MS
Data Science
Department
Computer Science
Level
Graduate
College
College of Computer Sciences & Information Systems
Course Detail
The MS Data Science (MS DS) program is a gateway to a world of opportunities. It prepares students to convert data into actionable insights, enabling them to make complex business decisions. Students will learn to handle large, complex data sets using computational, statistical, and machine learning techniques. The program offers exposure to the latest trends and technologies in data science, producing skilled professionals to meet the growing demand for data science products in national and international markets. The demand for data scientists is not just increasing; it’s rising steeply, and there is a significant need for data science specialists across the public and private sectors worldwide.
The program is designed in accordance with Higher Education Commission (HEC) guidelines. It spans a minimum of 2 years, comprising at least 4 semesters, and requires the successful completion of 30 credit hours. To meet the degree requirements, students must complete eight taught courses along with the 6 credit hours of research thesis (for MS by Thesis) or 6 credit hours of Independent Research Studies (IRS – I and IRS – II) (for MS by Independent Research Study) or 6 credit hours of 2 courses (Elective IV and Elective V where each course is of 3 credit hours) (for MS by Coursework). The MS by Coursework is subject to the departmental committee’s approval.
MS Data Science students learn to:
- Students will be able to translate data analyses into actionable business insights.
- Students will collaborate effectively in diverse teams.
- Students will design, implement, and evaluate machine learning models to solve complex data problems.
- Students will create clear and impactful data visualizations and reports to present findings effectively.
- Prepare students to effectively operate and communicate as leaders or team members while understanding professional ethics and social responsibility.
- Prepare students to embrace technological advancements in data science research and development and actively engage in lifelong learning.
Learning Outcomes for MS Data Science students include:
- The capacity to use knowledge to solve data science research and practical challenges efficiently.
- The capacity to evaluate intricate issues and find and create creative, data-driven solutions.
- The capacity to obtain the necessary information and abilities to preprocess and assess data efficiently.
- The capacity to regulate data science initiatives while being thoroughly aware of social concerns, professional ethics, and obligations.
- The capacity to interact with a variety of audiences effectively.
Career Path:
1. Machine Learning Engineer | 2. Data Scientist | 3. Data Analyst/Engineer |
4. Business Intelligence Analyst | 5. Big Data Engineer | 6. Computer Vision Engineer |
7. Data Product Manager | 8. Data Mining Engineer | 9. Healthcare Data Scientist |
10. Data Architect | 11. Operations Research Analyst | 12. Data Science Consultant |
Prospective Employers:
1. Technology Companies | 2. Research Institutions | 3. Automotive Industry |
4. Healthcare Industry | 5. Financial Services | 6. E-commerce and Retail |
7. Manufacturing and Logistics | 8. Educational Institutions | 9. Aerospace and Defense |
Eligibility
To be eligible for the MS in Data Science (MSDS) program, candidate must have a 4-year Bachelor’s degree (16 years of education) in a relevant computing discipline (such as BS Data Science, BS Computer Science, BS IT, etc.) from an HEC-recognised university, with a minimum 2.5 CGPA on a 4.0 scale or a 2nd Division.
Semester-wise Breakup
Semester 1
Course Title | Credit hours |
Research Methodology | 3 |
Statistics & Probability for Data Science | 3 |
Advanced Data Mining and Machine Learning | 3 |
Understanding of Holy Quran I * | 1 |
Total: | 10* |
Semester 2
Course Title | Credit hours |
Deep Learning and Neural Networks | 3 |
Advanced Big Data Analytics | 3 |
Elective I | 3 |
Understanding of Holy Quran II * | 1 |
Total: | 10* |
Semester 3
Course Title | Credit hours |
Elective II | 3 |
Elective III | 3 |
|
|
Total: | 6 |
Semester 4
Course Title | Credit hours |
MS Thesis /IRS – I and IRS – II / Elective IV* and Elective V */ | 6 |
Total: | 6 |
* Muslim students are required to take Understanding of the Holy Quran I (1 credit hour) and Understanding of the Holy Quran II (1 credit hour) to complete the degree requirement. Both courses are graded on a Pass/Fail basis and have no prerequisites.
** For MS by Coursework, students are required to take 2 additional courses (Elective IV and Elective V) each of 3 credit hours (subject to the approval of the departmental committee).
Core Courses:
Courses | Credit Hours |
Research Methodology | 3+0 |
Advanced Data Mining and Machine Learning | 3+0 |
Statistics & Probability for Data Science | 3+0 |
Deep Learning and Neural Networks | 3+0 |
Advanced Big Data Analytics | 3+0 |
Elective Courses:
Courses | Credit Hours |
Distributed Intelligence System | 3+0 |
Knowledge-Based System | 3+0 |
Advanced Modeling and Simulation | 3+0 |
Image Processing and Computer Vision | 3+0 |
Advanced Database Techniques | 3+0 |
Programming for Data Science (Python/R) | 3+0 |
Generative AI | 3+0 |
Data Ethics and Privacy | 3+0 |
Geospatial Data Analysis | 3+0 |
Design and Analysis of Algorithms | 3+0 |
Speech Processing | 3+0 |
Financial Data Analysis | 3+0 |
Time Series Analysis & Forecasting | 3+0 |
Web Mining | 3+0 |
Deep Reinforcement Learning | 3+0 |
Natural Language Processing | 3+0 |
Business Intelligence | 3+0 |
Web Intelligence and Big Data | 3+0 |
Cloud Computing for Data Science | 3+0 |
Numerical Linear Algebra | 3+0 |
Distributed Computing | 3+0 |
Information Retrieval | 3+0 |
Social Media Analysis | 3+0 |
Advanced Data Analytics and Visualization | 3+0 |
Text Processing | 3+0 |
Advanced Data Warehousing | 3+0 |
Optimization Methods | 3+0 |
Pattern Recognition | 3+0 |
MS Thesis *** | 6+0 |
Independent Research Study-I *** | 3+0 |
Independent Research Study-II *** | 3+0 |
*** Thesis and Independent Research Study – I and Independent Research Study – II are offered in Semester 4.
Similarly, students can take Elective courses (Elective IV and Elective V) from the above list in Semester 4.