Master of Science in AI and Data Science
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Overview
Program Details
Program Goals
Career Opportunities
Overview
Master the Future of Intelligence and Innovation
In an era defined by rapid digital transformation, the ability to harness the power of intelligent machines and vast amounts of data has become one of the most valuable skillsets of the 21st century. The Master of Science in Artificial Intelligence and Data Science (MSAIDS) at AUE is not just a postgraduate degree—it’s a launchpad for visionary thinkers, problem-solvers, and change-makers who are ready to lead the next wave of technological evolution.
Artificial Intelligence and Data Science are at the core of today’s most groundbreaking advancements—from predictive healthcare and autonomous vehicles to smart cities, financial forecasting, and intelligent robotics. Our MSAIDS program is designed to prepare students for the profound impact these technologies are having—and will continue to have—on society, industries, and human potential.
This program offers more than technical mastery. It is a transformative academic journey that combines rigorous scientific foundations, hands-on experience, and ethical foresight. Students will engage with complex datasets, develop intelligent systems, master machine learning and deep learning techniques, and explore the legal, societal, and ethical dimensions of AI in a globalized world.
What Makes Our Program Unique?
At AUE, we don’t just teach AI and Data Science—we cultivate innovators who will define its next frontier. Here’s what sets our program apart:
- Cutting-Edge Curriculum: A strategically designed sequence of courses that covers data engineering, machine learning, deep learning, ethics, research methodologies, and specialized topics in computer vision, NLP, decision systems, and intelligent agents.
- Hands-On Thesis Experience: Our two-stage thesis journey enables students to dive into real-world research and contribute original work to the field under expert supervision.
- Future-Ready Specializations: With electives in cloud-based AI, robotics, digital image processing, and cognitive systems, students gain practical skills aligned with industry’s evolving needs.
- Global Perspective, Local Relevance: We prepare students to address global challenges while responding to UAE and regional demands for AI innovation, ethical governance, and data-driven decision-making.
- Expert Faculty & Research Culture: Learn from faculty actively engaged in groundbreaking research and supported by a thriving academic ecosystem that values innovation and collaboration.
Who Should Join This Program?
This program is ideal for professionals and graduates with a background in computing, engineering, mathematics, or related fields who want to become leaders in AI, machine learning, and data science. Whether you aspire to be a data scientist, AI engineer, or applied researcher, this is your launchpad.
Program Details
Program Mission
To develop highly skilled AI and data science professionals capable of designing intelligent systems, analyzing complex datasets, and making ethical, data-driven decisions that transform industries, drive innovation, and serve society.
Program Learning Outcomes
Graduates will be able to:
- Identify, analyze, and define complex AI and Data Science problems, applying advanced principles of computing, mathematics, and domain-specific knowledge to develop effective and innovative solutions.
- Design, develop, and evaluate sophisticated AI and Data Science models, algorithms, and systems that address real-world challenges, considering factors such as scalability, performance, and constraints.
- Apply advanced mathematical, statistical, and computational methods to model data, derive insights, and create state-of-the-art machine learning and AI-based solutions.
- Demonstrate an understanding of ethical, legal, and societal implications of AI and Data Science, making informed decisions that promote fairness, transparency, and responsible use of data.
- Develop leadership skills to lead interdisciplinary teams effectively, communicate technical information and data-driven insights clearly, and collaborate efficiently in AI and Data Science projects.
- Continuously acquire and apply new knowledge in AI and Data Science, adapting to evolving technologies, methodologies, and industry standards with an emphasis on research and innovation.
Program Goals
Program Goals
Goal 1. Equip students with specialized knowledge and skills to solve real-world challenges.
Goal 2. Instill ethical practices and promote responsible AI.
Goal 3. Foster lifelong learning and adaptability to evolving technologies.
Goal 4. Develop leadership and interdisciplinary collaboration skills.
Goal 5. Promote innovation and research for impactful solutions.
Career Opportunities
Career Opportunities
Graduates of the MSAIDS program will be prepared to pursue roles such as:
- Data Scientist
- Machine Learning Engineer
- AI Researcher
- Business Intelligence Specialist
- Deep Learning Architect
- AI Policy and Ethics Advisor
- Cloud AI Developer
- Intelligent Systems Designer
These roles span industries including finance, healthcare, government, manufacturing, cybersecurity, retail, and transportation.
ADMISSION REQUIREMENTS
- A recognized undergraduate degree in Computer Science, Engineering, Mathematics, Statistics, Information Technology, or a closely related field.
- Minimum CGPA 3.00 out of 4.00.
