This course equips students with the skills and tools necessary to address applied data science problems with a specific emphasis on urban data. Building on top of the Principles of Urban Informatics (prerequisite for the class) it further introduces a wide variety of more advanced analytic techniques used in urban data science, including advanced regression analysis, time-series analysis, Bayesian inference, foundations of deep learning and network science. The course will also contain a team data analytics project practice. After this class the students should be able to formulate a question relevant to urban data science, find and curate an appropriate data set, identify and apply analytic approaches to answer the question, obtain the answer and interpret it with respect to its certainty level as well as the limitations of the approach and the data.
Visualization and visual analytics systems help people explore and explain data by allowing the creation of both static and interactive visual representations. A basic premise of visualization is that visual information can be processed at a much higher rate than raw numbers and text. Well-designed visualizations substitute perception for cognition, freeing up limited cognitive/memory resources for higher-level problems. This course aims to provide a broad understanding of the principals and designs behind data visualization. General topics include state-of-the-art techniques in both information visualization and scientific visualization, and the design of interactive/web-based visualization systems. Hands on experience will be provided through popular frameworks such as matplotlib, VTK and D3.js.
The course targets current and future urban practitioners looking to harness the power of data in urban practice and research. This course builds the practical skillset and tools necessary to address urban analytics problems with urban data. It starts with essential computational skills, statistical analysis, good practices for data curation and coding, and further introduces a machine learning paradigm and a variety of standard supervised and unsupervised learning tools used in urban data science, including regression analysis, clustering, and classification as well as time series analysis. After this class, you should be able to formulate a question relevant to Urban Data Science, locate and curate an appropriate data set, identify and apply analytic approaches to answer the question, obtain the answer and assess it with respect to its certainty level as well as the limitations of the approach and the data. The course will also contain project-oriented practice in urban data analytics, including relevant soft skills – verbal and written articulation of the problem statement, approach, achievements, limitations, and implications.
The UCSL at CUSP is a series of online sessions designed to build a common skillset and familiarity with techniques, concepts, and models for urban informatics computing. The online sessions focus on data explorations, programming skills and statistical methods needed for scientific computing in the field of Urban Informatics.
A site for IMA NY Students to find equivalent courses outside of IMA NY
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.