The future belongs to those who can transform data into intelligent decisions.
The MS in Data Science & AI program at AGU is designed to prepare professionals, researchers, and innovators capable of solving real world challenges using advanced data analytics and Artificial Intelligence technologies.
Data Science has emerged as a powerful discipline for enabling data-driven decision making across industries. Organizations worldwide are increasingly investing in transforming raw data into actionable knowledge and intelligent insights.
At the same time, Artificial Intelligence has become essential for building intelligent systems capable of solving complex real world problems. Individually, Data Science and AI offer significant value; however, the future belongs to professionals who can integrate both domains to develop impactful, scalable, and intelligent solutions.
• Industry aligned curriculum
• AI + Data Science integrated learning
• Real world projects and research
• Focus on innovation and problem solving
• Hands on development of AI enabled systems
• Opportunities in healthcare, finance, business, smart systems, and emerging technologies
Interdisciplinary and Domain Focused Learning
The MS in Data Science & AI program is specifically designed for students and professionals coming from diverse academic and professional backgrounds. Whether a learner belongs to healthcare, business, engineering, social sciences, finance, education, or technology, the program enables them to leverage their existing domain expertise while harnessing the power of Data Science and Artificial Intelligence.
The combination of domain knowledge with data-centric thinking and AI enabled problem solving empowers students to identify meaningful real world problems, analyze complex datasets, and design intelligent products and solutions relevant to their respective industries. This interdisciplinary approach creates professionals who not only understand technology but also understand the context and impact of the problems they are solving.
The MS in Data Science & AI program is designed to prepare future innovators and problem solvers who can:
• Analyze complex data centric challenges
• Identify meaningful patterns and feature sets
• Build AI enabled intelligent systems
• Develop practical solutions and products for real-world applications
This interdisciplinary program combines strong foundations in:
• Data Analytics
• Machine Learning
• Artificial Intelligence
• Predictive Modeling
• Decision Intelligence
• AI driven Product Development
Students will gain the knowledge and practical expertise required to solve problems from a data centric perspective while leveraging AI technologies for innovation and transformation.
The following are the fundamental requirements to get admission and complete Computing degrees at Al Ghazali University.
Eligibility Criteria, Duration of the Program and Award of Degree:
Applicants applying to the MS “Data Science and AI” Program must have their Bachelor’s (or Master’s) degree in any one of the following areas:
Any Engineering Discipline such as Electrical, Civil, Mechanical, Environmental, Chemical, Aeronautical etc.
Computer Science
Economics and Social Sciences
Accounting, Finance, Marketing, and Business Administration
Basic Sciences such as Physics, Biology, Chemistry etc.
Other related disciplines have exposure to computational problem solving, foundations of linear algebra, and introductory probability theory.
Those with non-computing backgrounds may be required to take prerequisite courses, as determined by the Al Ghazali Admissions Committee depending on the academic background of the applicant. Options for fulfilling these prerequisites will be provided to accepted applicants who need them
| Sr | Course Code | Title | Core | Credit Hours |
|---|---|---|---|---|
| 1 | DT 501 | Digital Transformation Data Analytics & Knowledge Framework | X | 3 |
| 2 | AI 501 | Foundation of AI | X | 3 |
| 3 | DS 501 | Tool & Practice | X | 3 |
| Sr | Course Code | Title | Core | Credit Hours |
|---|---|---|---|---|
| 1 | DT 501 | Digital Transformation Data Analytics & Knowledge Framework | X | 3 |
| 2 | AI 501 | Foundation of AI | X | 3 |
| 3 | DS 501 | Tool & Practice | X | 3 |
| Sr | Course Code | Title | Core | Credit Hours |
|---|---|---|---|---|
| 1 | BA 501 | Business Analytics | X | 3 |
| 2 | AI 502 | Computer VIsion / Generative AI | X | 3 |
| 3 | SN 601 | Social Network Analysis | X | 3 |
| 4 | RS 601 | Thesis I / Practicum I | X | - |
| Sr | Course Code | Title | Core | Credit Hours |
|---|---|---|---|---|
| 1 | EL 501 | Elective Course | - | 3 |
| 2 | RS 602 | Thesis II / Practicum II | - | 3 |
Sr # | Requirement | Course Code | Course Name | Credit Hours |
|---|---|---|---|---|
1 | Mandatory | AI xxx | Foundations of AI | 3 |
2 | Mandatory | AI xxx | Machine Learning | 3 |
3 | Mandatory | AI xxx | Digital Transformation, Data Analytics and Knowledge Framework | 3 |
Sr # | Requirement | Course Code | Course Name | Credit Hours |
|---|---|---|---|---|
AI xxx | Applied Probability | |||
AI xxx | Advanced Linear Algebra | |||
4 | Student may take 1 course from this list | AI xxx | Convex Optimization | 3 |
AI xxx | Information Theory and Machine Learning | |||
AI xxx | Introduction to Data Science |
Sr # | Requirement | Course Code | Course Name | Credit Hours |
|---|---|---|---|---|
Big Data Analytics | ||||
Deep Learning | ||||
Computer Vision | ||||
Digital Image Processing | ||||
Dynamic Programming and Reinforcement Learning | ||||
Intelligent Computing | ||||
Data Mining | ||||
Design and Analysis of Algorithms | ||||
5 | Student may take any 4 courses from this list | Data Analytics for Business | 12 | |
Data, Systems, and Sustainability | ||||
Data to Knowledge Visualization | ||||
Social Network Analysis | ||||
Biological Networks | ||||
Computational Genomics and AI | ||||
System Biology | ||||
Introduction to Game Theory | ||||
Business Analytics | ||||
Human centered AI Assisted Systems |
Stream Electives | Alternate Stream Electives | |
|---|---|---|
Big Data Analytics | AI Strategy Development | |
Deep Learning | Ethical and Responsible AI for Policy Making | |
Computer Vision | Agentic Systems | |
Digital Image Processing | Data to Knowledge Transformation | |
Applied Probability | Dynamic Programming and Reinforcement Learning | Data and AI Policy Making |
Advanced Linear Algebra | Design and Analysis of Algorithms | Large Language Model Systems |
Convex Optimization | Data Mining | Cloud Development for AI Systems |
Information Theory and Machine Learning | Intelligent Computing | MLOps and Scalable AI Solutions |
Introduction to Data Science | Multi Agent Systems | Advanced Computational Data Science |
Data Analytics for Business | TinyML and AI for Edge Devices | |
Data, Systems, and Sustainability | Explainable AI | |
Data to Knowledge Visualization | ||
Social Network Analysis | ||
Biological Networks | ||
Computational Genomics and AI | ||
System Biology | ||
Introduction to Game Theory | ||
Business Analytics | ||
Human centered AI Assisted Systems |
The remaining 6 Credit Hours can be completed as:
MS with Thesis Option: 6 Credit Hours Thesis/Practicum I & II
Or 2 additional electives of the student’s choice