Courses

The two-year full-time MSc program requires the completion of 36 credit hours as outlined below.

Structure

Requirements	Course Details
1 required course 0 credits	Data Science for Everyone Bootcamp
5 required courses 15 credits	Probability and Statistics; Machine Learning; Introduction to Data Science; Introduction to Artificial Intelligence; Ethics and Legal Considerations for Data Science
5 elective courses 15 credits	Advanced electives
2 Master's Thesis 6 credits	Research project at NYUAD or in collaboration with industry partners
2 Winter School courses 0 credits	Two required courses
4 Data Science and AI Seminar 0 credits	Four required courses

Specializations

Since Interdisciplinary Data Science and Artificial Intelligence are vast fields, students will have substantial flexibility in selecting electives that align with their academic and professional interests. In consultation with their faculty mentor and the Graduate Program Head, each student will develop a personalized course plan that leads to an informal focus in a specific area, culminating in a capstone research thesis.

Below are examples of potential focus areas, based on course offerings and faculty expertise:

Health Informatics: Applications of machine learning and data science in healthcare and biomedical research
Urban Mobility and Logistics: Data-driven approaches to urban transport, supply chains, and traffic operations
(Arabic) Natural Language Processing: AI models for language processing, with a focus on Arabic and multilingual contexts
Human-Machine Interaction: Designing AI-driven systems for enhanced user experience and interaction
Computational Social Science: AI-driven analysis of human behavior, policy, and social dynamics
Mathematical Foundations and AI Theory: Core algorithmic foundations, optimization, and theoretical advancements in AI
Data Science and Economics: AI applications in financial modeling, economic decision-making, and quantitative analysis

Course Descriptions

Required

Credits: 0

Occurring the summer before students begin coursework, this 0-credit course (which will be offered primarily online) is designed to ensure that students all meet the baseline requirements of programming and quantitative reasoning. It will run for approximately two weeks and will be coordinated with other MSc or PhD program bootcamps as applicable. During this Bootcamp, students will gain a strong foundation in programming in Python, in basic concepts of statistics and probability, in calculus and linear algebra, and in regression analysis. It will be taught in modules, utilizing both synchronous and asynchronous methodologies, by faculty with expertise in teaching each of these concepts. Note that the Bootcamp is included as part of the normal tuition for the program and is required for all admitted students. This course may also be taught jointly with New York, which offers a fully online version of it.
Credits: 0

This J-Term program, held in Abu Dhabi, is a key feature of the program. This program will be held jointly for both active cohorts of students (first-years and second-years). First-year students will participate in short courses taught by faculty, while second-year students will participate in small-group research workshops with visiting faculty organized by theme (with first-year students in the audience). Both cohorts will mix for a Hackathon that concludes the Winter School, where teams of mixed first-year and second-year students led by a visiting or NYUAD faculty member produce a pilot study that can lead to an interdisciplinary research collaboration.
Credits: 3

Description

This foundational course focuses on data manipulation, cleaning, visualization, and processing using Python and a related suite of data science libraries. Students will learn how to apply Data Science tools to underpin hidden data patterns and trends and improve decision- making, introducing fundamental tools, and improving students’ fluency in data science. It is a hands-on, programming-based course.
Credits: 3

Description

This foundational course introduces the basic notions of Probability theory, including common probability distributions, random processes, Markov chains. It focuses on building students’ statistical reasoning skills through a rigorous study of confidence intervals, hypothesis testing,regression analysis, and elaborates the difference between Frequentist and Bayesian statistics.
Credits: 3

Description

This foundational course teaches the most important Machine Learning (ML) algorithms and their applications. Students will learn the theoretical foundations of machine learning, and practice the implementation and application of machine learning techniques to real world problems. Topics include: (i) supervised learning (e.g. logistic regression, random forests, support vector machines, ensemble methods, and neural networks), (ii) unsupervised learning (e.g., clustering, dimensionality reduction, recommender systems), and (iii) applications and case studies (e.g., apply ML to the development of smart robots perception, text understanding (web search, anti-spam filter), computer vision, speech processing, health informatics, etc.).
Credits: 3

