Duration: 15 Weeks
Dates: Tue
Interactive Telecommunications (Graduate)
4 credits – 15 Weeks
Courses that count in IMA’s Programming and Data category
Interactive Telecommunications (Graduate)
4 credits – 15 Weeks
On October 8th, 2015, a team in Ecuador identified 431 species of birds – the world record for number counted in a single day. Earlier that year in Myanmar, a scientist counted one Jerdon’s babbler, the first in nearly eight decades. In December of 2019, eBird announced that its database held over 737 million bird observations. This morning, in Brooklyn Bridge park, I counted 38 house sparrows, 4 black-and-white warblers and an ovenbird. This course will consider birding as a practice, and will dive deep into the processes by which observations become data. As a collective, we will investigate how crowd-sourced data is transforming ornithology, and will explore ways to tell stories about the natural world through visualization and more radical forms of data representation.
Interactive Telecommunications (Graduate)
2 credits – 7 Weeks
ITPG-GT 2085-000 (22309)03/23/2023 – 05/04/2023 Thu9:00 AM – 12:00 AM (Morning)at Brooklyn CampusInstructed by Thorp, Jeremy
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
Data structures are fundamental programming constructs which organize information in computer memory to solve challenging real-world problems. Data structures such as stacks, queues, linked lists, and binary trees, therefore constitute building blocks that can be reused, extended, and combined in order to make powerful programs. This course teaches how to implement them in a high-level language, how to analyze their effect on algorithm efficiency, and how to modify them to write computer programs that solve complex problems in a most efficient way. Programming assignments. Prerequisite: ICS or A- in ICP. Equivalency: This course counts for CSCI-UA 102 Data Structures (NY). Fulfillment: CS Required, Data Science Required, CE Required.
Computer Science (Undergraduate)
4 credits – 15 Weeks
CSCI-SHU 210-000 (20398)01/30/2023 – 05/12/2023 Tue3:00 PM – 5:00 PM (Late afternoon)at ShanghaiInstructed by Tam, Yik-Cheung
CSCI-SHU 210-000 (20399)01/30/2023 – 05/12/2023 Thu3:00 PM – 5:00 PM (Late afternoon)at ShanghaiInstructed by Simikin, Sven
CSCI-SHU 210-000 (20400)01/30/2023 – 05/12/2023 Wed3:00 PM – 5:00 PM (Late afternoon)at ShanghaiInstructed by Simikin, Sven
CSCI-SHU 210-000 (20401)01/30/2023 – 05/12/2023 Mon11:00 AM – 1:00 PM (Morning)at ShanghaiInstructed by Tam, Yik-Cheung
CSCI-SHU 210-000 (20402)01/30/2023 – 05/12/2023 Wed11:00 AM – 1:00 PM (Morning)at ShanghaiInstructed by Simikin, Sven
CSCI-SHU 210-000 (20403)01/30/2023 – 05/12/2023 Fri11:00 AM – 1:00 PM (Morning)at ShanghaiInstructed by Simikin, Sven
Core: Data and Discovery (Undergraduate)
4 credits – 15 Weeks
Music Technology (Undergraduate)
4 credits – 15 Weeks
Exponential growth in the availability of high quality real estate and real estate-related data is fueling a major shift in development, investment, and lending decision-making processes. In this highly applied course, students will be introduced to major data analysis and machine learning platforms; a wide range of public and private real estate and urban data sources; approaches to exploratory data analysis, real estate data visualization, and communication of findings; applied statistical modeling, including forecast modeling; and, emerging and prospective real estate applications for artificial intelligence and machine learning. Assessment will include case work focusing on real-world real estate decisions and coding assignments. While the data and applications for this course are principally in the real estate sector, the applied skills learned may be of interest for students across a wide range of industries.
Finance (Undergraduate)
3 credits – 12 Weeks
The financial services industry is being transformed by regulation, competition, consolidation, technology and globalization. These forces will be explored, focusing on how technology is both a driver of change as well as the vehicle for their implementation. The course focuses on payment products and financial markets, their key systems, how they evolved and where might they be going, algorithmic trading, market structure dark, liquidity and electronic markets. Straight through processing, risk management and industry consolidation and convergence will be viewed in light of current events. The course objective is to bring both the business practitioner and technologist closer together. Topics will be covered through a combination of lectures, readings, news, case studies and projects.
