Topics and prerequisites vary by semester
Data Science (Undergraduate)
4 credits – 14 Weeks
DS-UA 300-000 (22034)09/03/2024 – 12/12/2024 Tue,Thu3:00 PM – 4:00 PM (Late afternoon)at OnlineInstructed by Sah, Sidharth
DS-UA 300-000 (22053)09/03/2024 – 12/12/2024 Fri11:00 AM – 12:00 AM (Morning)at OnlineInstructed by Atalik, Arda
DS-UA 300-000 (22081)09/03/2024 – 12/12/2024 Fri3:00 PM – 4:00 PM (Late afternoon)at OnlineInstructed by Patil, Gautam
Information visualization is the graphical representation of data to aid understanding, and is the key to analyzing massive amounts of data for fields such as science, engineering, medicine, and the humanities. This is an introductory undergraduate course on Information Visualization based on a modern and cohesive view of the area. Topics include techniques such as visual design principles, layout algorithms, and interactions as well as their applications of representing various types of data such as networks and documents. Overviews and examples from state-of-the-art research will be provided. The course is designed as a first course in information visualization for students both intending to specialize in visualization as well as students who are interested in understanding and applying visualization principles and existing techniques. Fulfillment: CS Electives, Data Science Data Analysis Required; Data Science Courses for Concentration in Artificial Intelligence. Prerequisite or Co-requisite: Data Structures. Students must be CS or DS major and have junior or senior standing.
Data Science (Undergraduate)
4 credits – 15 Weeks
DATS-SHU 235-000 (20423)01/30/2023 – 05/12/2023 Mon,Wed7:00 PM – 8:00 PM (Evening)at ShanghaiInstructed by Gu, Xianbin
Provides data science students with an opportunity to apply the knowledge gained in their course work to practical problems in industry. This course is for majors and minors only.
Data Science (Undergraduate)
2-4 credits – 12 Weeks
DS-UA 204-000 (2660)at Washington SquareInstructed by Skok Gibbs, Claudia
The first wave of data science focused on accuracy and efficiency: on what we can do with data. The second wave is about responsibility: what we should and should not do. Accordingly, this technical course tackles the issues of ethics and responsibility in data science, including legal compliance, data quality, algorithmic fairness and diversity, transparency of data and algorithms, privacy, and data protection. An important feature of this course is its holistic treatment of the data science lifecycle, beginning with data discovery and acquisition, through data cleaning, integration, querying, analysis, and result interpretation.
Data Science (Undergraduate)
4 credits – 15 Weeks
DS-UA 202-000 (9950)01/24/2022 – 05/09/2022 Mon,Wed8:00 AM – 9:00 AM (Morning)at Washington SquareInstructed by Wood, George
DS-UA 202-000 (9951)01/24/2022 – 05/09/2022 Thu8:00 AM – 8:00 AM (Morning)at Washington SquareInstructed by
Causal Inference provides students with the tools for understanding causation, i.e., the relationship between cause and effect. We will start with the situation in which you are able to design and implement the data gathering process, called the experiment. We will then define causation, identify preconditions required for A to cause B, show how to design perfect experiments, and discuss how to understand threats to the validity of less-than-perfect experiments. In this course, we will cover experimental design and then turn to those careful approaches, where we will consider such approaches as quasi-experiments, regression discontinuities, differences in differences, and contemporary advanced approaches.
Data Science (Undergraduate)
4 credits – 15 Weeks
DS-UA 201-000 (20565)01/27/2020 – 05/11/2020 Wed,Fri2:00 PM – 3:00 PM (Early afternoon)at Washington SquareInstructed by Strezhnev, Anton
DS-UA 201-000 (20567)01/27/2020 – 05/11/2020 Mon2:00 PM – 2:00 PM (Early afternoon)at Washington SquareInstructed by
DS-UA 201-000 (20568)01/27/2020 – 05/11/2020 Wed11:00 AM – 11:00 AM (Morning)at Washington SquareInstructed by
This course covers widely-used machine learning methods for language understanding—with a special focus on machine learning methods based on artificial neural networks—and culminates in a substantial final project in which students write an original research paper in AI or computational linguistics. If you take this class, you’ll be exposed only to a fraction of the many approaches that researchers have used to teach language to computers. However, you’ll get training and practice with all the research skills that you’ll need to explore the field further on your own. This includes not only the skills to design and build computational models, but also to design experiments to test those models, to write and present your results, and to read and evaluate results from the scientific literature.
Data Science (Undergraduate)
4 credits – 15 Weeks
DS-UA 203-000 (9643)01/24/2022 – 05/09/2022 Wed2:00 PM – 3:00 PM (Early afternoon)at Washington SquareInstructed by Bowman, Samuel
DS-UA 203-000 (9644)01/24/2022 – 05/09/2022 Tue3:00 PM – 4:00 PM (Late afternoon)at Washington SquareInstructed by
Data Science for Everyone is a foundational course that prepares students to participate in the data-driven world that we are all experiencing. It develops programming skills in Python so that students can write programs to summarize and compare real-world datasets. Building on these data analysis skills, students will learn how draw conclusions and make predictions about the data. Students will also explore related ethical, legal, and privacy issues.
Data Science (Undergraduate)
4 credits – 15 Weeks
DS-UA 111-000 (9775)09/02/2021 – 12/14/2021 Mon,Wed3:00 PM – 4:00 PM (Late afternoon)at Washington SquareInstructed by Jones-Rooy, Andrea · Spirling, Arthur
DS-UA 111-000 (9776)09/02/2021 – 12/14/2021 Thu4:00 PM – 5:00 PM (Late afternoon)at Washington SquareInstructed by López Peceño, Alejandro
DS-UA 111-000 (9778)09/02/2021 – 12/14/2021 Thu5:00 PM – 6:00 PM (Late afternoon)at Washington SquareInstructed by Palmer, Lexi
DS-UA 111-000 (9779)09/02/2021 – 12/14/2021 Fri3:00 PM – 4:00 PM (Late afternoon)at Washington SquareInstructed by López Peceño, Alejandro
DS-UA 111-000 (9780)09/02/2021 – 12/14/2021 Fri8:00 AM – 8:00 AM (Morning)at Washington SquareInstructed by Kottur, Ankita Vijaya Kumar
DS-UA 111-000 (9781)09/02/2021 – 12/14/2021 Fri2:00 PM – 3:00 PM (Early afternoon)at Washington SquareInstructed by Palmer, Lexi
Introduction to Data Science offers the fundamental principles and techniques of data science. Students will develop a toolkit to examine real world examples and cases to place data science techniques in context, to develop data-analytic thinking, and to illustrate that proper application is as much an art as it is a science. In addition, students will gain hands-on experience with the Python programming language and its associated data analysis libraries. Students will also consider ethical implications surrounding privacy, data sharing, and algorithmic decision making for a given data science solution.
Data Science (Undergraduate)
4 credits – 15 Weeks
For most students joining IMA in Fall 2022 and beyond, there is a new program structure that affects the categorization of courses on this site:
Any class in any IMA major elective category (ie "Art & Design") refers to the IMA program structure previous to those entering in Fall 2022. If you are in the class of 2026 (most entering Fall 2022 or later), any course in an IMA elective category are generic IMA electives in the new structure.
Here is a link to the IMA program structure (class of 2026 and beyond):
https://itp.nyu.edu/ima/curriculum/ima-program-structure-class-of-2026-and-beyond/