Description

Introduction to Artificial Intelligence is an immersive course that provides students with a robust foundation in the core principles and methodologies driving modern AI systems. The curriculum spans a diverse array of topics, beginning with problem-solving techniques and search algorithms essential for navigating complex decision spaces. Students will delve into knowledge representation and reasoning, learning to structure and manipulate information to enable intelligent behavior. The course also explores natural language processing and computer vision, examining methods that empower machines to interpret and generate human language, as well as process and analyze visual information, all while considering the broader ethical implications of AI technologies. Through a blend of theoretical lectures and practical assignments, participants will gain hands-on experience in designing and implementing AI solutions, equipping them to develop intelligent systems capable of tasks such as game playing, language translation, and pattern recognition. By the end of the course, students will not only have a comprehensive understanding of various AI approaches but also the ability to critically evaluate their strengths and limitations.
Credits: 3

Description

In this workshop-based course, students are introduced to and practice the skills of argumentation, research, and clarity of expression. In the first half of the class, students learn the craft of artistic writing, from the ground up — constructing one analytic piece in increments. The second part of the semester is devoted to putting these skills into professional practice, as students write artist statements, reviews of current work, personal essays, creative pieces, and more. Students read top critics in their fields as well as writers from the canon particularly relevant to their own work.
Credits: 3

Students will have two options for their Master's thesis:
1- A research project conducted exclusively at NYUAD working with the student’s mentor,
2- An applied research project conducted in collaboration with a local partner.

While the first option is more research-oriented, the second option will allow students to do their applied research project through a part-time internship with a local partner under the guidance of their academic mentor, with the aim of applying the conducted research to a specific local context. In both cases, students will learn how to produce a research paper in Data Science and how to apply data science. Students who pursue applied projects will have the opportunity to use NYUAD’s resources to become well-versed in combining theoretical approaches and solving real-world problems. Master's Thesis 1 serves as the initial phase of the project, allowing students to delve into the foundational aspects of their comprehensive data science project. During this phase, students will focus on project proposal development, planning, conducting a literature review, data acquisition, and preliminary analysis. Master's Thesis 2 represents the continuation of the project and provides students with the opportunity to further advance their research or applied project by producing novel insights. In this phase, students will share their thesis research progress, engage in discussions to address ongoing challenges, and maintain a consistent momentum towards producing their final report.
Credits: 0

This zero-credit course, taken every semester, introduces students to some of the active areas of research in Data Science. Invited speakers from both academia and industry will give 90-minute presentations every week during the academic year, and will meet with students in small groups as well. Moreover, by hosting leading keynote speakers, this seminar will expose students to managerial, entrepreneurial and leadership skills.

Electives

Students are required to take at least five elective courses, allowing them to explore specific areas of interest in consultation with their mentor and the Graduate Program Head. These electives draw upon the interdisciplinary nature of the program and the expertise of faculty across various divisions. These courses include but are not limited to the following:

Credits: 3

Topics include complete metric spaces, the contraction mapping theorem, Principal Component Analysis, Low rank approximation and Compression, Elements of abstract Integration, Least Squares problems and their regularizations, inner product spaces, Reproducing Kernel Methods.
Credits: 3

Description

This foundational course teaches about the algorithms, tools, and methods needed to process and analyze large amounts of data beyond the capabilities of typical research hardware. This course will cover real-world applications that require massive data analysis and how they can be implemented on Big Data platforms. The course will consist of lectures based on both textbook material and scientific papers, as well as programming assignments to provide students with hands-on experience on building data-intensive applications using existing Big Data platforms.
Credits: 3

This course is designed to provide detailed understanding of the physics, instrumentation, and signal processing methods used in X-ray (projection radiography), X-ray computed tomography, nuclear medicine (SPECT/PET), optical imaging, ultrasound imaging, and magnetic resonance imaging (MRI).
Credits: 3

Topics include the potential outcomes framework, natural experiments, and the growing field of applying data science tools to causal learning.
Credits: 3

