be able to apply those concepts to a range of domain-specific issues that relate to their major course of study.
Students completing the minor will gain a robust introduction to data science principles, while specializing in other liberal arts fields. They will have opportunities for hands-on experience working with real world datasets, and they will cultivate knowledge about the ethical issues related to data science.
Additionally, thanks to a grant from the NYU Curricular Development Challenge Fund, students will experience locally-inflected data science that explores contemporary urban and social issues relating to our home city of New York.
The minor consists of five four-point courses (20 credits). It is suitable for students in all disciplines, and gives students from all backgrounds an array of core data science skills, methodologies, and philosophies.
Please see the College of Arts & Sciences Bulletin on the minor for a comprehensive listing of requirements for a minor in data science.
We are proud to offer a range of courses that allow any student, regardless of prior experience, to learn the language and philosophies of data science. Our flagship course, Data Science for Everyone, is offered every semester, and is designed especially for students with no prior programming or statistics experience.
Data Science for Everyone and Introduction to Data Science will be offered in the fall 2019 and spring 2020 semesters. Causal Inference and Responsible Data Science will be offered in the spring 2020 semester. Please read below for more on our courses, and see the College of Arts & Science Bulletin for further details.
Data Science for Everyone (DS-UA 111) is a course that will change your life. It will empower you to understand and use data in a principled way to better explain, make decisions in, and predict outcomes in the world. Students will learn to conduct hands-on research in Python using real-world datasets as instruments to practice and apply principles of scientific thinking and causal inference. This course is open to all students regardless of prior programming or statistics experience. Learn more about the course in this video.
- This course will be co-taught by Prof. Arthur Spirling, who was recently awarded the NYU Golden Dozen Award for teaching excellence, and Prof. Andrea Jones-Rooy, Director of Undergraduate Studies, in the fall 2019 semester. See the spring 2019 syllabus here. Prerequisites: High school algebra.
Students who take Introduction to Data Science (DS-UA-112) will explore the theoretical issues, methods, tools, and problems that relate to data-rich issues in the humanities, social sciences, and sciences. Students will learn the core concepts of inference and computing while working with real data.
- This course will be taught by Prof. Christopher Policastro in the fall 2019 semester. See the tentative syllabus here. Prerequisites: Data Science for Everyone (DS-UA-111), or equivalent programming experience in Python.
We often want to know the relationship between cause and effect. In Causal Inference (DS-UA-201), students will learn to design and conduct experiments, define causation in the context of various liberal arts disciplines, and explore underlying theories, identify preconditions, and understand threats of validity to less-than-robust experiments. By the end of this course, students will be equipped to think about, interpret, and test for possible causal relationships between variables of interest.
- This course will be taught by Prof. Anton Strezhnev in the spring 2020 semester. See the tentative syllabus here. Prerequisites: Introduction to Data Science (DS-UA-112) or an equivalent course in probability.
The first wave of data science focused on accuracy and efficiency: what can we do with data? The second wave is about responsibility: what should we do and not do? Responsible Data Science (DS-UA-202) tackles issues of ethics and responsibility in data science, including legal compliance, data quality, diversity and algorithmic fairness, data and algorithm transparency, privacy, and data protection and security.
- This course will be taught in the spring 2020 semester. See the tentative syllabus here. Prerequisites: Introduction to Data Science (DS-UA-112) and Probability & Statistics (MATH-UA-235).
Are you a current student with a question about enrolling in one of our courses, or about the major or minor in data science? A prospective student wondering if this is the right program for you? Or perhaps a curious parent, or an interested third party? Regardless of who you are, we want to hear from you!