Computing and Data Science (Undergraduate)
3 credits – 12 Weeks
TECH-UB 50-000 (21263)09/23/2020 – 12/16/2020 Wed6:00 PM – 9:00 PM (Evening)at Washington SquareInstructed by Donefer, Bernard
Computing and Data Science (Undergraduate)
4 credits – 14 Weeks
Music Technology (Undergraduate)
3 credits – 15 Weeks
This course is the follow-on course to Introduction to Programming and Data Science, which is offered in the Fall. It is recommended for undergraduate students who 1) are interested in jobs in the rapidly growing fields of data science and data analytics or 2) who are interested in acquiring the technical and data analysis skills that are becoming increasingly relevant in all disciplines. Intro to Programming and Data Science forms the basis for this course, but it is not a pre-requisite. Students with basic knowledge of programming in Python and SQL are welcome to join. This course covers select topics that build on the prior course work and is largely project based. Much of the course will be project-based work, with students working on projects that utilize the skills used in this and the prior Programming and Data Science course.
Computing and Data Science (Undergraduate)
3 credits – 15 Weeks
TECH-UB 24-000 (19343)at Washington SquareInstructed by
TECH-UB 24-000 (19344)at Washington SquareInstructed by
This course examines the major trends in digital marketing using tools from business analytics and data science. While there will be sufficient attention given to top level strategy used by companies adopting digital marketing, the focus of the course is also on business analytics: how to make firms more intelligent in how they conduct business in the digital age. Measurement plays a big role in this space. The course is complemented by cutting-edge projects and various business consulting assignments that the Professor has been involved in with various companies over the last few years. Prof Ghose has consulted in various capacities for Apple, AMD, Berkeley Corporation, Bank of Khartoum, CBS, Dataxu, Facebook, Intel, NBC Universal, Samsung, Showtime, 3TI China, and collaborated with Alibaba, China Mobile, Google, IBM, Indiegogo, Microsoft, Recobell, Travelocity and many other leading Fortune 500 firms on realizing business value from IT investments, internet marketing, business analytics, mobile marketing, digital analytics and other topics.We will learn about statistical issues in data analyses such as selection problem, omitted variables problem, endogeneity, and simultaneity problems, autocorrelation, multi-collinearity, assessing the predictive power of a regression and interpreting various numbers from the output of a statistical package, various econometrics-based tools such as simple and multivariate regressions, linear and non-linear probability models (Logit and Probit), estimating discrete and continuous dependent variables, count data models (Poisson and Negative Binomial), cross-sectional models vs. panel data models (Fixed Effects and Random Effects), and various experimental techniques that help can tease out correlation from causality such as randomized field experiments.
Computing and Data Science (Undergraduate)
3 credits – 15 Weeks
TECH-UB 38-000 (19338)01/23/2023 – 05/08/2023 Thu6:00 PM – 9:00 PM (Evening)at Washington SquareInstructed by
This course is recommended for undergraduate students without programming experience who are interested in building capabilities in the rapidly growing fields of data science and data analytics. This hands-on coding course does not have any prerequisites and is meant to help students acquire programming and data analysis skills that are becoming increasingly relevant for entrepreneurial, corporate, and research jobs. The course offers an introduction to programming (using Python) and databases (using SQL). We will cover topics related to collection, storage, organization, management, and analysis of data. There is a strong focus on live coding in the classroom, with discussion of examples.
Computing and Data Science (Undergraduate)
3 credits – 15 Weeks
TECH-UB 23-000 (19340)at Washington SquareInstructed by
TECH-UB 23-000 (19342)at Washington SquareInstructed by
TECH-UB 23-000 (19345)at Washington SquareInstructed by
The study of modern cryptography investigates mathematical techniques for securing information, systems and distributed computations against adversarial attacks. We introduce fundamental concepts of this study. Emphasis will be placed on rigorous proofs of security based on precise definitions and assumptions. Topics include: one-way functions, encryption, signatures, pseudorandom number generators and zero-knowledge proofs. Prerequisite: Algorithms, theory of probability, or permission of the instructor. Fulfillment: Mathematics Additional Electives; Honors Mathematics Electives; CS Electives.
Computer Science (Undergraduate)
4 credits – 15 Weeks
CSCI-SHU 378-000 (18506)09/05/2022 – 12/16/2022 Mon,Tue3:00 PM – 4:00 PM (Late afternoon)at ShanghaiInstructed by Guo, Siyao
The purpose of the Senior Project is for the students to apply the theoretical knowledge they acquired during the Computer Science program to a concrete project in a realistic setting. During the semester, students engage in the entire process of solving a real-world computer science project. It requires students to pursue a long-term, mentored learning experience that culminates in a piece of original work. At the end of the semester, the proposed work comes to fruition in the form of a working software prototype, a written technical report, and an oral presentation at a capstone project symposium. Prerequisite: senior standing. Fulfillment: CS Required.
