Al Ghazali University


MS Data Science & AI
(MS DS & AI)
Program

Introduction

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.

 

Program Summary


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
.

 

Why this Program?

 

•  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.

Eligibility Criteria

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

Semester I

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

Semester II

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

Semester III

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 -

Semester IV

Sr Course Code Title Core Credit Hours
1 EL 501 Elective Course - 3
2 RS 602 Thesis II / Practicum II - 3

Total Credit Hours: 30 30 30

 

Core Foundations of DS/AI/ML

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

Mathematical & Statistical Foundation of AI/ML

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
 

Breadth in AI/ML

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
 

MS DSAI Electives:

 
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:
 

  1. MS with Thesis Option: 6 Credit Hours Thesis/Practicum I & II

  2. Or 2 additional electives of the student’s choice