Ph.D Data Science
Build the skills to lead in Ph.D Data Science
The Ph.D Data Science program at Superior University is tailored for individuals seeking to augment their knowledge and research skills in the rapidly evolving field of data science. The program offers a comprehensive and rigorous curriculum that delves into advanced topics such as machine learning. data mining, and statistical modeling, and introduces students to pioneering research methodologies and techniques.
Eligibility Criteria
18-years of education with 3 CGPA in science/
engineering/mathemat cs discipline preferably with 2 years degree program of MS (SE/CS/IT/EE/CE/DS) or equivalent from HEC recognized university or degree awarding institute.
• Candidates with relevant master’s degrees and candidates with course work or research experience in data science. Relevant degrees include mathematics, statistics, computer science, engineering, and other scientific disciplines that develop skills in drawing inferences or making predictions using data.
• Two years of relevant work experience is recommended.
• Minimum 80% marks in previous degree in case of annual system or 3.00/4.0 CGPA in case of semester system with no more than one second division throughout the academic career.
• No third division in entire academic career.
• Pass the following:
i. HEC (HAT) / GAT NTS (General) / Superior Graduate Admission Test; (at least 70%)
ii. Admission Interview by Superior Post Graduate Admission Committee.
- Total credit hours:48
- Semester: 6
- Course duration: 3 years
Semester 1
| Courses | Credit Hours |
|---|---|
| Elective-I | 3 |
| Elective-II | 3 |
| Elective-III | 3 |
| Total | 9 |
Semester 2
| Courses | Credit Hours |
|---|---|
| Elective-IV | 3 |
| Elective-V | 3 |
| Elective-VI | 3 |
| Total | 9 |
Semester 3,4,5&6
| Courses |
|---|
| Thesis |
Electives
| Electives |
|---|
| Artificial Intelligence |
| Big Data Analytics |
| Data Science |
| Information Security |
| Data Security & Cryptography |
| Machine Learning |
| Robotics |
| Deep Learning |
| Advanced Computer Vision |
| Distributed Computing |
| Data Visualization |
| Cloud Computing |
| Algorithmic trading |
| Bayesian Data Analysis |
| Bioinformatics |
| Computational Genomics |
| Deep Reinforcement Learning |
| Distributed Data Processing |
| Inference & Representation |
| Natural Language Processing |
| Optimization Methods for Data Science and |
| Machine Learning |
| Probabilistic Graphical Models |
| Scientific Computing in Finance |
| Social network analysis |
| Time series Analysis and Prediction |
| Advanced Web Architecture and Technologies |
| Information System Development |
| Information System Management |
| Information Systems Audit |
| Secure Software Development |
| Principles of Programming Languages |
| Advance Database Systems |
| Data Mining & Datawarehousing |
| Advanced Database Administration & Management |
| Distributed Database Systems |
| Enterprise Resource Planning System |
| Data Science |
| Business Process Management |
| Business Intelligence and Analytics |
| Knowledge Management |
| Distributed Computing |
| Agent Based Computing |
| High Performance Computing |
| Real Time Systems |
| Data Security & Cryptography |
| Computer Graphics |
| Advanced Computer Vision |
| Natural Language Processing |
| Computational Intelligence |
| Introduction to Soft Computing |
| Complex Networks |
| Object Oriented Analysis & Design |
| IT Requirement Engineering |
| Special Topic |
*To accommodate modern trends, Institute may reserve the right to change course requirements, fees, course classifications, course contents, class schedules, venues, faculty and the like, whenever it deems appropriate.