Computer Science (Undergraduate)
4 credits – 14 Weeks
An introduction to the fundamentals of computer programming. Students design, write, and debug computer programs. No prior knowledge of programming is assumed. Students will learn programming using Python, a general purpose, cross-platform programming language with a clear, readable syntax. Most class periods will be part lecture, part lab as you explore ideas and put them into practice. This course is suitable for students not intending in majoring in computer science as well as for students intending to major in computer science but having no programming experience. Students with previous programming experience should instead take Introduction to Computer Science. Prerequisite: Either placed into Calculus or at least a C in Pre-Calculus Fulfillment: Core Curriculum Requirement Algorithmic Thinking; EE Required Major Courses. Note: Students who have taken ICS in NY, Abu Dhabi, and Shanghai cannot take ICP.
Computer Science (Undergraduate)
4 credits – 14 Weeks
CSCI-SHU 11-000 (17503)02/07/2022 – 05/13/2022 Mon8:00 AM – 10:00 AM (Morning)at ShanghaiInstructed by Simon, Daniel
CSCI-SHU 11-000 (17504)02/07/2022 – 05/13/2022 Tue3:00 PM – 5:00 PM (Late afternoon)at ShanghaiInstructed by Simon, Daniel
CSCI-SHU 11-000 (23632)02/07/2022 – 05/13/2022 Wed8:00 AM – 10:00 AM (Morning)at ShanghaiInstructed by Simon, Daniel
CSCI-SHU 11-000 (23633)02/07/2022 – 05/13/2022 Wed8:00 AM – 10:00 AM (Morning)at ShanghaiInstructed by Liu, Yijian
CSCI-SHU 11-000 (23634)02/07/2022 – 05/13/2022 Thu3:00 PM – 5:00 PM (Late afternoon)at ShanghaiInstructed by Simon, Daniel
CSCI-SHU 11-000 (23767)02/07/2022 – 05/13/2022 Thu3:00 PM – 5:00 PM (Late afternoon)at ShanghaiInstructed by Liu, Yijian
CSCI-SHU 11-000 (26252)02/07/2022 – 05/13/2022 Tue9:00 PM – 10:00 PM (Evening)at ShanghaiInstructed by Spathis, Promethee
CSCI-SHU 11-000 (26253)02/07/2022 – 05/13/2022 Thu9:00 PM – 10:00 PM (Evening)at ShanghaiInstructed by Spathis, Promethee
This course has three goals. First, the mastering of a modern object-oriented programming language, enough to allow students to tackle real-world problems of important significance. Second, gaining an appreciation of computational thinking, a process that provides the foundations for solving real-world problems. Finally, providing an overview of the very diverse and exciting field of computer science – a field which, arguably more than any other, impacts how we work, live, and play today. Prerequisite: Introduction to Computer Programming or placement exam. Equivalency: This course counts for CSCI-UA 101. Fulfillment: Core Curriculum Requirement Algorithmic Thinking; Computer Science Major Required Courses; Computer Systems Engineering Major Required Courses; Data Science Major Foundational Courses; Electrical and Systems Engineering Major Required Major Courses.
Computer Science (Undergraduate)
4 credits – 14 Weeks
CSCI-SHU 101-000 (17449)02/07/2022 – 05/13/2022 Tue8:00 AM – 10:00 AM (Morning)at ShanghaiInstructed by Gu, Xianbin
CSCI-SHU 101-000 (17509)02/07/2022 – 05/13/2022 Thu8:00 AM – 10:00 AM (Morning)at ShanghaiInstructed by Yin, Wen
CSCI-SHU 101-000 (17572)02/07/2022 – 05/13/2022 Thu8:00 PM – 9:00 PM (Evening)at ShanghaiInstructed by Yin, Wen
CSCI-SHU 101-000 (17596)02/07/2022 – 05/13/2022 Wed9:00 AM – 11:00 AM (Morning)at ShanghaiInstructed by Gu, Xianbin
CSCI-SHU 101-000 (17751)02/07/2022 – 05/13/2022 Fri9:00 AM – 11:00 AM (Morning)at ShanghaiInstructed by Yin, Wen
The field of natural language processing (NLP), also known as computational linguistics, is interested in the modeling and processing of human (i.e., natural) languages. This course covers foundational NLP concepts and ideas, such as finite state methods, n-gram modeling, hidden Markov models, part-of-speech tagging, context free grammars, syntactic parsing and semantic representations. The course surveys a range of NLP applications such as information retrieval, summarization and machine translation. Concepts taught in class are reinforced in practice by hands-on assignments.
Computer Science (Undergraduate)
4 credits – 7 Weeks
CS-UH 2216-000 (9051)08/26/2024 – 10/11/2024 Mon,Wed2:00 PM – 3:00 PM (Early afternoon)at Abu DhabiInstructed by Habash, Nizar
Computer Science (Undergraduate)
4 credits – 15 Weeks
Computer Science (Undergraduate)
4 credits – 15 Weeks