- All applicants who got less than 3.0 CGPA (but not less than 2.5) or its equivalent at their bachelor’s degree will be conditionally admitted to the university for one semester, where they can register no more than 9 CH of remedial courses and should achieve a CGPA of 3.0 in their first semester.
- A minimum of 6.0 on the IELTS or 79 on the TOEFL iBT or TOEFL ITP 550
- Applicants must attend an admissions interview with the Program Representative.
- Applicants are required to write a one-page (500-word) personal admissions statement in English.
ACCREDITATION
PROGRAM STRUCTURE
Course Category
Total Number of Courses
Total Number of Credit Hours
Core Courses
6
18
Elective Courses
7
6
Thesis Course
2
6
Total
15
30 Credit Hours
Core Courses
Elective Courses
Thesis
Core Courses
Core Courses
18 CREDIT HOURS
This course provides essential skills in data wrangling, data engineering, and statistical analysis to prepare students for data science and AI applications. Students will learn techniques for cleaning, transforming, and organizing data from various sources, as well as foundational methods for exploratory data analysis and statistical inference. The course emphasizes practical skills in Python (or R) and introduces tools and methods used in modern data engineering, preparing students to work effectively with large datasets.
This course provides a comprehensive exploration of Big Data and Data Mining concepts, techniques, and applications. Students will learn the essential elements of Big Data infrastructure, including distributed computing frameworks, NoSQL databases, and cloud storage systems. Emphasis will be placed on processing and managing large datasets using tools such as Hadoop and Apache frameworks. Students will explore key techniques in data mining such as association rule mining, clustering, and classification to extract meaningful patterns from Big Data. Additionally, students will engage in advanced topics such as data warehousing, stream mining, and visualization using tools like Tableau. The course will culminate in understanding the latest trends and innovations in Big Data and Data Mining. Through hands-on projects, practical exercises, and collaborative assignments, students will gain the skills necessary to apply these concepts to real-world scenarios, ensuring effective team-based problem-solving and communication.
This course explores the foundational theories and ethical principles guiding the governance of Artificial Intelligence (AI) systems. Students will examine key issues such as privacy, fairness, accountability, and transparency within the context of AI. The course covers regulatory frameworks, legal elements, and the evolving role of governance in the public and private sectors. Emphasizing a multidisciplinary approach, students will critically analyze case studies, explore global regulations, and engage with practical challenges in responsible AI development. The course culminates in collaborative projects where students work in teams to design ethically sound and transparent AI systems, addressing real-world governance challenges.
This course provides a comprehensive foundation in research design and methodology, covering qualitative, quantitative, and mixed methods approaches. Students will learn to conduct systematic literature reviews, apply theoretical frameworks, formulate research questions, and develop purpose statements to guide meaningful inquiries. Emphasis is placed on effective writing strategies, ethical considerations, data collection, and analysis procedures. Through a blend of theoretical and practical activities, students will develop the skills needed to design, conduct, and present rigorous research projects. This course prepares students to undertake high-quality research applicable across a variety of fields, equipping them with essential skills for both academic and professional research environments.
This course provides a comprehensive introduction to machine learning, covering both fundamental principles and practical applications. Students will explore key supervised and unsupervised learning techniques, including classification, clustering, and dimensionality reduction, along with advanced topics in neural networks and deep learning. Emphasis is placed on model evaluation, feature extraction, and algorithmic implementation. Through hands-on projects and collaborative work, students will apply machine learning and deep learning models to real-world datasets, preparing them for further specialization in AI and data-driven fields.
This course covers essential and advanced topics in deep learning technologies and algorithms, including foundational architectures, training techniques, and practical applications. Students will explore Convolutional and Recurrent Neural Networks, Autoencoders, and Generative Adversarial Networks (GANs), with an introduction to reinforcement learning and Transformers. Through hands-on projects, students will gain experience designing, implementing, and evaluating deep learning models using tools like TensorFlow or PyTorch, with a focus on collaborative problem-solving and real-world applications.
Elective Courses
Elective Courses
6 CREDIT HOURS
This course offers an in-depth exploration of digital image processing, focusing on the fundamental theories and advanced techniques essential for analyzing, enhancing, and interpreting images. Students will gain expertise in image formation, sampling, and quantization, along with advanced methods for spatial and frequency-domain filtering, noise reduction, and color image processing. The course covers essential topics such as wavelet transformations, image compression, and morphological operations, enabling students to perform effective feature extraction, segmentation, and shape analysis. Practical applications and projects allow students to apply these concepts to real-world image processing challenges, equipping them with the skills to develop innovative solutions for fields like medical imaging, remote sensing, and automated inspection.
