Courses
Courses
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. Prerequisite: None.
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. Pascal Wallisch in the Fall 2020 semester. See the tentative syllabus here. Prerequisite: 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 syllabus here. Prerequisite: Data Science for Everyone (DS-UA 111).
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. Prerequisite: Introduction to Data Science (DS-UA-112).