This course covers topics such as migration and mobility, housing, traffic networks, remote sensing and imaging, policing, and more. This seminar-based course also covers applications from diverse fields, such as sociology, psychology, data science, and machine learning. It covers the use of computational techniques to model and predict social phenomena using real data.
Credits: 3

Topics include image classification, image segmentation, object localization, and object detection.
Credits: 3

This course introduces the algorithmic techniques necessary for mining massive amounts of data. This includes randomized approximation algorithms for streaming data, randomized numerical linear algebra and its applications to dimension reduction and clustering, algorithms for graph analysis, and large scale machine learning.
Credits: 3

This course is an introduction to the use of innovative large datasets collected from the web, or from exhaustive administrative datasets, from surveys and from field experiments and lab experiments, starting from concrete questions and actual research experience of social scientists.
Credits: 3

This course introduces the fundamentals of neural networks, with applications to text data, image data, audio data, and other structured data.
Credits: 3

This course will introduce students to data science in environmental science, including the nature of environmental data and the use of relevant computational tools and software packages in Python.
Credits: 3

This course presents an overview of the management and utilization of health-related data to support decision-making. This includes (i) clinical data, such as data collected within electronic health records and clinical decision support systems, (ii) public health data, and (iii) bioinformatics data such as proteomics. The course also introduces state-of-the-art artificial intelligence and machine learning advances in the field of health informatics.
Credits: 3

Topics include data representations of text, recurrent neural networks, and transformers; this course will be taught in the HuggingFace Python ecosystem and will have some focus on Arabic NLP.
Credits: 3

Topics include convex and non-convex optimization, duality, stochastic gradients, and derivative-free optimization.
Credits: 3

This course will discuss topics such as basic concentration inequalities, random vectors in high dimension, matrix concentration inequalities, community detection, covariance estimation and clustering, matrix completion, spiked models and outliers detection, information plus noise models, random graphs, together with their applications to machine learning and data science.
Credits: 3

This course explores the intersection of robotics, artificial intelligence, and human interaction. It aims to equip students with the knowledge and skills to design, develop, and analyze robots that can interact socially with humans in various contexts.
Credits: 0

This course introduces students to the fundamentals of data and physics-driven urban traffic operations management by first developing models of traffic physics at the microscopic, mesoscopic, and macroscopic levels along with the scaling laws that connect them. The models inform various tools operating at the interface of traffic physics and data-driven optimization.
Credits: 0

This course will introduce students to (i) advanced state-of-the-art computational methods relevant to healthcare problems, (ii) the nature of diverse heterogeneous clinical data, such as electronic health records and medical images, (iii) the challenges of translating such solutions into clinical settings, including model failures, regulation, etc., as well as (iv) the use of relevant tools such as Python, ML libraries, Github, latex, and neptune.ai.

Sample Course Structure

Term	Year 1	Credits	Year 2	Credits
Summer Term	Data Science for Everyone Bootcamp (as needed)	0	Master’s thesis research and/or internship	0
Fall Semester	IDSC-GH 5010 Introduction to Data Science	3	Elective 2	3
	IDSC-GH 5020 Probability and Statistics	3	Elective 3	3
	IDSC-GH 5030 Ethics and Legal Considerations for Data Science	3	IDSC-GH 6010 Master’s Thesis 1	3
	IDSC-GH 5900 Data Science Seminar	0	IDSC-GH 5900 Data Science Seminar	0
J-Term	IDSC-GH 5910 Data Science Winter School	0	IDSC-GH 5920 Data Science Winter School	0
Spring Semester	IDSC-GH 5040 Machine Learning	3	Elective 4	3
	IDSC-GH 5050 Intro to AI	3	Elective 5	3
	Elective 1	3	IDSC-GH 6020 Master’s Thesis 2	3
	IDSC-GH 5900 Data Science Seminar	0	IDSC-GH 5900 Data Science Seminar	0
	Year 1 Credits	18	Year 2 Credits	18
			Total Credits	36

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