Program Structure & Curriculum
Outline of the Program
The program is spread over 4 semesters, with a 6-credit hour thesis being offered in the second year.
Course Offer Plan
Course types |
Cumulative Credits |
---|---|
Program Core Courses (3) |
9 |
Specialization Core Courses (2) |
6 |
Electives Courses (3) |
9 |
Postgraduate Research Thesis |
6 |
Courses
Course Code |
Course Title |
Credit Hours |
---|---|---|
DS-5100 |
Postgraduate Research Thesis |
(6+0) |
Program Core Courses |
||
DS-5101 |
Statistical and Mathematical Methods For Data Science |
(3+0) |
DS-5102 |
Tools and Techniques in Data Science |
(3+0) |
DS-5103 |
Machine Learning |
(3+0) |
Specialized Core Courses: (Choose any 2) |
||
DS-5104 |
Big Data Analytics |
(3+0) |
DS-5105 |
Deep Learning |
(3+0) |
DS-5106 |
Natural Language Processing |
(3+0) |
DS-5107 |
Distributed Data Processing |
(3+0) |
Elective Courses |
||
Course Code |
Course Title |
Credit Hours |
DS-5108 |
Advanced Computer Vision |
(3+0) |
DS-5109 |
Algorithmic trading |
(3+0) |
DS-5110 |
Bayesian Data Analysis |
(3+0) |
DS-5111 |
Big Data Analytics |
(3+0) |
DS-5112 |
Bioinformatics |
(3+0) |
DS-5113 |
Cloud computing |
(3+0) |
DS-5114 |
Computational Genomics |
(3+0) |
DS-5115 |
Data Visualization |
(3+0) |
DS-5116 |
Deep Learning |
(3+0) |
DS-5117 |
Deep Reinforcement Learning |
(3+0) |
DS-5118 |
Distributed Data Processing and Machine Learning |
(3+0) |
DS-5119 |
Distributed Machine Learning in Apache Spark |
(3+0) |
DS-5120 |
High performance computing |
(3+0) |
DS-5121 |
Inference & Representation |
(3+0) |
DS-5122 |
Natural Language Processing |
(3+0) |
DS-5123 |
Optimization Methods for Data Science and Machine Learning |
(3+0) |
DS-5124 |
Probabilistic Graphical Models |
(3+0) |
DS-5125 |
Scientific Computing in Finance |
(3+0) |
DS-5126 |
Social network analysis |
(3+0) |
DS-5127 |
Time series Analysis and Prediction |
(3+0) |
In addition, students are allowed to take any elective course in other department of University with the permission of Chairman.
Degree Completion Requirements
For completion of MS degree, a student must have:
- Passed courses totaling at least 30 credit hours, including core courses.
- Obtained at least 2.5 on a scale of 4.0