This course offers an in-depth exploration of advanced Natural Language Processing (NLP) with a focus on transformer models. Students will learn the architecture, functionality, and applications of transformers in key NLP tasks such as text classification, named entity recognition, summarization, and question answering. The course emphasizes practical skills in fine-tuning and training transformers, understanding attention mechanisms, and leveraging pre-trained models using the Hugging Face library. Additionally, students will work collaboratively to develop projects, explore emerging trends, and tackle challenges such as zero-shot learning and multilingual NLP.
This course provides an in-depth understanding of cloud computing technologies and their applications in AI and Data Science. It covers the architecture, deployment models, and services of cloud platforms, such as AWS, Google Cloud, and Microsoft Azure, that enable scalable data processing, machine learning, and big data analytics. Through a blend of theoretical knowledge and hands-on experience, students will learn how to leverage cloud infrastructures to develop and deploy AI-driven applications and data-intensive systems. Key topics include cloud service models (IaaS, PaaS, SaaS), machine learning in the cloud, cloud storage, data security, and the ethical considerations of using cloud resources for AI. By the end of the course, students will be equipped to design and implement data-driven AI applications in a cloud environment, addressing real-world challenges in scalability, performance, and resource management.
This course provides an in-depth exploration of Business Intelligence (BI), Analytics, and Decision Support Systems (DSS). It covers essential concepts, frameworks, and technologies for strategic and operational decision-making. Students will learn about the architecture and applications of BI systems, data warehousing, descriptive and predictive analytics, and advanced modeling techniques. Emphasis is placed on real-world case studies, hands-on projects, and collaborative exercises. The course also addresses emerging trends such in AI, Big Data, and the Internet of Things (IoT), preparing students to effectively leverage analytics for business success.
This course provides an in-depth exploration of intelligent agents and multi-agent systems, focusing on the principles, design, and application of autonomous agents that interact, communicate, and cooperate in dynamic environments. Students will examine foundational concepts such as agent architecture, reasoning mechanisms, communication protocols, and coordination strategies. Through practical exercises, students will implement various agent-based models, explore game theory and negotiation in multi-agent interactions, and apply agent-based methodologies to real-world scenarios. The course emphasizes collaborative learning, where students work in teams to develop, analyze, and present multi-agent solutions for complex, real-world problems, preparing them for advanced roles in autonomous systems and AI-driven applications.
This course provides an in-depth exploration of AI robotics, covering the essential principles, architectures, and algorithms used in the development of intelligent autonomous systems. Students will study foundational concepts, including levels of autonomy, decision-making, perception, and sensor integration. Emphasis is placed on software architectures, reactive and deliberative behavior systems, and the practical application of algorithms for navigation, path planning, localization, and mapping. Through hands-on projects and collaborative activities, students will develop and implement robotic solutions, gaining experience in team-based design and problem-solving within the context of AI-driven robotics.
This course is designed to provide flexibility within the AI and Data Science curriculum, allowing exploration of emerging and high-demand topics in the field. When offered, the department council will choose a specialized topic based on current industry trends, recent technological advancements, and student interests. Topics may include areas such as Large Language Models (LLMs), Generative AI, Edge AI, Advanced Data Analytics for real-world
applications, or any emerging topic in AI and Data science. The course will enable students to engage with cutting-edge challenges and develop innovative solutions, ensuring their skills align with modern industry needs. Students will gain specialized knowledge in the chosen topic, with an emphasis on hands-on projects and collaborative learning to foster critical thinking, adaptability, and problem-solving skills in AI and Data Science.
Thesis
Thesis
6 CREDIT HOURS
This course serves as the foundational phase of the thesis process in the Master’s program for AI and Data Science. This course guides students through the initial stages of their thesis research, focusing on developing a comprehensive research proposal, conducting an in-depth literature review, and formulating clear research questions and hypotheses. Students will engage with qualitative, quantitative and mixed research methodologies, emphasizing the application of these approaches to complex problems in AI and Data Science. Ethical considerations and data management practices will also be integrated, ensuring that students are prepared to handle diverse and sophisticated datasets responsibly. The course culminates in an oral defense of the thesis proposal, where students will present and justify their research design and methodology.
This course is a continuation of Thesis I and is designed to guide students through the later stages of their thesis journey, focusing on advancing their research from data collection through to the final oral defense. Students will refine their research methodology and
techniques as needed, analyze their findings in depth, and ensure that their thesis reflects a comprehensive understanding of their research problem. This course culminates in a formal public defense of the completed thesis, where students will articulate their research
contributions and respond to questions from faculty and peers.
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